Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Khaddar, Shivam
Doctor of Philosophy - PhD
University of British Columbia
The thesis presents novel econometric structures to model daily activity-travel behaviour of an individual including their activity generation and activity scheduling decisions, and their variation based on work arrangement such as telecommuting. The central concept is to adopt advanced modelling techniques to understand how time use decisions such as activity participation, travel decisions such as travel mode choice and land use decisions such as destination choice are interdependent. In the case of activity generation, individual’s choices of activity engagement time allocation, frequency, and their destinations are modelled. For example, a multiple discrete continuous extreme value model with ordered preferences (MDCEV-OP) is formulated to address individual’s activity engagement and duration decisions at a disaggregated episode-level while accounting for activity frequency. Furthermore, a multinomial logit model is utilized to explore destination choices and a logsum based feedback mechanism is adopted to investigate the influence of destination choices on activity generation decisions. In case of activity scheduling decisions, individual’s choices such as activity start time, mode, companionship, and destination location are modelled. A joint discrete choice modelling framework is adopted to investigate these choices and capture interdependencies among them. Additionally, various advanced modelling techniques were adopted to investigate the choice of telecommuting and their influence on activity generation, vehicle kilometre travelled and other teleactivities. In this context, the MDCEV-OP model is utilized to explore variation in activity time use behaviour between telecommuters and non-telecommuters. Moreover, an endogenous switching regression modelling approach is utilized to investigate the decision to become telecommuters and non-telecommuters, and the associated heterogeneity in their travel outcomes such as vehicle kilometre travelled. Lastly, a multivariate ordered probit model is utilized to explore preferences towards teleactivities in the post-pandemic period and capture interrelationship between telecommuting, teleshopping and online food ordering. The model results provide granular level insights on the complexity of individual’s activity-travel decisions and confirm the need to address interdependencies among time use, travel, land use and teleactivities for better behavioural representation. The findings can be used for the development of an operational activity-based model, assisting in improved forecasting of travel demand, and effective testing of transportation policies.
MODELLING ACTIVITY GENERATION AND SCHEDULING DECISIONS, AND EXPLORING THE INTERPLAY WITH TELECOMMUTING by Shivam Khaddar M.Eng., University of Ottawa, 2020 B.Tech., Christ University, 2018 A DISSERTATION SUBITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2024 © Shivam Khaddar, 2024ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a dissertation entitled: MODELLING ACTIVITY GENERATION AND SCHEDULING DECISIONS, AND EXPLORING THE INTERPLAY WITH TELECOMMUTING submitted by SHIVAM KHADDAR in partial fulfillment of the requirements of the degree of DOCTOR OF PHILOSOPHY. Dr. Mahmudur Fatmi, School of Engineering Supervisor Dr. Kasun Hewage, School of Engineering Supervisory Committee Member Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Babak Mohamadpour Tosarkani, School of Engineering University Examiner Dr. Mathew Roorda, University of Toronto External Examiner iii ABSTRACT The thesis presents novel econometric structures to model daily activity-travel behaviour of an individual including their activity generation and activity scheduling decisions, and their variation based on work arrangement such as telecommuting. The central concept is to adopt advanced modelling techniques to understand how time use decisions such as activity participation, travel decisions such as travel mode choice and land use decisions such as destination choice are interdependent. In the case of activity generation, individual’s choices of activity engagement time allocation, frequency, and their destinations are modelled. For example, a multiple discrete continuous extreme value model with ordered preferences (MDCEV-OP) is formulated to address individual’s activity engagement and duration decisions at a disaggregated episode-level while accounting for activity frequency. Furthermore, a multinomial logit model is utilized to explore destination choices and a logsum based feedback mechanism is adopted to investigate the influence of destination choices on activity generation decisions. In case of activity scheduling decisions, individual’s choices such as activity start time, mode, companionship, and destination location are modelled. A joint discrete choice modelling framework is adopted to investigate these choices and capture interdependencies among them. Additionally, various advanced modelling techniques were adopted to investigate the choice of telecommuting and their influence on activity generation, vehicle kilometre travelled and other teleactivities. In this context, the MDCEV-OP model is utilized to explore variation in activity time use behaviour between telecommuters and non-telecommuters. Moreover, an endogenous switching regression modelling approach is utilized to investigate the decision to become telecommuters and non-telecommuters, and the associated heterogeneity in their travel outcomes such as vehicle kilometre travelled. Lastly, a multivariate ordered probit model is utilized to explore preferences towards teleactivities in the post-pandemic iv period and capture interrelationship between telecommuting, teleshopping and online food ordering. The model results provide granular level insights on the complexity of individual’s activity-travel decisions and confirm the need to address interdependencies among time use, travel, land use and teleactivities for better behavioural representation. The findings can be used for the development of an operational activity-based model, assisting in improved forecasting of travel demand, and effective testing of transportation policies. v LAY SUMMARY Activity based models are used to forecast travel demand and test the impacts of alternative transportation policies and travel demand management strategies such as telecommuting. The key to unbiased policy testing is the accurate prediction of travel demand, which is largely dependent on the representation of individuals’ travel behaviour in the model. Activity-travel decisions are complex in nature as they interact with many other decisions such as the location and time of travel. Furthermore, the advent of teleactivities; particularly, telecommuting has changed travel-related decisions. This thesis develops advanced econometric models to represent the complexity associated with individuals’ daily activity-travel decisions. The innovative modelling approaches proposed in the study provide important insights on individual’s activity-travel behaviour including confirming the interdependencies among activity-travel decisions, and their variation based on the telecommuting opportunity. The findings can be utilized for accurate travel behaviour representation, travel demand forecasting, and transportation policy testing. vi PREFACE Portions of this thesis have been published as peer-reviewed journal papers, and in fully peer-reviewed conference proceedings. This thesis is largely derived from the following papers: Chapter 4 is derived from: Khaddar, S., Saxena, S., Fatmi, M. R., & Pinjari, A.R. (2022) An Episode-Level Joint Model for Activity Engagement, Duration, and Destination Location Choice Decisions, Transportation Letters, 15:8, 860 870, DOI: 10.1080/19427867.2022.2102347 Chapter 5 is derived from: Khaddar, S., Varghese, V., Fatmi, M. R., & Chikaraishi, M. (2023) A Joint Model for Activity Start Time, Travel Mode, Companionship, and Destination Location Decisions. Transportmetrica A: Transport Science (Under Review 3rd Stage) Chapter 6 is derived from: Khaddar, S., Fatmi, M. R., & Nikodym, T. (2023). Are Telecommuters’ and Non-Telecommuters’ Daily Time-Use Behaviors Different? An Episode-Level Model for Non-Mandatory Activities. Transportation Research Record, 2677 (11), 96 110. https://doi.org/10.1177/03611981231164390 Chapter 7 is derived from: Khaddar, S., Varghese, V., Fatmi, M. R., & Chikaraishi, M. (2024) Jointly Modeling Work Arrangements and Vehicle Kilometer Traveled (VKT) for Non-work Travel: An Endogenous Switching Regression Modeling Approach. Published in the fully peer-reviewed proceeding of the 103rd Annual Meeting of Transportation Research Board (TRB), Washington D.C., USA. Chapter 8 is derived from: Khaddar, S., & Fatmi, M. R. (2024). Modeling Telecommuting and Teleshopping Preferences in the Post-Pandemic Era. Transportation Research Record. https://doi.org/10.1177/03611981231222238 vii TABLE OF CONTENTS ABSTRACT................................................................................................................................................... III LAY SUMMARY ...........................................................................................................................................V PREFACE ..................................................................................................................................................... VI LIST OF TABLES ......................................................................................................................................... XI LIST OF FIGURES ..................................................................................................................................... XIII LIST OF ABBREVIATIONS ....................................................................................................................... XIV ACKNOWLEDGEMENTS.......................................................................................................................... XVI DEDICATION ........................................................................................................................................... XVIII CHAPTER 1: INTRODUCTION .................................................................................................................... 1 1.1. BACKGROUND AND MOTIVATION ................................................................................................................. 1 1.2. RESEARCH GAPS ........................................................................................................................................... 5 1.3. RESEARCH OBJECTIVES ................................................................................................................................. 8 1.4. THESIS OUTLINE............................................................................................................................................ 9 CHAPTER 2: LITERATURE REVIEW ....................................................................................................... 12 2.1. ACTIVITY-BASED TRAVEL DEMAND MODELS ............................................................................................ 12 2.2. BEHAVIOURAL MODELLING OF ACTIVITY-TRAVEL DECISIONS: INDEPENDENT MODELLING ..................... 14 2.3. BEHAVIOURAL MODELLING OF ACTIVITY-TRAVEL DECISIONS: JOINT MODELLING .................................. 16 2.3.1. Modelling of Activity Generation Decisions...................................................................................... 17 2.3.2. Modelling of Activity Scheduling Decisions ...................................................................................... 20 2.3.3. Modelling of Teleactivities and Travel Outcomes ............................................................................. 23 2.3.3.1. Modelling Behavioural Differences Between Telecommuters and Non-telecommuters ............................... 24 2.3.3.2. Modelling Decision to Telecommute or Non-telecommute and Associated Travel Outcomes ..................... 27 2.3.3.3. Modelling Preferences Towards Telecommuting and Teleshopping ............................................................. 30 2.4. CONCLUDING REMARKS .............................................................................................................................. 33 CHAPTER 3: DATA .................................................................................................................................... 35 3.1. STUDY AREA ............................................................................................................................................... 35 3.2. PRIMARY DATA ........................................................................................................................................... 36 3.2.1. Okanagan Travel Survey ................................................................................................................... 36 3.2.2. Pre and Post COVID-19 Travel Survey............................................................................................. 39 3.3. SECONDARY DATA ...................................................................................................................................... 42 viii 3.4. INDEPENDENT VARIABLES GENERATED ...................................................................................................... 42 CHAPTER 4: MODELLING ACTIVITY GENERATION DECISIONS ...................................................... 45 4.1. INTRODUCTION ............................................................................................................................................ 45 4.2. METHODOLOGY........................................................................................................................................... 46 4.3. DESCRIPTIVE STATISTICS ............................................................................................................................ 50 4.4. INDEPENDENT VARIABLES GENERATED ...................................................................................................... 51 4.5. MODEL RESULTS ......................................................................................................................................... 53 4.5.1. Goodness-of-fit Measures .................................................................................................................. 54 4.5.2. Destination Choice Results ................................................................................................................ 54 4.5.3. Activity Engagement and Duration Choice Results: Baseline Preference Estimates ....................... 56 4.5.4. Activity Engagement and Duration Choice Results: Logsum Parameter Estimates ......................... 60 4.5.5. Activity Engagement and Duration Choice Results: Satiation parameters Estimates ...................... 60 4.6. CONCLUDING REMARKS .............................................................................................................................. 65 CHAPTER 5: MODELLING ACTIVITY SCHEDULING DECISIONS ...................................................... 67 5.1. INTRODUCTION ............................................................................................................................................ 67 5.2. METHODOLOGY........................................................................................................................................... 68 5.3. DESCRIPTIVE STATISTICS ............................................................................................................................ 72 5.4. INDEPENDENT VARIABLES TESTED ............................................................................................................. 74 5.5. MODEL RESULTS ......................................................................................................................................... 78 5.5.1. Goodness-of-fit Measures .................................................................................................................. 78 5.5.2. Parameter Estimation Results: Activity Start Time ........................................................................... 79 5.5.3. Parameter Estimation Results: Mode-companionship ...................................................................... 80 5.5.4. Parameter Estimation Results: Destination Location ....................................................................... 82 5.5.5. Effects of Common Unobserved Terms.............................................................................................. 85 5.5.6. Contribution of Common Unobserved Terms .................................................................................... 87 5.6. CONCLUDING REMARKS .............................................................................................................................. 92 CHAPTER 6: MODELLING ACTIVITY GENERATION DECISIONS OF TELECOMMUTERS AND NON-TELECOMMUTERS .......................................................................................................................... 95 6.1. INTRODUCTION ............................................................................................................................................ 95 6.2. METHODOLOGY........................................................................................................................................... 96 6.3. DESCRIPTIVE STATISTICS ............................................................................................................................ 99 6.4. INDEPENDENT VARIABLES TESTED ........................................................................................................... 101 6.5. MODEL RESULTS ....................................................................................................................................... 103 ix 6.5.1. Goodness-of-fit Measures ................................................................................................................ 103 6.5.2. Non-telecommuters: Baseline Preference and Satiation Parameter Estimates .............................. 105 6.5.3. Telecommuters: Baseline Preference and Satiation Parameter Estimates ..................................... 107 6.5.4. Forecasting Analysis ....................................................................................................................... 112 6.6. CONCLUDING REMARKS ............................................................................................................................ 115 CHAPTER 7: MODELLING DECISION TO TELECOMMUTE OR NON-TELECOMMUTE AND ASSOCIATED TRAVEL OUTCOMES...................................................................................................... 118 7.1. INTRODUCTION .......................................................................................................................................... 118 7.2. METHODOLOGY......................................................................................................................................... 119 7.2.1. Endogenous Switching Regression Model ....................................................................................... 119 7.2.2. Expected Values ............................................................................................................................... 121 7.2.3. Treatment Effects ............................................................................................................................. 122 7.3. DESCRIPTIVE ANALYSIS ............................................................................................................................ 123 7.4. INDEPENDENT VARIABLES TESTED ........................................................................................................... 124 7.5. MODEL RESULTS ....................................................................................................................................... 126 7.5.1. Parameter Estimation Results: Selection Model ............................................................................. 126 7.5.2. Parameter Estimation Results: Outcome Model ............................................................................. 128 7.5.3. Parameter Estimation Results: Error Correlation .......................................................................... 129 7.5.4. Marginal Effects .............................................................................................................................. 131 7.5.5. Treatment Effects ............................................................................................................................. 132 7.6. CONCLUDING REMARKS ............................................................................................................................ 133 CHAPTER 8: MODELLING PREFERENCES TOWARDS TELECOMMUTING AND TELESHOPPING IN THE POST-PANDEMIC ERA............................................................................................................... 136 8.1. INTRODUCTION .......................................................................................................................................... 136 8.2. METHODOLOGY......................................................................................................................................... 137 8.3. DESCRIPTIVE ANALYSIS ............................................................................................................................ 139 8.4. INDEPENDENT VARIABLES TESTED ........................................................................................................... 141 8.5. MODEL RESULTS ....................................................................................................................................... 143 8.5.1. Goodness-of-fit Measures ................................................................................................................ 144 8.5.2. Parameter Estimation Results ......................................................................................................... 144 8.5.3. Marginal Effects .............................................................................................................................. 152 8.6. CONCLUDING REMARKS ............................................................................................................................ 155 CHAPTER 9: CONCLUSION .................................................................................................................... 157 x 9.1. CONTRIBUTIONS ........................................................................................................................................ 157 9.2. LIMITATIONS AND FUTURE WORKS........................................................................................................... 159 REFERENCES ........................................................................................................................................... 164 xi LIST OF TABLES Table 3.1. Comparison of OTS with 2016 Canadian Census ....................................................... 38 Table 3.2. Comparison of survey data with Census data .............................................................. 41 Table 4.1. Descriptive statistics of the OTS Sample by activity engagement and time consumption .................................................................................................................................. 51 Table 4.2. Descriptive statistics of the variables retained in the final model ............................... 52 Table 4.3. Parameter estimation results: Destination Choice ....................................................... 55 Table 4.4. Parameter estimation results: Baseline preference parameters, Logsum parameters, Satiation parameters ...................................................................................................................... 61 Table 5.1. Descriptive statistics of the variables retained in the final model ............................... 75 Table 5.2. Goodness-of-fit measures of the joint and independent models .................................. 78 Table 5.3. Parameter estimation results of the activity start time, mode-companionship, and destination location choice components ....................................................................................... 83 Table 5.4. Correlation matrix for joint model ............................................................................... 87 Table 5.5. Contribution of different variables towards total variance of utility differences......... 91 Table 6.1. Summary of activity participation and time allocation of the OTS Sample .............. 100 Table 6.2. Descriptive statistics of the variables retained in the final model ............................. 102 Table 6.3. Goodness-of-fit Measures .......................................................................................... 104 Table 6.4. Parameter estimation results of the MDCEV-OP model for non-mandatory activity participation and duration of telecommuters and non-telecommuters ........................................ 109 Table 6.5. Fit measures for the predictions ................................................................................. 113 Table 6.6. Observed and predicted average time allocations (participation rates) from the proposed MDCEV-OP model ..................................................................................................... 114 xii Figure 7.1 Example of Travel Patterns ....................................................................................... 119 Table 7.1. Summary of Dependent Variables ............................................................................. 124 Table 7.2. Descriptive statistics of the variables retained in the final model ............................. 125 Table 7.3. Parameter Estimation Results .................................................................................... 130 Table 7.4. Marginal Effects ........................................................................................................ 132 Table 8.1 Descriptive Statistics of the Explanatory Variables ................................................... 142 Table 8.2. Parameter Estimation Results .................................................................................... 150 Table 8.3. Marginal effects of the variables retained in the MVOP model ................................ 153 xiii LIST OF FIGURES Figure 1.1 Components of Trip Based Mode.................................................................................. 2 Figure 1.2 Components of Typical ABM ....................................................................................... 4 Figure 1.2 Thesis Outline .............................................................................................................. 11 Figure 2.1 Disaggregated vs Aggregated approach ...................................................................... 18 Figure 3.1 Study Area ................................................................................................................... 36 Figure 3.2 Okanagan Travel Survey (OTS) .................................................................................. 37 Figure 3.3 Pre and Post COVID-19 Travel Survey ...................................................................... 40 Figure 4.1. Conceptual Framework .............................................................................................. 47 Figure 5.1. Interdependencies Among Different Choice Dimensions .......................................... 69 Figure 5.2 Distribution of Activity Start Time Alternatives ......................................................... 74 Figure 5.3 Distribution of Mode-companionship Choice Alternatives ........................................ 74 Figure 5.4 Distribution of Destination Location Choice Alternatives .......................................... 74 Figure 7.1 Example of Travel Patterns ....................................................................................... 119 Figure 8.1 Conceptual Framework ............................................................................................. 139 Figure 8.2 Distribution of Individuals who Engaged in Online Grocery Shopping, Online Food Ordering, and Work from Home Few times a week/Everyday .................................................. 141 Figure 8.3 Distribution of Individuals who Engaged in Online Grocery Shopping, Online Food Ordering, and Work from Home in the Later Stage of the Pandemic (in past 7 days of survey) 141 xiv LIST OF ABBREVIATIONS ABM Activity Based Model ADAPTS Agent-based Dynamic Activity Planning and Travel Scheduling AIC Akaike Information Criteria ALBATROSS A Learning-Based, Transportation-Oriented Simulation System BC British Columbia BIC Bayesian Information Criteria CEMDAP Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns CUSTOM Comprehensive Utility based System of Activity-Travel Scheduling Option Modelling DA Dissemination Area DIC Deviance Information Criterion FIML Full Information Maximum Likelihood GHG Greenhouse Gas GHK Geweke-Hajivassiliou-Keane ICT Information Communication and Technology IIA Independence of Irrelevant Alternatives IPF Iterative Proportional Fitting LOS Level-of-Service LR Likelihood Ratio MCMC Markov Chain Monte Carlo xv MDC Multiple Discrete-continuous MDCEV Multiple Discrete-continuous Extreme Value MDCEV-OP Multiple Discrete-continuous Extreme Value with Ordered Preference MDCNEV Multiple Discrete-Continuous Nested Extreme Value MNL Multinomial Logit MVOP Multivariate Ordered Probit OTS Okanagan Travel Survey RMSE Root Mean Squared Error SALT Scheduler for Activities, Location, and Travel SDS Shorter-term Decision Simulator VIF Variance Inflation Factor VKT Vehicle Kilometre Travelled xvi ACKNOWLEDGEMENTS I would like to sincerely thank several individuals for their unwavering support, direction, and inspiration during my time as a PhD student at the University of British Columbia, Okanagan (UBCO). First and foremost, I want to sincerely thank Dr. Mahmudur Fatmi, my supervisor, for all his help and encouragement. I consider it an honor to work under his astute supervision. I am deeply thankful for his invaluable advice, for sharing his immense knowledge with me, for guiding me, for constantly inspiring me to publish, for giving me confidence boosts, and for his unwavering support—not just as a mentor but also as a friend. He has greatly inspired me to elevate both my personal and academic standards. I'm also appreciative of the funds I've received to work as a teaching assistant and research assistant; without him, it would not have been feasible. I would like to extend my sincere gratitude to my supervisory committee members - Dr. Kasun Hewage and Dr. Zheng Liu, for their insightful feedback and suggestions on my research during my time as a PhD student. My thesis would not have met this standard without their insightful reviews. My profound gratitude goes out to Dr. Mathew Roorda, my external examiner, for his time, knowledge, and insightful comments that have greatly improved my thesis. I would also like to thank Dr. Babak Mohamadpour Tosarkani, the university examiner, for his valuable time and wise remarks. The data for this research was provided by the City of Kelowna and UBC Integrated Transportation Research (UiTR) Laboratory, for which I am grateful. I sincerely appreciate Dr. Abdul Rawoof Pinjari, Dr. Shobhit Saxena, Dr. Makoto Chikaraishi, and Dr. Varun Varghese for sharing their knowledge with me. A special thanks goes out to all the researchers at the UBC Integrated xvii Transportation Research (UiTR) Laboratory. I want to express my gratitude to Trevor, Nathan, and Corrie for editing my papers. A special thank you to my wife, Maitreyi Kiran Joshi, and my parents, Dr Vijay Kumar Khaddar and Dr Shree Dwivedi Khaddar for their unwavering inspiration and tangible support. I could not have completed this work without the support of my entire family. Most importantly, I want to express my gratitude to the Almighty God for giving me the fortitude, wisdom, and good health I needed to finish my PhD journey. xviii DEDICATION To My family1 CHAPTER 1: INTRODUCTION 1.1. Background and Motivation Travel demand models are used to make predictions about future transportation choices based on certain conditions and/or scenarios. Government and transit agencies among others use these models to develop transportation policies and infrastructure investment decisions such as transit-oriented development, and travel demand management strategies such as congestion pricing, parking strategies, and work-from-home strategies, among others. These travel demand models are used to evaluate the effectiveness of alternative scenarios to tackle transportation-related challenges such as urban sprawl, traffic congestion, and transport related greenhouse gas (GHG) emissions, among others. The accuracy in representing individuals’ behaviour and the consequent prediction ability of these travel demand models largely dictates the effectiveness and unbiased nature of a policy and investment. One of the popular methods for travel demand modelling is trip-based models assuming trip (i.e., travel between points A and B) as the unit of analysis. Such models include four steps referred to as ‘trip generation’, ‘trip distribution’, ‘mode choice’, and ‘trip assignment’ (Figure 1.1). These models are often developed at the aggregate-level (Bhat and Koppelman, 2006) – i.e., population aggregation (e.g., dividing the population into different groups based on socio-demographic attributes), spatial aggregation (e.g., dividing a region into zone), and temporal aggregation (e.g., considering for the peak-hour traffic). These trip-based models are sensitive to forecasting the number of trips, location of trips, modes, and routes of trips. However, one of the major criticisms of these models is incapability to capture decision maker’s actual travel behaviour. Another major limitation is their inability to capture interactions among different travel decisions of an individual. 2 For example, trip-based models are often developed for certain types of trips (e.g., work trips) made during the peak-hours without considering the interactions among other trips (e.g., recreational, and shopping trips) and travel decisions (e.g., time, mode, and destination) (Pinjari and Bhat, 2011). This limitation leads trip-based models to ignore the complex temporal-modal-spatial linkages, which can result in illogical trip predictions (Davidson et al., 2007). For example, the decision to travel with children can substantially govern other choices such as time, mode, and destination of travel. Such granular level interactions are often ignored in these travel demand models, indicating the incapability to capture decision maker’s actual travel behaviour. Due to capturing a snapshot of travel at the aggregate-level, trip-based models pose challenges in replicating and forecasting travel patterns realistically, which can further lead to distorted travel demand management policy formulations (Zhong et al., 2015). Figure 1.1 Components of Trip Based Mode Source: (Bhat et al., 2003) To this end, an alternative approach to trip-based model is an activity-based model (ABM), which assumes that travel decisions are made based on individuals’ daily needs and their desire to participate in activities over time and space to accomplish those needs (Kitamura, 1988; Pinjari 3 and Bhat, 2011). ABM considers activity (e.g., work, recreation, leisure) as a building block and therefore makes “travel” a “derived demand” from the desire to participate in an activity (Davidson et al., 2007; Kitamura and Fujii, 1998). ABM can accommodate temporal-spatial constraints and intricate relationships that exist among different activity and travel decisions at a disaggregated individual/household level. In this context, an ABM typically includes two components: activity generation and activity scheduling. Activity generation focuses on answering the following questions: what types of activities to participate in? for how long? and how many times? Activity scheduling focuses on answering the following questions: what time to participate in an activity? where to perform an activity? what type of mode to choose for travelling purpose? and with whom to travel/perform activity? Figure 1.1 denotes different components of a typical ABM (Tajaddini et al., 2020). In this context, ABMs view aforementioned decisions over a 24-hour time period where individuals allocate certain durations to different activities and make associated travel decisions (e.g., time of day, mode, companionship, destination location), subject to their sociodemographic, spatial-temporal, and other related attributes. Due to such strong theoretical underpinning and disaggregate-level approach, ABMs represent travel behaviour realistically as well as result in more accurate forecasting of travel demand as compared to trip based models (Miller, 2023). Lastly from an implication perspective, ABMs provide a better ability to develop a vast variety of transportation-related plans and polices such as work from home which is a temporally sensitive strategy. The accuracy of predicting one’s travel demand and sensitiveness towards policy testing largely depends on how adequately ABMs mimic one’s “actual” behaviour. To do so, ABMs require to capture the underlying decision process and behavioural mechanism of one’s activity-travel decisions. Activity-travel decisions are complex and interdependent in nature. For example, the 4 Figure 1.2 Components of Typical ABM Source: (Tajaddini et al., 2020) decision to engage in an activity is linked with its temporal allocation (i.e., activity duration) and is often governed by other travel decisions. Similarly, willingness to perform an activity with children may encourage travel by car. Therefore, time use (e.g., activity participation and duration, activity start time), travel (e.g., travel mode, travel companionship), and land use (e.g., destination location) decisions interact with each other due to spatial-temporal-personal constraints of an individual and are often made contemporaneously. In addition to travel activities, advances in information communication and technology (ICT) and unprecedented events such as the COVID-19 pandemic have increased the virtual accessibility to different activities (collectively known as teleactivities), influencing one’s travel demand (Dias et al., 2020). This virtual accessibility can substitute/complement/change in-person activity engagement. In this context, a surge in telecommuting (e.g., work from home) has been noticed which has the potential to cause substantial changes in day-to-day travel patterns and lifestyles (Mouratidis and Papagiannakis, 2021). Since teleactivities can ease temporal-spatial constraints such as commute to work travel time saving, it can significantly impact activity participation and 5 associated travel decisions (Rajagopalan et al., 2009). For example, individuals working from home may allocate this time to other non-mandatory activities in a day. This dissimilarity may result in different travel outcomes such as vehicle kilometre travelled (VKT) in a day and may also impact other activity engagement decisions (Dias et al., 2020; Moeckel, 2017). Keeping the above discussion in mind, past behavioural modelling efforts have provided crucial insights into activity-travel patterns. However, there is considerable room for enhancing these modelling techniques by capturing the interactions among time use, travel, and land use-related decisions, and accommodating how they may vary by work arrangement. 1.2. Research Gaps The research gaps that this thesis tries to fill are the following: Firstly, from an activity generation modelling standpoint, existing modelling approaches consider aggregated choice alternatives at the level of daily activities, without considering episode-level engagement of activities (Bhat et al., 2013; Enam and Konduri, 2018; Palma et al., 2021). More specifically, they ignore multiple occurrences of the same activity type often referred to as episodes, that can be of different durations, performed at different times of day, using different modes, and at different locations. For example, an individual can travel to location A to perform a shopping activity for 30 minutes, then travel to location B to dine- in, and again travel to location C for shopping for 30 minutes. In this case, the existing modelling technique aggregates the two episodes of shopping into one activity performed for 1 hour which is an inaccurate representation of activity generation choices and ignores the episode-level decisions such as mode and destinations which may lead to inaccurate prediction of travel demand. In this context, land use and activity generation decisions share an intricate relationship (Eluru et al., 2010). This warrants a need to disentangle how activity generation decisions are influenced by land use decisions such 6 as destination location, providing important insights about the impact of the built environment and land use attributes. This exercise is important for a more realistic representation of activity-travel patterns and robust policy formulations. Secondly, from an activity scheduling modelling perspective, time use (e.g., activity start time), travel (e.g., mode, companionship), and land use (e.g., destination location) decisions share a complex interdependency and are often made contemporaneously. For example, travel with a spouse or with children can interact with activity start time, mode choice, and destination location choice. From a modelling perspective, limited approaches have focused on disentangling the interdependency that exists among these choice dimensions (Golshani et al., 2018; Hasnine and Habib, 2020; Nurul Habib, 2012; Shabanpour et al., 2017). Accounting for interactions among time use-travel-land use choice alternatives can provide granular level insights into the activity scheduling decisions of an individual (Nurul Habib et al., 2008). Moreover, these decisions are governed by observed (e.g., socio-demographic characteristics which are typically captured in travel surveys) and unobserved (e.g., attitudes and beliefs which are often not captured in typical travel surveys) factors and there is a need to accommodate them in the choice modelling process to provide accurate estimation and avoid distorted policy formulations. This warrants a need to adopt a robust statistical modelling approach accounting for observed and unobserved factors as well as capture the trade-offs among these choice dimensions. Accounting for such interactions in a unified framework will provide granular insights into the activity scheduling decision of an individual, making ABMs more behaviourally realistic. Thirdly, as telecommuting becomes more prominent due to rapid digitalization (ICT usage) coupled with exposure to such remote activities during the COVID-19 pandemic, activity-travel behaviour may have also become more complex (Motte-Baumvol et al., 2024). In this context, 7 there is a need to understand the influence of telecommuting on activity generation patterns. Particularly, telecommuters (i.e., individuals working from home), and non-telecommuters (i.e., individuals working at the workplace) might have significantly different daily activity generation patterns. For instance, the savings in commute travel time may allow telecommuters to allocate more time to non-mandatory (e.g., shopping, recreation, leisure) activities, which might not be true for non-telecommuters. Keeping this in mind, there is a need to investigate differences in activity engagement patterns, including duration and frequency of non-mandatory activities for telecommuters and non-telecommuters at a disaggregated episode-level. This exercise can assist in developing future travel demand models that are more sensitive to such worker profiles. Fourthly, the decision to telecommute or not and associated travel outcomes share a complex interdependency (Wöhner, 2022). In this regard, telecommuting may have a significant impact on travel outcomes (e.g., VKT) as it eases spatial-temporal related constraints and eliminates the need to commute to work. However, this might not be the case for non-telecommuters. This indicates the presence of heterogeneity in the travel outcomes between the two worker profiles. Furthermore, workers may self-select themselves into a particular work arrangement type. Therefore, to understand the influence of work arrangement type on travel outcome, there is a need to explore this behavioural heterogeneity that exists between telecommuters and non-telecommuters, while accounting for self-selection issues. This exercise not only circumvents biased model estimations but also aids in gauging the effect of the work arrangement (i.e., telecommuting or non-telecommuting) on travel outcomes. In addition, there is a need to quantify the effect of one’s self-selection into a particular work arrangement type on associated travel outcomes such as VKTs in a more realistic manner. 8 Finally, telecommuting may also influence engagement in other teleactivities. For example, individuals’ decisions to telecommute may impact the method of other daily activities to be transitioned into teleactivities such as teleshopping and online food ordering, and vice versa. Moreover, for a better understanding of future teleactivity engagement patterns, it is important to consider the extent to which historical exposure to such teleactivities will influence them. Such relationships predominantly affect one’s daily travel decisions and incorporating them will enhance the behavioural realism and improve overall forecasting accuracy. 1.3. Research Objectives The goal of the thesis is to develop advanced behavioural models to capture activity-travel related decisions, their interdependencies, and how they are influenced by telecommuting. The specific objectives of this thesis are: • To model activity generation and destination choice decisions at a disaggregate episode level. • To model activity scheduling decisions including activity start time, travel mode, activity companionship, and destination location choices while capturing interdependencies among these decisions. • To investigate differences in activity generation patterns of telecommuters and non-telecommuters. • To model the decision to telecommute or non-telecommute and associated vehicle kilometre travelled while capturing the trade-offs among these decisions. • To model preferences towards teleshopping and telecommuting in the post-pandemic period while capturing interdependencies among these activities. 9 1.4. Thesis Outline This thesis comprises a total of nine chapters (Figure 1.2). Chapter 1 provides a background and motivation for the proposed research. In addition to this, identified research gaps and research objectives are outlined in this chapter. Chapter 2 provides a review of relevant literature. First, the concept of an ABM is discussed followed by multiple approaches adopted in the past to develop an operational ABM. Lastly, an extensive review of the different modelling approaches adopted in the past to investigate activity-travel decisions is provided. Chapter 3 provides a brief introduction to the study area. Then, this chapter presents a description of multiple data sources utilized in this thesis. The chapter also includes a description of the generation of explanatory variables, which are further used to develop advanced econometric models. Chapter 4 presents the developed model for activity generation decisions. This chapter focuses on a brief introduction about the proposed research, data description, independent variables generated, modelling approach, discussion of the results, and finally includes a concluding remark. Chapter 5 presents the developed model for activity scheduling decisions and captures interdependencies among these decisions. This chapter focuses on a brief introduction about the proposed research, data description, independent variables generated, modelling approach, discussion of the results, and finally includes a concluding remark. Chapter 6 presents the developed model to investigate variation in activity generation decisions of telecommuters and non-telecommuters. This chapter focuses on a brief introduction about the 10 proposed research, data description, independent variables generated, modelling approach, discussion of the results, and finally includes a concluding remark. Chapter 7 presents the developed model for work arrangement choices and associated travel outcomes. This chapter focuses on a brief introduction about the proposed research, data description, independent variables generated, modelling approach, discussion of the results, and finally includes a concluding remark. Chapter 8 presents the developed model to capture preferences towards teleshopping and telecommuting in the post-pandemic period. This chapter focuses on a brief introduction about the proposed research, data description, independent variables generated, modelling approach, discussion of the results, and finally includes a concluding remark. Chapter 9 summarizes the contribution of the proposed research, followed by its limitations and the scope of future research. 11 Figure 1.2 Thesis Outline 12 CHAPTER 2: LITERATURE REVIEW 2.1. Activity-Based Travel Demand Models Activity-based models (ABMs) have been developed since the early 1970s. These models are based on a theory that postulates that an individual travelling to a specific destination, at a particular time of day, by certain mode, with/without someone is a result of a need to engage in an activity (Hägerstrand, 1970). Therefore, ABMs consider activity as the building block while “travel” is a “derived demand” from the desire to participate in an activity in a certain time and space resolution (Davidson et al., 2007; Kitamura and Fujii, 1998). ABMs are considered to address the limitations of the traditional four-step travel demand models because they can account for spatial-temporal constraints and activity-travel interactions at an individual and/or household level (Chu et al., 2012). This enables ABMs to replicate one’s travel decisions more realistically at a granular level. Typically, ABMs comprise of the following two components: activity generation and activity scheduling, (Castiglione et al., 2015). Activity generation includes the modelling of the decisions related to activity engagement type and activity duration. Whereas the activity scheduling component models time of day, destination location, travel mode, and companionship decisions. These decisions are interdependent in nature and subsequent activity decisions are governed by previous activity and related spatial-temporal, travel decisions which are often subject to a time budget such as 24-hour for daily models. This understanding of the ABM paradigm enables better prediction of activity-travel decisions and makes them more sensitive to policies. In this regard, various advancements have been made in developing ABMs. This development can be broadly categorized as: a) constraints-based models, b) utility maximization-based models, c) 13 heuristic models, and d) hybrid models (Rasouli and Timmermans, 2014). Initially, ABMs were developed using a constraints-based approach. These models were based on an understanding of whether activity engagement is feasible within specific spatial-temporal constraints or not. CARLA is one of the first models that utilizes a list of activities and their associated duration that needs to be scheduled to generate all activity patterns (Clarke, 1986). Utility maximization-based models were developed with an understanding that individuals make activity-travel decisions to maximize their utility based on these decisions (Bhat et al., 2004). Such models adopt a series of econometric models to predict one’s activity-travel decisions. One of the notable models that uses a series of utility maximization-based econometric models is the Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns (CEMDAP). This model consists of an activity generation and activity scheduling module, dealing with one’s activity-travel choices (Bhat et al., 2004). Utilizing a similar approach, a Comprehensive Utility based System of Activity-Travel Scheduling Option Modelling (CUSTOM) was proposed that simulates an individual’s activity-travel demand. Moreover, it uses a utility maximization approach to jointly model activity engagement, activity duration, and destination location choices (Nurul Habib, 2018). Models such as SCHEDULER (Gärling et al., 1994) and A Learning-Based, Transportation-Oriented Simulation System (ALBATROSS) (Arentze and Timmermans, 2004) were developed by applying heuristics decisions/rules to mimic activity-travel behaviour more realistically. These models involve fixed and deterministic rules while generating activity patterns. In this context, ALBATROSS simulates activity generation, activity scheduling, travel mode choice, destination location choice, and companionship choice by accommodating multiple household, spatial, and temporal constraints. However, due to very strong linkages with rules, these models ignore the 14 underlying behavioural decision-making process of an individual. To overcome such issues hybrid models are utilized. These models combine multiple approaches (i.e., utility maximization and rule-based) to model activity-travel decisions. For example, Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) (Auld and Mohammadian, 2009a) and TASHA (Roorda et al., 2008) are a few examples of such hybrid approaches. Recently, based on a similar approach, a novel activity-based shorter-term decision simulator (SDS) was developed to forecast activity-travel choices (Khan and Habib, 2021). Moreover, Scheduler for Activities, Location, and Travel (SALT) was presented to better understand activity-travel decisions taking place in-home and out-of-home (Hesam Hafezi et al., 2022). This modelling system utilizes a series of econometric models to micro-simulate one’s choices. The fundamental need of an ABM is to capture individuals' activity-travel behaviour, which will enable them to accurately predict and become responsive to policies. 2.2. Behavioural Modelling of Activity-Travel Decisions: Independent Modelling The activity-travel decisions that are of interest for this thesis include activity engagement, activity duration, activity start time, mode, companionship, and destination location. A large body of existing research focuses on modelling various activity decisions independently – meaning they ignore the simultaneous interactions among different decisions and focus on modeling the factors influencing one decision. In the case of methodology, many studies have conceptualized activity-travel decisions as a discrete choice problem. More specifically, representing these decisions as a discrete entity. For example, in the case of activity generation decisions, many efforts were made to investigate ‘activity engagement’ and ‘activity duration’ choices using discrete choice modelling structures (Bhat et al., 2010; Kim and Wang, 2017). Furthermore, a separate 15 multinomial logit model and hazard-based model were utilized to investigate activity engagement type and duration decisions, respectively (Kitamura et al., 1997). However, discretization of activity duration may lead to aggregation bias in the model, as proper discretization of time alternatives is always a challenge. Therefore, past studies recognized the continuous nature of time for ‘activity duration’ and adopted appropriate modelling techniques. For instance, a Tobit-based approach was utilized to explore the time duration allocated to participate in non-mandatory activities (Yamamoto and Kitamura, 1999). Similarly, a hazard-based model was used to analyze activity duration of non-mandatory activities (Lee and Timmermans, 2007). These approaches considered activity duration as a continuous dimension circumventing the limitations caused by time discretization. In a similar line of investigation, past literature also made several attempts to model activity scheduling (activity start time, mode choice, companionship choice, and destination location) choices in a similar independent manner. In the context of the activity start time, earlier studies have adopted multinomial logit (MNL) modelling techniques by converting the continuous-time dimension into discrete alternatives (Small, 1982). However, they fail to capture correlation among the adjacent time intervals, which violates the independence of irrelevant alternatives (IIA) assumption of the MNL. Further, to relax the IIA assumption, a nested logit approach was adopted in a similar context (Bhat, 1998). Recently, a hybrid random utility maximization-random regret minimization discrete choice structure was deployed to capture individuals' preferences towards activity start time for non-mandatory activities (Shabanpour et al., 2017). In the context of mode choice, initial studies have also adopted MNL structures in a wide variety of empirical contexts focusing on exploring the effects of different sociodemographic and travel-related segments (Fang et al., 2022a; Sultana et al., 2018; Villena-sanchez et al., 2022). To further 16 relax the IIA assumption of the MNL and capture the unobserved heterogeneity among individuals, studies have adopted advanced econometric models such as mixed logit models and latent class models (Fu, 2021; Shamshiripour et al., 2019). Moreover, capturing activity destination location choices of an individual has been widely explored in recent decades using discrete choice models, capturing the effect of built environments on travel behaviour (Fang et al., 2022b; Kim and Lee, 2017; Tsoleridis et al., 2022). Lastly, to investigate teleactivity engagement patterns and associated outcomes different approaches have been adopted. For instance, an MNL model was adopted to explore the telecommuting frequency and investigate the impact of residential built environment on work-from-home frequency (Tang et al., 2011). In a similar line of investigation, another study also adopted a tobit regression approach to observe the impact of telecommuting on travel outcomes such as VKT, while introducing telecommuting as a dummy variable (Zhu and Mason, 2014). However, these approaches have certain limitations. A crucial criticism of independent models is that they fail to capture intricate relationships or interdependencies that exist among different activity-travel dimensions. Failing to account for such complex decision-making process can lead to an unrealistic representation of one’s travel behaviour. This will not only impact the overall accuracy and forecasting power of an ABM but also may lead to distorted policy formulations. To this end, several approaches were developed to simultaneously model activity-travel decisions and capture the underlying decision-making process, which is discussed in the next section. 2.3. Behavioural Modelling of Activity-Travel Decisions: Joint Modelling Recognizing an understanding that activity-travel decisions are interdependent and complex in nature, past literature made several attempts to jointly model various choices utilizing a single unified framework. This approach of capturing interrelationships/trade-offs among activity-travel 17 dimensions enhanced the behavioural realism of the econometric models and provided granular level information that has been ignored in the past (Garikapati et al., 2014; Rasouli and Timmermans, 2014). Keeping this in mind, the next sections only provide a review of joint models adopted in the past to explore activity-travel dimensions. 2.3.1. Modelling of Activity Generation Decisions Activity generation involves modelling the decisions of ‘what type of activity to engage in? (referred to as activity engagement)’ and ‘for how long? (referred to as activity duration)’. These decisions share an intricate interdependency. Methodologically, several approaches have been adopted to model activity generation decisions (Rasouli and Timmermans, 2014). For instance, a discrete choice modelling structure was utilized to model activity participation and activity duration simultaneously (Ettema, 2005). However, due to the nature of decisions like activity engagement (discrete) and duration (continuous) and because individuals can potentially take part in multiple activities in a day, multiple discrete-continuous (MDC) choice models have been widely used. For instance, joint logit-hazard based approach was adopted to examine an individual’s engagement in shopping activity and associated time allocations (Srinivasan and Bhat, 2005). More importantly, as the activity-travel decisions are spread over a 24-hour time period and are subject to time constraints, appropriate modelling techniques have been adopted in the past. For instance, the multiple discrete-continuous extreme value (MDCEV) framework was utilized to model the simultaneous decisions of participating and allocating time in different activities over a 24-hour time period (Garikapati et al., 2014). The model was developed considering home-based tours as the unit of analysis. Each tour was assumed to include one or more stops where individuals spent time for different activities. Similarly, an MDCEV framework was used to model activity engagement decisions and accommodate household member interactions (Bhat et al., 2013). They 18 confirm that built environment and neighborhood measures impact activity engagement and duration decisions at a household level. Recently, activity engagement and duration allocation behaviour of different generations (e.g., Generation X, Millennials) were examined using a similar framework allowing simultaneous modelling of discrete and continuous choices (Enam and Konduri, 2018). However, one of the major limitations of the existing activity generation literature based on MDC modelling approaches is the aggregation of activity generation and time duration to the level of different types of activities – i.e., allocating time to each activity type for a specific period (i.e., typically over a day) rather than accounting for time allocation at a disaggregate episode-level for each activity type. For example, an individual can travel to location A to perform a shopping activity for 2 hours, then travel to location B to dine-in for 1 hour, and again travel to location C for shopping for 2 hours (Figure 2.1). Figure 2.1 Disaggregated vs Aggregated approach In this case, the conventional MDCEV (aggregated approach) model aggregates the two episodes of shopping into one activity performed for 4 hours, and 1-hour lunch, thus, does not reveal the frequency of engagement in shopping episodes and also fails to account for associated travel decisions such as time of day/mode/destination which vary by each episode. However, past 19 literature has adopted approaches to model disaggregate episode-level time use decisions by defining an individual’s activity engagement as the combination of activity type and time of day categories for modelling purposes. However, doing this does not account for multiple occurrences of each activity type within the time of day period (Paleti et al., 2011). However, limited attempts were made to investigate activity engagement and duration allocation at an episode level while accounting for multiple episodes of each activity type (Palma et al., 2021). Therefore, to capture activity generation decisions in a more behaviourally realistic manner there is a need to extend existing models to disaggregate episode level. In addition to this, decisions of what activities to engage in and for how long are complex processes and are often influenced by the decisions of where to travel, how to travel, and with whom to travel (Akar et al., 2009). This complex interdependency can be termed as activity-based accessibility, providing information on how different travel-related choices affect activity engagement and duration decisions (Dong et al., 2006). To incorporate such complex interdependency and model multiple choice dimensions simultaneously, studies have adopted joint modelling frameworks. For instance, a joint modelling system, where the MDCEV model was used to represent activity engagement and duration components, and destination location choice was modeled using multinomial logit (MNL) model which was further nested in the MDCEV framework (Eluru et al., 2010). Nesting within the same framework allowed authors to extensively capture the effect of the built environment and land use attributes on the activity generation process. Similarly, another study adopted the MDCEV modelling technique to estimate tour-based activity engagements and duration and used a logsum parameter to provide feedback to estimate the effects of social utility (i.e., individuals’ desire to engage in an activity alone or not) on activity generation (Khan and Habib, 2021). In this line of investigation, it is quite evident that land use and activity generation 20 decisions share an intricate relationship. However, limited studies have explored this relationship at such granular episode level (Eluru et al., 2010). More specifically, there is a need to disentangle how land use decisions such as destination location influences activity generation choices providing important insights about the impact of built environment and land use attributes. 2.3.2. Modelling of Activity Scheduling Decisions One of the important aspects of an ABM is its capacity to replicate or mimic daily activity scheduling decisions addressing the underlying behavioural dynamics of an individual’s daily travel (Miller and Roorda, 2003). This includes decisions such as activity start time, companionship, mode, and destination location choice (Miller, 2021). These attributes are key components of travel demand forecasting models and significantly impact the temporal and spatial distribution of one’s activity-travel pattern. From a behavioural modelling perspective, there is a need to model them jointly in a single unified framework as these decisions are closely intertwined (Nurul Habib et al., 2008). For example, an individual traveling with a spouse/partner is less likely to take transit (Khan and Habib, 2021). Similarly, individuals traveling alone are more prone to engage in activities during the PM peak hours while returning home, this may be due to the unavailability of their spouse/children (Shabanpour et al., 2017). Because of such interdependencies, there is a presence of observed and unobserved factors (not captured directly in the survey) influencing these choice dimensions (Auld and Mohammadian, 2009b; Rasouli and Timmermans, 2014). To explore such complex interdependencies among these choices and enhance the overall behavioural representation, past literature adopted several approaches. In this regard, a trivariate structure was deployed to capture the trade-offs among workers’ mode choice, activity start time, and duration dimensions (Nurul Habib, 2012). This study mainly focuses on modelling the 21 commuter’s activity schedule highlighting important behavioural interactions among these choice dimensions. In a similar line of investigation, a joint discrete-continuous framework was adopted to investigate the trip timing (continuous dimension) and mode choice (discrete dimension) decisions jointly (Nurul Habib et al., 2009). This study jointly estimated a multinomial logit model for mode choice and a hazard-based model for trip timing decisions, while accounting for the correlations among the error terms. Similarly, different discrete-continuous approaches were adopted to address the jointness among activity time use and mode share decisions (Habib, 2013; Liu et al., 2017). These studies provided promising insights on trip timing decisions and interactions with mode choice which not only enhances the behavioural realism of activity-based model but also can be utilized to make robust travel demand management policies. In a similar context, it is well established that companionship (i.e., with whom to engage in activity) has a strong influence on time use and associated travel decisions (Fu et al., 2016; Hu et al., 2022; Khan and Habib, 2021). For example, a joint discrete-continuous structure was adopted to investigate the effect of household member interactions on one’s activity participation and duration decisions (Bhat et al., 2013). Similarly, a mixed multiple-discrete continuous extreme value model was adopted to investigate the influence of companionship and activity purpose on vehicle type and usage decisions. The discrete part of the model is formed as a combination of companionship, mode, and activity purpose choices, whereas mileage for each alternative was considered as a continuous decision. The results reveal that companionship choices such as ‘activity participation with a household member or non-household member’ and mode choice decisions are considered consciously while engaging in non-mandatory activities impacting overall travel behaviour (Imani et al., 2014). Furthermore, a nested discrete-continuous structure was adopted to explore the activity participation and duration patterns for dual-earner households 22 living with and without children (Bernardo et al., 2015). This study reveals that the presence of children significantly reduces non-mandatory activity participation. To further explore the effect of companionship on activity-travel patterns, various studies have extensively investigated joint decisions of companionship and mode choice, and companionship and activity timing choice. In the context of companionship-mode choice, a nested logit framework was deployed to investigate intrahousehold trade-offs associated with travel mode choices for home-based tours (Ho and Mulley, 2013). The study confirms that maintenance and discretionary activities are more likely to be performed with household members. Similarly, a copula-based approach was adopted to understand the trade-offs among mode and companionship choices for school trips (Ermagun et al., 2015). Moreover, in terms of companionship-activity timing, a study explored the relationship between social context (i.e. activity participation with household/non-household member) and activity-travel attributes such as activity start time and duration (Nurul Habib et al., 2008). The findings of this study reveal that the social context of activity participation strongly affects scheduling decisions, and such accommodation in an activity-based model significantly improves overall behavioural realism. Lastly, in terms of capturing interactions between destination location decisions and other travel attributes, past literature investigated the effect of land use and built environments on activity engagement and duration decisions (Eluru et al., 2010). They adopted a joint MNL-MDCEV framework to explore the activity time use choices, along with destination location decisions. Similarly, a recently proposed multiple discrete-continuous extreme value with ordered preference (MDCEV-OP) model was adopted to investigate the interaction among time use and land use decisions, at a disaggregate episode level (Khaddar et al., 2022). In addition to this, several studies have attempted to jointly model mode and destination location choices. The majority of these 23 studies have adopted a nested logit approach providing insights into the hierarchical ordering of mode and destination location choices in different contexts (Miller et al., 2019; Newman and Bernardin, 2010; Yagi and Mohammadian, 2008). In addition to this, one of the major concerns with replicating the destination location choice decisions is defining a behaviourally plausible activity destination location choice set (Arentze et al., 2013). Several techniques such as random sampling of alternatives (Nerella and Bhat, 2004; Sivakumar and Bhat, 2007) and stratified sampling techniques, which are mainly based on logical reasoning have been adopted in the past (Hassan et al., 2019; Tsoleridis et al., 2022). However, considering destination accessibility measures such as travel time/distance as an alternative can mimic/replicate a more realistic destination location choice behaviour of an individual (Scott and He, 2012; Yang et al., 2013). Keeping the above discussion in mind, it is evident that past literature has utilized various solitary and unified utility maximization frameworks to investigate activity-travel choice dimensions. However, in most cases, these investigations tackled time use (e.g., activity start time) and travel-related decisions (e.g., mode and companionship) only, limiting their interactions with spatial decisions such as destination location choices (Hawkins and Nurul Habib, 2021). Therefore, to enhance behavioural realism, there is a need to capture interrelationships among time use-travel-land use decisions such as activity time of day, mode choice, companionship, and destination location choices. 2.3.3. Modelling of Teleactivities and Travel Outcomes In addition to modelling traditional activity-travel decisions, rapid digitalization and adoption of ICT usage have provided virtual accessibility that triggered a shift from in-person activity engagement to the adoption of teleactivities (conducting activities online) (De Vos, 2020). These activities are not limited to online shopping (also referred to as teleshopping), online food ordering 24 from restaurants, and working from home (also referred to as telecommuting), among others (Mouratidis and Papagiannakis, 2021). In addition to this, unprecedented events such as COVID-19 caused drastic changes in one’s travel behaviour and day-to-day mobility patterns. This shift can ease the temporal-spatial constraints of an individual encouraging them to engage in other activities and significantly impact associated travel decisions that warrant continuous attention. Therefore, the next section provides a review of different studies exploring engagement in teleactivities and associated outcomes. 2.3.3.1. Modelling Behavioural Differences Between Telecommuters and Non-telecommuters Telecommuting is a flexible working arrangement that allows workers to work remotely, either from home (the most common type of telecommuting) or another location that is not the actual workplace (ex. co-working space), through the use of ICT (Mokhtarian, 1991). Although telecommuting is often seen as a means of lessening peak-hour congestion and emissions due to the reduction in travel for mandatory activities like work (Elldér, 2020), it might induce an increase in non-mandatory activities such as personal business and recreation activities. For instance, telecommuters do not have to commute, providing more time to be allotted to other activities. Telecommuters could generate more trips since they are unable to perform non-mandatory activities on commuting trips. Furthermore, since telecommuters are likely to be staying at-home for most of the day, when they go out they could be using vehicles and for longer distances resulting in increased congestion and emissions. On the other hand, non-telecommuters are likely to engage in non-mandatory activities after work (Rajagopalan et al., 2009). These differences generate a need to understand telecommuters’ daily time-use behaviour, particularly for non-mandatory activities, and how they are different than non-telecommuters. 25 Past literature has largely focused on investigating the frequency of non-mandatory activity participation of telecommuters. For example, a two-stage least square model was developed considering socio-demographic and transportation attributes to investigate telecommuters daily total trips, work trips, and non-work trips. They reaffirm that telecommuting has a complementary effect on non-mandatory trips, implying telecommuters have longer and frequently higher activity engagement rates compared to non-telecommuters (Zhu, 2012). In a similar line of investigation, Asgari & Jin adopted a structural equation modelling system revealing causal relationships between non-mandatory activity participation and telecommuting, considering various demographic variables. They found that increased total durations of non-mandatory activities increased the likelihood of telecommuting (Asgari and Jin, 2017). Furthermore, to account for the frequency of non-mandatory activity engagement for telecommuters, a Poisson model was adopted. This study supported that telecommuting is associated with more non-mandatory trip generation, regardless of socio-demographic variables, commute distance, and other variables (He and Hu, 2015). In addition, Kim et al. investigated whether telecommuting had a substituting or complementary effect on various types of household travel, utilizing unrelated censored regression models. They found that telecommuting had a complementary effect on the households’ vehicle travel (Kim et al., 2015). These studies all successfully found that telecommuting produces increased demands of out-of-home non-mandatory activities focusing on just one aspect, either duration, trip count, or vehicle distance travel. One must note that telecommuters and non-telecommuters are likely to depict significantly different activity engagement rates and associated temporal allocations. Therefore, past studies aimed to capture such temporal interactions for the aforementioned two worker profiles by adopting joint discrete-continuous structures to replicate activity engagement and duration 26 decisions simultaneously (Calastri et al., 2017; Chu, 2020). A key illustration of this difference is that non-telecommuters have the opportunity to perform non-mandatory activities during their commutes (conceivably while returning home from work), an opportunity not available to telecommuters. To reaffirm this, Srinivasan & Bhat ran a multiple-component model system which included a seemingly unrelated regression model and a joint mixed logit hazard-duration model to investigate factors impacting activity generation of four different types of household structures (Srinivasan and Bhat, 2005). They revealed that non-telecommuters who leave work before 4 p.m. have longer shopping durations than those who leave later (Srinivasan and Bhat, 2005). Similarly, Garikapati et al. adopted a multiple-discrete continuous framework to model activity participation and time allocation for daily commuters primarily making home-based work tours. The study reveals a negative effect of higher commute durations on escorting and maintenance activities. However, female workers made more non-mandatory detours, maybe due to their household roles/responsibilities (Garikapati et al., 2014). In a similar line of investigation, Rajagopalan et al. utilized a Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) model to examine non-mandatory activity timing behaviour of non-telecommuters, and found that, for individuals and couples, the most preferred time to perform out-of-home non-mandatory activities was after arriving back home from work (Rajagopalan et al., 2009). On the other hand, past literature also attempts to shed light on 24-hour time-use patterns of telecommuters. A major confounding factor associated with non-mandatory activity participation for telecommuters is the desire to “get out” and avoid “cabin fever”, as they are often confined to a single location (Kim et al., 2015). To illustrate such differences, Pendyala et al., utilized two waves of travel diaries from California, reaffirming that telecommuters tend to spread their participation in non-mandatory activities over the day (Ram M. Pendyala et al., 1991), avoiding 27 sedentary lifestyles associated with telecommuting. Furthermore, Srinivasan & Bhat developed a MDC structure to investigate nuclear family couples’ solo- and joint-discretionary activity participation and duration considering socio-demographics, household characteristics, mandatory activity participation, schedule variables (day-of-week and season-of-year), and built environment characteristics. They found that individuals working from home for long durations are less likely to participate in longer durations of out-of-home non-mandatory activities (Srinivasan and Bhat, 2006). Similarly, Paleti & Vukovic developed a joint count and MDCEV model to investigate the choice of telecommuting in multi-worker households, and its effect on daily time-use behaviour of household members. They found individuals who telecommute frequently were less likely to participate in eat out and socializing activities (Paleti and Vukovic, 2017). However, a major limitation of these studies is ignoring the multiple occurrences of each activity type (i.e., episodes). This may lead to biased and unrealistic behavioural representation of the two worker profiles. 2.3.3.2. Modelling Decision to Telecommute or Non-telecommute and Associated Travel Outcomes Telecommuting is often considered a work arrangement that aims to reduce congestion during peak hours, associated travel emissions, and most importantly work-related travel (Elldér, 2020). Telecommuting has a significant impact on travel behaviour as it eases spatial-temporal travel-related constraints, and substitutes commute travel to work. However, its impact on non-work travel needs further investigation. For instance, the flexibility associated with telecommuting can encourage individuals to live farther away from the workplace and travel longer distances for daily non-work activity engagement (Shah et al., 2022). Moreover, due to the elimination of commute travel time, workers may reallocate this extra time to non-work related travel increasing their daily trip rate and overall household travel. This behaviour of telecommuters is often different than 28 commuters, as commuters can engage in non-work related travel during their commute (e.g., while returning home from work), circumventing the need to travel again for certain purposes. This led to heterogeneity in non-work travel outcomes of the two worker profiles which needs further investigation. In this line of investigation, studies have largely focused on quantifying the travel outcomes of telecommuters and non-telecommuters. For instance, telecommuting has a substitutionary effect on non-work related trips because of spatial-temporal alterations induced by telecommuting in one’s travel behaviour (Ram M Pendyala et al., 1991). This study suggested that telecommuters reduce non-work-related travel by confining their travel-activity realms closer to home and thus discovering destinations in proximity to home. Furthermore, telecommuters may deliberately perform multiple activities in a single tour, which may again reduce their overall travel (Mokhtarian, 2004). On the contrary, several studies also suggest that telecommuting induces new travel. For example, availability of extra time (saved from not commuting) may encourage telecommuters to travel longer distances for certain purposes such as to eat out (Zhu, 2012). Similarly, telecommuters may still end up traveling for non-work related trips that were previously linked with commute trips. For example, picking up and dropping off a child to school or child-care can induce extra VKT because of two round trips from and to home, which might not be the case if it was done during commute trip (home-school/childcare-work-school/childcare-home). In addition, telecommuting has been observed to increase an individual's propensity to chain trips and engage in complex tours (Zhu and Guo, 2022). Lastly, for non-telecommuters, studies reaffirm that such workers (traveling daily to work) have a significant positive effect on work-related VKT, however results might not be the same for non-work trips (Yin et al., 2020). A reasoning behind this is having higher diversification in choosing non-work related destinations which is closer to 29 their workplace (Chen and Borges, 2022). Another rationale for lower non-work related VKT for commuters is living in highly accessible locations which allows them to make shorter non-work tours (Chowdhury and Scott, 2020). However, non-telecommuter’s willingness to engage in non-work activities like recreation/leisure/social activities after work with children or family may again encourage them to go out after work and generate higher VKTs (Rajagopalan et al., 2009). Methodologically, multiple statistical techniques have been explored to investigate the impact of an individual’s work arrangement decisions on VKTs. For example, a tobit regression approach was utilized to investigate the daily VKT of household members when the household head is telecommuting (Kim, 2017). Another study also adopted a tobit regression approach to observe the impact of telecommuting on VKT, while introducing telecommuting as a dummy variable (Zhu and Mason, 2014). However, these studies did not account for a complex relationship that may exist among work arrangement type (i.e., telecommuting and non-telecommuting) and associated travel outcome (i.e., VKT). To account for such interdependency among these two dimensions, joint modelling approach has also been adopted. For example, a joint count and discrete-continuous model were adopted to explore the daily activity behaviour and telecommuting decision in multi-worker households (Paleti and Vukovic, 2017). Moreover, single equation approaches and structural equation modelling have also been common. Recently, a path analysis approach was adopted to observe the direct and indirect effects among residential location choice, telecommuting, and household-level VKT (Kim, 2016). Furthermore, the presence of unobserved factors also dictates the relationship among these two choice dimensions. These factors are not limited to attitudes, habits, preferences, and motivations, among others. Therefore, to account for unobserved factors (self-selection issue) and understand the complex interdependencies, past studies have adopted various approaches but 30 mainly in the context of residential location and associated VKT consumption (Mokhtarian and van Herick, 2016). For example, a two-step switching model was utilized to investigate daily VKT of residents in Shanghai, China. More specifically, the study accounts for self-selection into transit-oriented developments and non-transit-oriented developments, and further calculated expected values to quantify the effects of self-selection on VKT (Chen et al., 2017). Similarly, an endogenous switching regression approach from the family of sample selection models was adopted to jointly model residential location choice and VKT for residents in Atlanta, Georgia, USA (Kim and Mokhtarian, 2023a). The study accounts for self-selection into urban and non-urban areas while considering VKT as the outcome decision. To further quantify the effect of residential location on VKT, they estimated expected values and treatment effects (Kim and Mokhtarian, 2023a). They demonstrated how expected values would be overestimated if the self-selection effect were not controlled in their application. Since similar self-selection effects could exist in work arrangement choice, there is a need to not only account for issues related to self-selection but also utilize a statistically robust mechanism to accurately quantify the effect of work arrangement on daily non-work VKT. 2.3.3.3. Modelling Preferences Towards Telecommuting and Teleshopping In the past decades, transportation planners and researchers have started investigating factors influencing teleactivities such as telecommuting (work from home) and teleshopping (online shopping) because of their potential to influence activity-travel behaviour (Farag et al., 2007; Sener, 2011). To understand activity-travel behaviour, past studies have explored interactions between in-person activity engagement and teleactivities (Dias et al., 2020). However, most of these studies concentrate on pre-pandemic and during the pandemic activity-travel behaviour. For instance, a study adopted structural equation modelling approach to investigate the effect of 31 teleworking on activity-travel behaviour during the pandemic (Rafiq et al., 2022). In addition, a latent class cluster analysis approach was utilized to capture the heterogeneity that exists among different groups of teleworkers (Ton et al., 2022). However, the COVID-19 pandemic has altered people’s daily activity-travel patterns and choices (De Vos, 2020; Mouratidis and Papagiannakis, 2021; Parady et al., 2020). In terms of telecommuting engagement rates, the number of people working from home substantially increased during the pandemic as compared to the pre-pandemic levels across the world (Kong et al., 2022; Mouratidis and Papagiannakis, 2021). Many individuals found working from home to be productive because of “no commute time”(Beck and Hensher, 2022). On the contrary, distractions at home were one of the reasons for a reduction in the propensity towards working from home (Conway et al., 2020). Factors such as low travel cost, time-saving, and higher flexibility from employers were some motivational determinants for adopting work from home (Amekudzi‐Kennedy et al., 2020). Another teleactivity that significantly replaced in-person shopping during the pandemic was teleshopping (Mouratidis and Peters, 2022), more specifically online food ordering (Alaimo et al., 2020), and online grocery shopping (Koch et al., 2020). A significant increase in online grocery shopping during the initial stages of the pandemic was noticed as compared to pre-pandemic setting (Li et al., 2020). Furthermore, a study found convenience and delivery times to be the most significant factors affecting teleshopping choices (Kulkarni and Barge, 2020). In addition to the COVID-19 pandemic, these teleactivities are influenced by determinants such as access to ICT (e.g., internet), attitudes/experiences towards teleactivities, access to mobility tool ownership and built environment, and sociodemographic characteristics. For example, familiarity with the internet and its frequent usage is positively linked with the adoption of teleactivities 32 (Singh et al., 2013; Zhai et al., 2017). Most importantly, internet access at home via a variety of ICT devices allows one to engage in work from home activities, but also participate in online shopping (Loo and Wang, 2018). In terms of people’s attitudes and experiences towards working from home, studies suggest that reducing family-work conflicts and distracting environments can lead to positive experiences with work productivity. (Galanti et al., 2021). Good access to home internet promoted adoption of working from home; while lower delivery delays, easy navigation of website/application, infrequent order cancellation, and high quality products increased online shopping intentions (Zhai et al., 2017; Zhen et al., 2016). Among sociodemographic characteristics, gender influences teleactivity adoption choices. This could be because females tend to take higher responsibility for household-related tasks which encourages them to engage in teleactivities (both working from home and online shopping) as compared to men (Sener and Reeder, 2012). Younger individuals depicted a higher propensity towards teleactivity engagement frequency, which could be due to a higher appreciation towards ICT usage and a tendency to easily adapt to rapid digitalization (Wang and Law, 2007). Among household-level attributes, higher-income households depicted higher accessibility to ICT-related devices (e.g., smartphones, desktops) which increased their propensity towards the adoption of teleactivities (Kim and Cara, 2021). However, the results are not the same for low-income households. Households with children (aged less than 16 years old) revealed a similar trend, which may be due to childcare responsibilities, or due to such households having higher needs and requiring more trips for which children’s presence would be inconvenient (Lavieri et al., 2018). On the other hand, access to travel modes (i.e., access to a vehicle, owning a driving license, owning a transit pass) might have a negative relationship with teleactivities. Several studies revealed that individuals owning cars are less likely to engage in online shopping (Kim and Cara, 33 2021), as they provide a higher level of travel flexibility and the opportunity to travel by their preferred mode. However, results were contrary for households with lower vehicle ownership, indicating their dependency upon ICT to perform daily activities. Lastly, the built environment plays a significant role in the adoption of teleactivities (Cao, 2009). For instance, individuals living in suburban settings (far from the central business district) are more likely to adopt work from home, due to higher commute distance and travel time (Kim et al., 2013). Moreover, studies suggest that online shopping is more prevalent for individuals living in urban areas as they tend to have better computer skills and access to ICT infrastructure (Cao et al., 2013). However, some studies suggest that even rural residents follow the same trend because of the lower accessibility to shops, inciting a need for online shopping for daily needs (Blasio, 2008; Zhen et al., 2018). Furthermore, past literature confirms that teleshopping and telecommuting are intertwined and can mutually influence each other (Zhu, 2012). Therefore, some studies explored interactions that exist among teleshopping and telecommuting before the pandemic (Loo and Wang, 2018; Sener and Reeder, 2012; Zhu, 2012). Although various methodologies have been used to study the impact of several determinants on teleactivities (Gössling, 2018; Le et al., 2021; Rasouli and Timmermans, 2014; Suel and Polak, 2018), unprecedented events like pandemics warrant additional attention to this topic. For instance, there is a need to understand how working from home can impact other teleactivities such as online shopping among others. Additionally, to understand post-pandemic activity-travel behaviour there is a need to consider the effects of pre-pandemic and during pandemic experiences and frequencies. 2.4. Concluding Remarks This chapter provides a brief review of the ABM concept and an in-depth review of different econometric approaches adopted in the past to explore activity-travel decisions within an ABM 34 context. The review was structured based on the different decisions one makes over a 24-hour time period. The chapter also states the limitation of the existing econometric models that replicate one’s activity-travel patterns. Several critical concepts that make these models more behaviourally realistic are discussed above. This is not limited to 24-hour time budget constraints, specification of temporal choices in a continuous manner, interactions among different activity-travel dimensions (e.g., activity start time – mode), and interdependency among different teleactivity types and associated travel outcomes, among others. Overall, this exercise acts as a baseline for developing advanced econometric models to investigate one’s activity-travel decisions that are presented in Chapter 4 through to Chapter 8. 35 CHAPTER 3: DATA This section provides brief information about the study area and multiple data sources utilized in the study to develop advanced econometric models. The primary data section mainly provides information about the Okanagan Travel Survey (OTS) and web-based survey conducted to capture the influence of the COVID-19 pandemic on activity-travel preferences. In addition, this study also utilizes data from other sources which is discussed in the secondary data section. Lastly, this section provides a brief description of the explanatory variables generated in the study. 3.1. Study Area The study area considered in the thesis is the Okanagan region which includes the following cities/districts: Kelowna, West Kelowna, Lake Country, Vernon, and Peachland, of British Columbia (BC), Canada (Figure 3.1). The region is located in the interior region of southern BC. This region is ranked as one of the fastest growing regions in Canada and is the third largest urban area in the province of BC. It noticed a growth in population from 194,882 in 2016 to 222,162 in 2021 (Regional District of Central Okanagan, 2022). It has a population density of 78.97 people per square kilometre. Moreover, Kelowna is the largest city in the Okanagan region with a population of 144,576 in 2021. The city has a population density of 682.4 people per square kilometre (City of Kelowna, 2022). 36 Figure 3.1 Study Area 3.2. Primary Data 3.2.1. Okanagan Travel Survey The primary data for this study comes from the “Okanagan Travel Survey” (OTS) conducted in the Okanagan region of British Columbia, Canada in 2018. The OTS was conducted between late October and mid-December of 2018. The survey was a typical 24-hour household travel survey that captured household characteristics, demographics of all the household members, and detailed travel information from each member of the household (5 years of age or older) who travelled on the most recent previous weekday. The 24-hour travel log captured information on the origin and destination locations, arrival and departure time, travel purpose, travel time, number of vehicle occupants, mode of travel, transit routes chosen, and vehicle availability for the trip. In addition to travel characteristics, the survey also gathered socio-37 demographic information at the individual level such as gender, age, driver’s license, mobility challenged status, school location, employment status, occupation status, and work location. Household-level information includes home location, dwelling type, number of household members, annual household income, number of vehicles (vehicle type, fuel type), number of bikes, household size configuration (number of part-time/full-time workers, number of students, number of individuals with driver’s licenses). The survey captured information on 4886 households, 10,801 individuals, and 30,299 trips, after data validation and data cleaning. Figure 3.2 Okanagan Travel Survey (OTS) Furthermore, Table 3.1 provides a comparison between OTS and the 2016 Canadian Census. It is quite evident that the majority of the sociodemographic attributes are within a reasonable range of variability from the Census data (Table 3.1). For instance, females depict a share of 51.5% and 51.9% in Census and OTS, respectively. In the case of age, younger individuals (15 to 24) are under-represented by 1.2%. However, older individuals (65 years of age and above) are over-represented in the data. In terms of household-level attributes, the share of individuals with annual 38 household income of $125,000 and above are under-represented by 1% only. Lastly, single-detached houses and row houses/townhouses are over-represented in the data whereas, apartments or condominiums are under-presented in the data. Table 3.1. Comparison of OTS with 2016 Canadian Census Attributes Census (%) OTS Survey (%) Gender Male 48.5 48.1 Female 51.5 51.9 Age 0 to 4 4.4 3.5 5 to 14 9.8 8.2 15 to 24 8.3 7.1 25 to 34 12.0 10.2 35 to 44 11.3 10.5 45 to 54 13.7 11.3 55 to 64 15.3 19.9 65 and above 22.3 29.0 Annual Household Income $0 to $50,000 35.3 31.5 $50,000 to $80,00 22.1 24.5 $80,000 to $125,000 22.3 24.7 $125,000 and above 20.3 19.3 Household Size 1 person 27.8 23.0 2 persons 40.7 51.8 3 persons 13.5 11.5 4 persons 11.6 10.2 5+ persons 6.3 3.5 Dwelling Type Single-detached house 51.7 61.7 Apartment or condominium 23.4 17.6 Row house or townhouse 6.3 9.1 Semi-detached house 14.4 5.7 Mobile home 3.9 4.0 Other 0.3 1.9 39 3.2.2. Pre and Post COVID-19 Travel Survey To understand the post-pandemic preferences towards different activities, data from a web-based survey is also utilized in this thesis. This web-based survey was conducted in the Okanagan region of British Columbia, Canada. The survey was conducted from November 2020 to January 2021. Information collected was for four different periods: pre-pandemic (i.e., before March 2020), early stages of pandemic (i.e., during March 2020), later stages of pandemic (i.e., during the survey which was November 2020 to January 2021), and post-pandemic (no travel restrictions/social distancing measures). The post-pandemic section assumes a futuristic scenario where all restrictions related to the COVID-19 pandemic are lifted. Respondents were asked to provide feedback on the activity engagement frequency utilizing options such as every day, a few times a week, a few times a month, a few times a year, or never. In the case of later stage of pandemic, respondents were asked to report their teleactivity engagement frequency in the past 7 days when the survey was taken. To capture these patterns objectively for the last 7-day time period, different options were utilized varying from one day to five or more days, or not at all. The information collected is related to activity-travel behaviour and experiences (Figure 3.3). This includes themes such as frequency of activity engagement (in-home and out-of-home), activity engagement related experiences, and sociodemographic characteristics. More specifically, teleactivities considered were online shopping for grocery and medical supplies, online order of food from a restaurant, and work from home, among others. In addition to work and home location, socioeconomic and demographic information such as age, gender, education, employment status, occupation, driver’s license, and transit pass ownership, as well as household-level attributes which include annual income, dwelling type, and vehicle ownership were captured through this survey. 40 Figure 3.3 Pre and Post COVID-19 Travel Survey Following data collection and cleaning to reasonably represent the Okanagan population, a validation exercise is performed by comparing the survey data with the 2016 Canadian census. More specifically, the survey sample was weighted using the iterative proportional fitting technique (IPF) (Lomax and Norman, 2016). The final sample consists of information from 226 individuals. The validation results reveal that the majority of the sample characteristics lie within a few percentage points from the census data (Table 3.2) and represent the Okanagan population to some extent. For example, females depict a share of 51.5% and 49.1% in census and survey data, respectively. In case of age, individuals aged 18-29 are over-represented by 4%. However, the data under-represents older population aged 75 years and above. Furthermore, individuals in management-related occupations depict share of 11.3% and 10.7% in census and survey data, respectively, which shows under-representation by 0.6% only. In terms of household-level attributes, household size of 5 or more is over-represented by 0.3% only. Lastly, share of individuals with an annual household income of $50,000 - $99,999 are under-represented by 1.9%, 41 whereas, individuals with an annual household income of $200,000 and above are over-represented by 3.3%. Table 3.2. Comparison of survey data with Census data Attributes Census (in %) Survey (in %) Gender Female 51.5 49.1 Male 48.5 50.9 Age 18 to 29 years 12.6 16.6 30 to 34 years 7.2 8.5 35 to 39 years 6.7 12.1 40 to 44 years 6.7 10.3 45 to 49 years 7.3 5.4 50 to 54 years 9.5 15.7 55 to 59 years 10.9 11.2 60 to 64 years 10.4 8.5 65 to 69 years 9.6 6.7 70 to 74 years 6.9 4.0 75 years and above 12.2 1.0 Annual household income $0 – $49,999 34.4 32.3 $50,000-$99,999 34.2 32.3 $100,000-$149,999 18.3 15.0 $150,000-$199,999 7.7 11.5 $200,00 and above 5.5 8.8 Household size 1 person 26.5 24.3 2 people 43.2 38.9 3 people 13.2 15.0 4 people 11.0 15.5 5 or more people 5.9 6.2 Occupation Management occupations (e.g. Business, finance, and administration occupations) 11.3 10.7 Natural and applied sciences and related occupations 5.4 6.2 Health occupations 8.5 6.7 Education, law and social, community and government services 8.9 10.2 Art, culture, recreation and sport related occupation 2.5 3.1 Sales, and service occupations 25.2 26.7 42 Attributes Census (in %) Survey (in %) Trades, transport and equipment operators and related occupations 17.7 14.2 Natural resources, agriculture and related production occupations 3.1 3.6 Occupations in manufacturing and utilities 3.2 3.6 Other 14.3 15.1 3.3. Secondary Data This thesis also utilizes data from supplementary data sources. These data sources include 2016 Canadian Census, Open data portal of Okanagan municipalities, BC Transit open data portal, and Desktop Mapping Technologies Inc. (DMTI) database. These sources were used to extract information on built environment measures such as transportation network, land use attributes, proximity measures, and neighbourhood characteristics. Transportation infrastructure attributes include the length of sidewalks and cycle lanes. Land use characteristics include the percentage of residential, commercial, and industrial land uses, among others. Furthermore, access to activity location measures representing road network-based distance to different activity locations, and transportation services such as the distance from home to the urban centre, closest bus stop, and other activity points were generated using ArcGIS. Finally, neighbourhood characteristics such as the percentage of single-detached dwellings, percentage of owned dwellings, and population density were generated at the dissemination area level of the respondents’ home location. 3.4. Independent Variables Generated While developing the behavioural models, data from 2018 OTS and Pre and Post COVID-19 Travel Survey was utilized. These models accommodate the influence of sociodemographic attributes at an individual and household level, vehicle ownership characteristics, accessibility 43 measures, land use and built environment measures, and neighborhood characteristics, among others on different activity-travel decisions. In terms of sociodemographic attributes, information such as age, gender, employment status, occupation status, household size, annual household income, driving license ownership, transit pass ownership, dwelling type, and tenure type, among others were extracted from the primary data sources. Furthermore, in terms of vehicle ownership attributes, information such as the number of vehicles in a household, number of bikes in a household, type of vehicle (e.g., SUV, Truck), and fuel type, among others were generated from the primary data sources. Accessibility measures include distance from respondent’s residential location to workplace, nearest urban centre, transit stop, and point of interest (e.g., grocery store, health service, shopping store, restaurants), among others. To generate these measures, road network-based distance was calculated using network analysis tool of ArcGIS (v.10.6.1), and secondary data sources were utilized to generate additional information. Similarly, land use and built environment attributes were generated using respondent’s residential location using ArcGIS (v.10.6.1). Built environment attributes include transportation infrastructure such as length of sidewalk and bike lane in the neighbourhood. On the other hand, land use attributes include a share of different land use types (e.g., residential, commercial, industrial etc.) in neighbourhood. Furthermore, this information was used to derive land use mix index, following the method proposed by Bhat and Gossen (Bhat and Gossen, 2004). Land use mix diversity index ranges from 0 to 1, where 0 represents a perfectly homogeneous land use; and 1 a perfectly heterogeneous land use. Equation 3.1 is used to calculate the land-use mix index. 𝐿𝑎𝑛𝑑 𝑢𝑠𝑒 𝑚𝑖𝑥 𝑖𝑛𝑑𝑒𝑥 = 1 − {|𝑟𝑇−13|+|𝑐𝑇−13|+|𝑚𝑇−13|43} (3.1) 44 Where T is the total land area, r is the residential land area, c is the commercial/industrial land area, and m is the miscellaneous land area. Finally, neighborhood characteristics such as the percentage of single-detached dwellings, percentage of owned dwellings, and population density were generated at the dissemination area level of the respondents’ home location. 45 CHAPTER 4: MODELLING ACTIVITY GENERATION DECISIONS 4.1. Introduction This chapter aims to examine individuals’ activity generation decisions such as activity engagement and duration decisions at a disaggregated episode-level (i.e., considering multiple occurrences of each activity type in a day), along with the choice of activity destination (i.e., the destination location choice). Activity engagement is modeled as a discrete choice decision while the duration of the activities is modeled as a continuous decision. Methodologically, a multiple discrete-continuous (MDC) choice modelling framework has been adopted to accommodate the discrete and continuous nature of the decisions, as well as the 24-hour time constraint while generating daily activities and their duration. This study has further extended the existing MDC model to a modified version of multiple discrete-continuous extreme value with ordered preferences (MDCEV-OP) modelling framework which deals with activity generation decisions at a disaggregated episode-level. An ordering of activity episodes is introduced within the formulation of this modified MDCEV model to ensure logical consistency that a higher numbered episode does not occur without the occurrence of lower-numbered episodes (i.e., episode 3 is not predicted without predicting episodes 1 and 2 of an activity). Furthermore, travel decisions, like destination choice, are modelled using an MNL technique. To investigate the interdependency among activity generation decisions and destination choice that varies by each episode, a logsum- based destination accessibility has been derived from the destination choice utility functions of the MNL model and a coupling mechanism has been utilized to provide feedback from the destination Chapter 4 is derived from: Khaddar, S., Saxena, S., Fatmi, M. R., & Pinjari, A.R. (2022) An Episode-Level Joint Model for Activity Engagement, Duration, and Destination Location Choice Decisions, Transportation Letters, 15:8, 860 870, DOI: 10.1080/19427867.2022.2102347 46 choice decisions to the activity generation decisions. The model extensively tests the effect of sociodemographics, vehicle ownership, land use, and built environment characteristics on individuals’ daily activity generation and destination choices using data from the 2018 OTS. 4.2. Methodology The destination choice model is based on the principle of indirect utility maximization, specifically utilizing a conditional multinomial logit model. Denote 𝑈𝑖𝑙 as the utility of location l chosen by individual i which can be expressed as: 𝑈𝑖𝑙 = 𝜗′𝑊𝑖𝑙 + 𝜀𝑖𝑙 (4.1) Here, 𝑊𝑖𝑙 denotes a vector of the attributes of the location l for individual i, and 𝜗′ is a vector of the corresponding coefficients of the parameters to be estimated, and 𝜀𝑖𝑙 is the random error term. The spatial unit considered in this study for destination choice is the dissemination area (DA) level. In order to evaluate the effects of destination choice utility on individuals’ activity engagement and duration decision, logsum values are calculated for individual i participating in each activity-episode using the following equation: 𝐿𝐿𝑖𝑘,𝑗𝑘 = 𝑙𝑜𝑔 [∑ 𝑒𝑥𝑝(𝜗′𝑊𝑖𝑙𝑘,𝑗𝑘)𝑙∈𝐶𝑖𝑘,𝑗𝑘] (4.2) where, 𝐿𝐿𝑖𝑘,𝑗𝑘 represents the logsum value for the ith individual’s destination location choice for 𝑗𝑘𝑡ℎ episode of the kth activity, and 𝐶𝑖𝑘,𝑗𝑘 is the set of destination locations considered for that activity episode. Furthermore, MDCEV-OP framework is utilized to investigate activity engagement at the disaggregate episode-level (discrete choice) and corresponding duration (continuous choice). The lower-tier travel decisions from the destination choice provide feedback to such an episode-level 47 activity engagement and duration (i.e., the MDCEV-OP) model through destination accessibility measures generated in the form of logsum as mentioned in Equation 4.2. Further, to incorporate this feedback a two-stage (sequential) estimation procedure has been adopted in this study (Figure 4.1). Figure 4.1. Conceptual Framework The proposed model assumes that individuals allocate time to different episodes of different activities to maximize the utility they derive from their time allocation subject to a time budget constraint of 1440 minutes (total time available in a day). An additively separable non-linear utility, specifically, a linear expenditure system is employed in this study as mentioned below (Bhat, 2008; Saxena et al., 2020): 𝑈(𝑡𝑖) = 𝜓𝑖1𝑙𝑛(𝑡𝑖1) + ∑ ∑ 𝜓𝑖𝑘,𝑗𝑘𝛾𝑖𝑘𝑙𝑛 (𝑡𝑖𝑘,𝑗𝑘𝛾𝑖𝑘+ 1)𝐽𝑘𝑗𝑘=1𝐾𝑘=2 (4.3) where 𝑈(𝑡𝑖) is the total utility acquired by individual i from allocating time to each of the 𝑗𝑘 episodes of the K activities. 𝑡𝑖 is a vector of time allocations 𝑡𝑖𝑘,𝑗𝑘∀𝑗𝑘 ∈ 𝐽𝑘 and 𝑘 ∈ 𝐾, where 𝑡𝑖𝑘,𝑗𝑘 is the time allocation by individual i to 𝑗𝑘𝑡ℎ episode of the kth activity. The above equation is 48 a valid utility function if 𝜓𝑖1 > 0, 𝜓𝑖𝑘,𝑗𝑘 > 0, and 𝛾𝑖𝑘 > 0 ∀ 𝑘 ∈ 𝐾. In Equation (4.3), 𝜓𝑖1 is the baseline marginal utility of the time allocation to the outside good for which the allocation is analyzed at an aggregate level. 𝜓𝑖𝑘,𝑗𝑘 is the baseline marginal utility of allocating time to 𝑗𝑘𝑡ℎ episode of the kth activity influencing both the discrete decision of whether to allocate time to the corresponding episode and the amount of time allotted to the episode. Here, 𝛾𝑖𝑘 is the satiation parameter for any episode of activity k controlling time allocation by an individual to the episode of that activity. Note that 𝛾𝑖𝑘 (satiation parameter) is assumed to be the same across different episodes of an activity type k as it enables to maintain analytic tractability of the model and its estimation. This assumption, while advantageous from the standpoint of model tractability, does not impose any restriction on time allocation pattern because baseline preferences are different across episodes of an activity. To capture heterogeneity in individuals’ preference for different activity-episodes, the baseline parameter utility and satiation parameter utility can be parameterized as: 𝜓𝑖𝑘,𝑗𝑘 = 𝑒𝑥𝑝(𝛽′𝑍𝑖𝑘𝑗𝑘 + 𝜃𝐿𝐿𝑖𝑘,𝑗𝑘 + 𝜀𝑖𝑘𝑗𝑘) (4.4) 𝛾𝑖𝑘 = 𝑒𝑥𝑝(𝜌′𝑌𝑖𝑘) (4.5) where, 𝑍𝑖𝑘𝑗𝑘 and 𝑌𝑖𝑘 are the vectors of the exogenous variables, and 𝛽′ and 𝜌′ are the vectors of corresponding estimable coefficients. 𝜀𝑖𝑘𝑗𝑘 is a random error term recognizing unobserved factors that influence individuals’ preferences. In Equation (4.4), 𝐿𝐿𝑖𝑘,𝑗𝑘 represents the logsum value for each individual’s trip destination choice for 𝑗𝑘𝑡ℎ episode of the kth activity obtained from Equation (4.2), and 𝜃 is the corresponding coefficient. It must be noted that positive values of logsum coefficients are desirable since an increase in the accessibility offered by the destination choice options will lead to an increase in the corresponding activity-episode level engagement propensity 49 and vice versa. In this framework, activity time consumption is modeled at an episode level and, thereby, implicitly modelling the frequency of occurrence of each activity too. This modelling approach should ensure that a higher-numbered episode of an activity should not be predicted without the occurrence of all the lower numbered episodes (e.g., 3rd episode of shopping activity cannot be predicted without the occurrence of 1st and 2nd one). To address this issue, the MDCEV-OP model incorporates a non-increasing baseline preference across episodes of an activity, while defining episodes in the non-decreasing order of their observed duration: 𝜓𝑘,𝑗𝑘 ≥ 𝜓𝑘,(𝑗𝑘+1)∀ 𝑘 ∈ {2, … . 𝐾}, 𝑗𝑘 ∈ {1,2,3 … . 𝐽𝑘 − 1} (4.6) An implication of ensuring such a condition is that the distribution of baseline marginal utility parameters influencing the occurrence of each episode of an activity k is right-truncated by that of the previous episode. Doing so ensures that a higher numbered episode of activity is never chosen without all the lower numbered episodes of that activity. While it is difficult to specify such truncations using the assumed stochastic distributions, to ensure the conditions in Eq (4.6), the likelihood function of the traditional MDCEV model (that is defined at an episode level) is conditioned on the set of conditions in Equation (4.6). The resulting conditional likelihood function is derived to be a closed-form expression as mentioned below (for more details of the derivation of the likelihood function, refer to (Saxena et al., 2020)): ℒ{𝑡1∗, … , (𝑡𝑚,1∗ , 𝑡𝑚,2∗ , … 𝑡𝑚,𝐼𝑚∗ ), . . , (𝑡𝑀,1∗ , 𝑡𝑀,2∗ , … 𝑡𝑀,𝐼𝑀∗ ), 0, . .0} = |𝐽|𝜇𝛿−1(𝛿 − 1)! ∗∏ ∏ exp (𝜇𝑉𝑚,𝑖𝑚)𝐼𝑚𝑖𝑚=1𝑀𝑚=1∏ ∏ (𝑒𝜇?̅?𝑘,𝑗𝑘∑ 𝑒𝜇?̅?𝑘,𝑠𝐽𝑘𝑠=𝑗𝑘)∗(∑ ∑ exp (𝜇𝑉𝑘,𝑗𝑘𝐽𝑘𝑗𝑘=1𝐾𝑘=1 ))𝛿𝐼𝑘𝑗𝑘=1𝑀𝑘=1 (4.7) 50 Where 𝑡1∗ and 𝑡𝑚,𝐼𝑚∗ represents the time alloted to the outisde good and each episode of an activity respectively. 𝑚 and 𝑀 is the index for the chosen activity and set of activity respectively. 𝐼𝑚 is the number of episodes of a activity m, and 𝑖𝑚 is the episode number for that activity. Furthermore, δ denotes the total number of chosen episodes across all activities. 𝑉𝑘,𝑗𝑘 = 𝛽𝑘,𝑗𝑘′ 𝑧𝑘,𝑗𝑘 −𝑙𝑛 (𝑡𝑘,𝑗𝑘∗𝛾𝑘+ 1) and |𝐽| is the determinant of the Jacobian matrix, which is of a similar structure as derived by Bhat et al. (Bhat, 2005). The final likelihood equation is very similar to traditional disaggregated MDCEV models, scaled by the rank-ordered logit terms of the baseline marginal utilities of the corresponding episodes being in the decreasing order for all chosen activities. For more details of the likelihood derivation refer to Saxena et al. (Saxena et al., 2022). 4.3. Descriptive Statistics The 2018 OTS was used to generate disaggregated episode-level activity time allocation information of a) in-home (IH), and b) out-of-home activities (OH). The OH activities were further categorized into the following eight activity types: escorting (pick-up/drop-off activities), personal business (bank, doctor, errands, etc.), recreation (gym, swimming, etc.), eat out (dine-in), social (visiting friends/relatives, social/civic event, etc.), shopping (buying grocery, goods, etc.), education, and work. After thorough data processing, the final sample includes the daily activity and travel information of 7332 individuals. The maximum number of episodes considered for each activity type was five. A descriptive analysis of the data is in Table 4.1. In the case of activity engagement rate and duration, descriptive analysis is performed for both activity-level and episode-level activities. It may be noted that 100% participation is observed in in-home activity. The average time consumption is calculated considering only those individuals who reported participation in that activity type. In terms of activity duration, it is evident that escorting has the 51 lowest share of activity duration at both episode and activity levels. In addition, individuals spent approximately 8 hours per day in work and 6 hours per day in educational activities. 44.43% of individuals participated in work activities. However, only 3.17% of individuals participated in an educational activity. Interestingly, 6.82 % and 10.73% of individuals engage in the second episode of work and shopping activities, respectively. Moreover, as expected less than 1% of individuals are engaging in more than one episode of eat out or recreational activities in a day. Table 4.1. Descriptive statistics of the OTS Sample by activity engagement and time consumption Activity Purpose Activity level participation Episode level participation (% of individuals participating in an episode) Average time allocation (minutes) % of individuals participating in an activity 1 or more 2 or more 3 or more 4 or more 5 or more Episode level Activity level Escorting 15.35 15.35 7.90 1.67 0.63 NA 11.3 28.3 Personal Business 25.21 25.21 6.32 1.86 0.44 NA 34.4 88.4 Recreation 15.56 15.56 1.23 NA NA NA 88.6 111.2 Eat Out 12.43 12.43 0.97 NA NA NA 46.3 62.6 Social 16.74 16.74 2.39 0.34 NA NA 95.1 172.9 Shopping 34.90 34.90 10.73 3.48 1.20 0.38 23.4 52.9 Education 3.17 3.17 NA NA NA NA 372.6 372.6 Work 44.43 44.43 6.82 1.85 0.49 NA 160.0 453.2 Note: NA- Not applicable 4.4. Independent Variables Generated The model tests the influence of sociodemographic attributes (e.g., age, gender, annual household income, employment status, household size), vehicle ownership attributes (e.g., number of vehicles, number of bikes), land use attributes (e.g., land use mix index, share of residential, commercial, industrial, and other areas), accessibility measures (e.g., distances from home to the 52 nearest urban center, bus stop, point of interest), and built environment attributes (e.g., length of bike lane and sidewalk), among others. The built environment and land-use attributes were generated for 250 metre buffer area based on the respondent’s home location, whereas accessibility measures were generated based on the road network-based distance from their home location using ArcGIS (v.10.6.1). Whereas, these attributes were generated at a dissemination areas (DA) level for the destination choice component. The summary statistics of the variables retained in the final model are presented in Table 4.2. Table 4.2. Descriptive statistics of the variables retained in the final model Variables Definition Mean/ Distribution SD Destination Choice Auto travel time in minutes Auto travel time between origin and destination DA 18.40 13.94 Length of active transport infrastructure available in KM Available length of sidewalk and bike lane at the destination DA 6.51 7.13 Land use mix index > 0.5 Dummy, if land use mix index is more than 0.5 at the destination DA =1, 0otherwise 38.40% - Higher percentage of residential area Dummy, if share of residential area is more than 50% at the destination DA =1, 0otherwise 41.59% - Higher percentage of commercial area Dummy, if share of commercial area is more than 50% at the destination DA =1, 0otherwise 3.94% - Distance to nearest urban core < 2KM Dummy, if distance to the nearest urban core is less than 2 km from the centroid of destination DA = 1, 0 otherwise 21.44% - Distance to nearest recreational center < 2KM Dummy, if distance to the nearest urban core is less than 2 km from the centroid of destination DA= 1, 0 otherwise 80.91% - Density of restaurants Density of restaurants in the destination DA 5.87 17.97 Density of shopping center Density of shopping center in the destination DA 2.85 8.23 Activity Engagement and Duration Gender: Male Dummy, if an individual is male =1, 0 otherwise 47.56% - Age below 35 Dummy, if an individual is 35 years old or less =1, 0 otherwise 18.03% - 53 Variables Definition Mean/ Distribution SD Age above 65 Dummy, if an individual is 65 years old or more =1, 0 otherwise 31.04% - Annual household income less than $50,000 Dummy, if an individual’s annual household income is less than $50,000 =1, 0 otherwise 19.91% - Annual household income more than $80,000 Dummy, if an individual’s annual household income is more than $80,000 =1, 0 otherwise 42.81% - Full-time worker Dummy, if an individual is a full-time worker=1, 0 otherwise 44.03% - Households with more than 3 members Dummy, if an individual resides in a household with more than 3 members=1, 0 otherwise 19.84% - Household with children more than 2 Dummy, if an individual resides in a household with more than 2 children =1, 0 otherwise 31.18% - Household vehicle ownership more than 2 Dummy, if an individual resides in a household with more than 2 vehicles =1, 0 otherwise 25.04% - Ownership of driving license Dummy, if an individual owns a driving license=1, 0 otherwise 96.37% - Distance from home to nearest urban core less than 2KM Dummy, if distance to the nearest urban core is less than 2 km from residential location of an individual = 1, 0 otherwise 24.51% - Distance from home to nearest bus stop less than 0.5KM Dummy, if distance to the nearest bus stop is less than 0.5 km from residential location of an individual = 1, 0 otherwise 65.06% - Note: SD – standard deviation 4.5. Model Results This section is further categorized into the following sub-sections. First, the goodness-of-fit measures of the proposed model are presented. Further, the parameter estimation results are discussed for the destination choice model. Lastly, parameter estimation results are discussed for the activity generation decisions. 54 4.5.1. Goodness-of-fit Measures A comparison was made between the two empirical models – (a) the proposed model that considers feedback from the destination choice model through logsum parameters (destination accessibility) within the MDCEV-OP activity episode generation and time allocation model and (2) another model that does not incorporate this feedback in the MDCEV-OP component through goodness-of-fit measures. Results suggest that model which considers destination choice logsum parameters within MDCEV-OP model (Log-likelihood at convergence: -106072.04; AIC: 212342.76; BIC: 213016.28; Adjusted R2: 0.124) provides a superior statistical fit than the independent model (Log-likelihood at convergence: -116575.86; AIC: 233342.18; BIC: 233988.32; Adjusted R2: 0.037). Overall, the assessment measures indicate that including logsum parameters in the MDCEV-OP model helps in incorporating the influence of destination accessibility on activity generation and time use and thereby improves the overall explanatory potential of the model. 4.5.2. Destination Choice Results The model estimation results and goodness-of-fit measures are reported in Table 4.3. Results suggest that individuals’ destination choice is significantly influenced by destination characteristics such as level of service measures, land use, access to activity location measures, transportation infrastructure attributes, and neighborhood characteristics. Results reveal that destinations with longer travel times are less likely to be chosen for an activity purpose, which is expected (Auld and Mohammadian, 2011; Hassan et al., 2019). In addition, among the land use attributes, land use mix index and percentage of the commercial area of the dissemination area (DA) are found to have a significantly positive effect on individuals’ choice of destination. This 55 result reaffirms that areas with higher land use mix are prevalent with urban characteristics offering a multitude of activity opportunities related to non-mandatory (e.g., escorting, personal business, eat out, recreation, social, shopping) activities. In the case of neighborhood characteristics, the percentage of the residential area reveals a negative relationship, which suggests that primarily residential DAs are less likely to offer the opportunity to engage in different activities. Among the transport infrastructure attributes, DAs with active transportation infrastructure such as sidewalk and bicycle lanes are more likely to be preferred as destination locations. Table 4.3. Parameter estimation results: Destination Choice Out of home activities Variables Escorting Activity Personal Business Activity Recreational Activity Eat Out Activity Social Activity Shopping Activity Auto travel time in minutes -0.138 (-29.16) -0.108 (-30.29) -0.113 (-20.70) -0.131 (-19.39) -0.099 (-23.38) -0.096 (-34.49) Length of active transport infrastructure available in KM 0.081 (24.45) 0.073 (24.76) 0.077 (17.37) 0.076 (16.67) 0.061 (16.62) 0.085 (36.66) Land use mix index > 0.5 0.221 (3.50) 0.526 (9.66) 0.265 (3.27) -0.682 (-6.36) 0.228 (3.32) 0.354 (7.28) Higher percentage of residential area -0.571 (-7.35) -0.643 (-9.24) -1.399 (-12.49) 0.735 (5.41) -0.691 (-8.05) -0.572 (-9.02) Higher percentage of commercial area 0.629 (5.98) 1.094 (12.93) 0.509 (3.85) 0.994 (8.86) 1.109 (15.70) Distance to nearest urban core < 2KM 0.310 (4.46) 0.568 (9.89) Distance to nearest recreational center < 2KM 1.146 (8.92) Density of restaurants 0.024 (14.34) Density of shopping center 0.046 (24.74) Note: Log-likelihood at convergence: -17828.20; Akaike information criterion (AIC): 35724.60; Bayesian information criterion (BIC): 35975.31 56 Among access to activity location measures, individuals are more likely to prefer destinations with proximity to the urban core for activities like escorting and personal business. As expected, areas closer to recreational centers attract individuals for recreational activity. Moreover, DAs with a higher density of activity locations such as restaurants and shopping centers are more likely to increase individuals’ preference for destinations to perform eat out and shopping activities. 4.5.3. Activity Engagement and Duration Choice Results: Baseline Preference Estimates While estimating the model baseline preference, constants are also estimated for each episode of activity except for in-home activity. The constants are specified such that the parameter estimate corresponding to the first episode is interpreted as the ‘activity level’ constant (because it enters the baseline utility of all episodes of that activity). The ‘activity level’ constant can be interpreted as the aggregate influence of unobserved factors on the likelihood of taking part in one or more episodes of activity. Whereas episode-specific constants (subsequent parameter estimates) capture the differential effect for that episode with respect to the ‘activity level’ constant. For instance, the baseline preference constant for the second episode of “recreation” activity is -9.094 (as it is the sum of the corresponding activity level constant and 2nd episode-specific constant i.e., -8.083 + (-1.011) = -9.094). Note that, in the MDCEV-OP framework, the baseline utility parameter across different episodes of activity are in decreasing order. Therefore, baseline preferences are always in decreasing order across the different episodes of an activity, even if the episode-specific constants are zero or positive, precluding the need to estimate each episode-specific constant. Because of this property, episode-specific constants estimated in the model turned out to be insignificant and were dropped from the final model. Another reason for insignificant episode-specific parameters is the episode level engagement rates, as they are observed in decreasing order 57 in the data (i.e., the survey data reflects that 3rd episode of activity did not take place without the occurrence of the 1st and 2nd episode of the same activity). Moreover, the percentage of individuals participating in higher number of activity episodes are quite low in the data (Table 4.1). In any case, the constants do not offer a substantive interpretation and, therefore, the signs on the constants should not be used to make behavioural interpretations (for more details refer to (Saxena et al., 2020)). One must note that the scale parameter estimate for the model is 0.280 with a t-stat of 186.53, thus implying that the scale is significantly different from 1. The next subsection highlights the effect of different attributes on activity engagement. Among sociodemographic characteristics, model results suggest that males are less likely to participate in escorting and shopping activities as compared to females. This finding indicates that females take higher responsibility for household-related activities (Astroza et al., 2018). However, males depicted a higher tendency to participate in educational activities. Among the age groups considered in this study, younger individuals (i.e., age below 35) showed a higher likelihood towards work and educational activity, as they are more likely to be students and/or young professionals. However, they have a lower tendency to engage in eat out and shopping activities. On the other hand, older individuals (i.e. age above 65) showed a lower propensity towards both mandatory (i.e. work and education) and non-mandatory (i.e. escorting, personal business, social, and shopping) activities. This finding might indicate mobility challenges experienced by older adults (Ravulaparthy et al., 2016). Individuals from lower-income households showed a lower likelihood towards recreational and eat out activity and are more likely to take part in activities like personal business and education. This finding might manifest constraints related to their financial affordability. However, individuals residing in higher-income households exhibit a higher propensity to engage in out-of-home activities like personal business, recreation, eat out, 58 and shopping, which might be attributed to their higher expenditure capabilities. Moreover, they depict a higher level of engagement in work activities. Full-time workers are more likely to engage in work activity instead of educational activity as depicted in the results. In addition, results reveal that full-time workers engage in escorting, recreational, eat out, social, and shopping activities, which are important for work-life balance and are mostly performed during their commute such as escorting children to childcare, and/or during the evening such as eat out or socialize with friends/relatives/colleagues after the work. In terms of household-level characteristics, higher household size depicts a positive relationship with escorting activity, implying the need to pick-up/drop-off different individuals in the household. Also, the episode-specific parameter for the 2nd episode suggests that households with more members lead to engagement in higher escorting activity episodes. In addition, a higher propensity towards recreational activity is observed. However, higher household size showed a negative relationship with eat out activity, as members are more likely to eat at home together and socialize among themselves (Auld et al., 2011). In the presence of a higher number of children in a household, the model results reveal positive effects on the engagement of escorting activity, along with the occurrence of a higher number of episodes. Results suggest that presence of more children in a household increases the likelihood to engage in higher episodes of escorting activity, as a positive parameter is estimated for the 2nd episode of escorting activity. However, the activity level parameter estimated for escorting activity is insignificant, which does not imply that the presence of more children does not affect escorting activity. Due to the ordering imposed in the model which ensures that 2nd episode of escorting activity cannot be predicted without the prediction of the 1st episode. Because of this property, one can infer that a higher number of children increases the probability to engage in a greater number of escorting episodes, which 59 conditions engagement in 1st episode as well, hence increasing the propensity towards the first episode too. Households with a higher number of children are more likely to participate in work and educational activities. However, these households are less inclined towards eat out activity indicating the majority of the time allocation to childcare responsibilities (Bernardo et al., 2015). In addition, among mobility tool ownership, a household with a higher number of vehicles showed a lower likelihood of escorting activities. This finding infers that an increase in mobility gained through higher vehicle ownership and a consequent reduction in dependency among household members for travel may lead to lower pick-up/drop-offs. Intuitively, the presence of higher vehicle ownership might increase the likelihood to engage in multiple activities, as indicated through the positive relationship for eat out, social, and educational activity. Moreover, results reveal that with higher vehicle ownership there is a higher probability to engage in multiple episodes of work activity, as it provides more flexible mobility options. As expected, individuals with a driving license have a greater propensity to engage in out-of-home activities such as escorting, recreation, eat out, and shopping indicating the importance of mobility tool ownership. Lastly, this study also considers the effects of accessibility measures on activity engagement. For instance, individuals living in the urban core tend to engage more in out-of-home activities such as recreation, social, shopping, education, and work. This finding reaffirms an understanding that higher commercial land use and destinations in the urban core might offer the opportunity to engage in more out-of-home activities. Individuals living closer to bus stops are likely to engage in activities such as work. This finding reflects that better public transit accessibility increases mobility by making activity destinations more accessible (Fan and Khattak, 2009). However, the results were not the same for non-mandatory activities such as recreation and social. These 60 activities are usually performed with a companion and individuals might choose to use a private vehicle for travel in such cases. 4.5.4. Activity Engagement and Duration Choice Results: Logsum Parameter Estimates An interesting contribution of this study is incorporating a logsum variable from the destination choice model, a stronger measure of travel choices such as destination location (destination accessibility), into the baseline marginal utility of non-mandatory activities to reflect the influence of destination accessibility on episode-level activity generation and time allocation. The coefficient of the logsum variables is found to be positive and statistically significant. These results imply that activity engagement and duration choices are sensitive to destination location choices (i.e., destination accessibility), and such destination accessibility metrics help in capturing the influence of level-of-service, land use, and built environment measures on activity generation and time allocation. This indicates that as travel time to arrive at the activity location reduces and the presence of built environment characteristics such as active transportation infrastructure, mixed land use, commercial areas, activity points, and proximity to it increases, the likelihood of engaging in and allocating time to these activities also increases. 4.5.5. Activity Engagement and Duration Choice Results: Satiation parameters Estimates Table 4.4 represents the satiation parameter estimates. As mentioned earlier in the methodology section, satiation parameters are the same across all the episodes of activity. Model results suggest that males are likely to spend longer durations in escorting and recreational activities, which is consistent with the findings reported in previous literature (Palma et al., 2021). However, males spend shorter durations in personal business and shopping activities, which further indicates 61 differences in role/responsibility between men and women. Older individuals (age above 65) tend to spend longer durations in eat out and shopping activities, as anticipated. In addition, as expected, full-time workers are more likely to spend longer duration in work activity; however, they are likely to spend a shorter duration in other out-of-home activities like escorting, personal business, eat out, and social. Lastly, individuals with more children in the household are more likely to allocate longer time duration to work activity. This is perhaps because of a greater need for financial resources and stability to support children in their personal and professional growth. Also, such individuals are more likely to allocate more time to personal business and shopping activities which might be largely related to childcare needs and might require multiple episodes in a day. Interestingly, they are found to be involved in a shorter duration of escorting activities. As individuals with multiple children might need to make pick-up/drop-off activities for different children at different locations such as childcare centers and schools, such episodes are likely to be of shorter duration. Table 4.4. Parameter estimation results: Baseline preference parameters, Logsum parameters, Satiation parameters Activity-level 2nd episode 3rd episode 4th episode 5th episode Activity-level 2nd episode 3rd episode 4th episode 5th episode Coefficient (t-stat) Baseline preference constants Gender: Male Escorting -8.399 (-125.52) - - - -0.087 (-5.96) - - - Personal Business -8.127 (-355.63) - - - - - - - Recreation -8.083 (-94.97) -1.011 (-29.74) - - Eat Out -8.007 (-112.62) -0.756 (-20.56) - - Social -8.128 (-298.02) - - - - - Shopping -8.027 (-178.17) - - - - -0.030 (-2.50) - - - - 62 Activity-level 2nd episode 3rd episode 4th episode 5th episode Activity-level 2nd episode 3rd episode 4th episode 5th episode Education -8.504 (-111.13) 0.192 (4.54) Work -7.898 (-422.40) - - - - - - - Age below 35 Age above 65 Escorting - - - - -0.124 (-5.63) - - - Personal Business - - - - -0.054 (-3.41) - - - Recreation - - - - Eat Out -0.025 (-1.00) - - - Social - - -0.050 (-2.35) - - Shopping -0.046 (-1.93) - - - - -0.037 (-2.53) - - - - Education 0.968 (13.62) -0.812 (-3.84) Work 0.020 (1.34) - - - -0.360 (-16.94) - - - Annual household income less than $50,000 Annual household income more than $80,000 Escorting - - - - Personal Business 0.027 (1.48) - - - 0.081 (4.56) - - - Recreation -0.033 (-1.24) - 0.075 (3.67) - Eat Out -0.105 (-4.27) - 0.036 (1.56) - Social - - - - - - Shopping - - - - 0.018 (1.28) - - - - Education 0.118 (2.29) - Work 0.044 (3.43) - - - Full-time workers Households with more than 3 members Escorting 0.155 (9.11) - - - 0.102 (4.12) 0.060 (1.08) - - 63 Activity-level 2nd episode 3rd episode 4th episode 5th episode Activity-level 2nd episode 3rd episode 4th episode 5th episode Personal Business - - - - - - - - Recreation 0.103 (4.83) - 0.077 (3.29) - Eat Out 0.170 (6.62) - -0.052 (-1.76) - Social 0.114 (4.71) - - - - - Shopping 0.045 (2.47) - - - - - - - - - Education -1.506 (-5.06) - Work 0.664 (44.95) - - - - - - - Household with children more than 2 Household vehicle ownership more than 2 Escorting - 0.350 (8.61) - - -0.074 (-4.51) - - - Personal Business - - - - - - - - Recreation - - - - Eat Out -0.038 (-1.40) - 0.104 (4.40) Social - - - 0.019 (1.00) - - Shopping - - - - - - - - Education 0.230 (4.17) 0.152 (3.19) Work 0.034 (2.54) - - - - - - 0.104 (3.09) Ownership of driving license Distance from home to nearest urban core less than 2KM Escorting 0.343 (5.36) - - - - - - - Personal Business - - - - - - - - Recreation 0.124 (1.55) - 0.052 (2.23) - Eat Out 0.166 (2.45) - - - 64 Activity-level 2nd episode 3rd episode 4th episode 5th episode Activity-level 2nd episode 3rd episode 4th episode 5th episode Social - - - 0.059 (2.68) - - Shopping 0.072 (1.76) - - - - 0.023 (1.58) - - - - Education - 0.088 (1.79) Work - - - - 0.020 (1.40) - - - Distance from home to nearest bus stop less than 0.5KM Logsum parameters: Destination choice Escorting -0.043 (-2.84) - - - 0.463* (67.29) Personal Business -0.066 (-4.12) - - - 0.387* (54.40) Recreation -0.068 (-3.36) - 0.500* (59.12) Eat Out - - 0.425* (66.23) Social -0.045 (-2.13) - - 0.553* (53.41) Shopping -0.076 (-5.25) - - - - 0.266* (56.17) Education - Work 0.020 (1.46) - - - Parameter estimation results: Satiation parameters Constant Male Age above 65 Full-time worker Households with children more than 2 Escorting 3.601 (66.24) 0.194 (3.83) -0.159 (-2.16) -0.399 (-7.33) -0.231 (-4.24) Personal Business 5.136 (148.94) -0.046 (-1.06) - -0.339 (-6.32) 0.148 (2.59) Recreation 4.889 (94.60) 0.169 (3.29) - - - Eat Out 4.762 (65.16) - 0.205 (2.36) -0.421 (-4.86) - Social 6.246 (135.48) - - - - 65 Constant Male Age above 65 Full-time worker Households with children more than 2 Shopping 4.759 (108.26) -0.077 (-1.70) 0.113 (2.23) -0.121 (-1.88) 0.089 (1.49) Education 8.197 (23.90) - - - - Work 7.731 (84.35) - - 0.338 (3.50) 0.173 (2.51) Note: * Single destination choice logsum is estimated across all episodes of an activity; Blank spaces indicate NA (i.e. Not Applicable) as no activity engagement is observed in the data for that particular episode of an activity; “-“ – Insignificant. 4.6. Concluding Remarks This chapter presents episode-level activity participation and time allocation model using a MDCEV-OP framework. An ordering of activity episodes is introduced within the formulation of this modified MDCEV model to ensure logical consistency that a higher numbered episode does not occur without the occurrence of lower-numbered episodes. The travel decisions, like destination choice, are modeled using a MNL technique. A coupling mechanism is adopted to provide feedback from destination choice decisions (i.e., destination accessibility) using logsum variables to the activity generation decisions, thus capturing its impacts on activity engagement and duration choices. The motivation for adopting a disaggregated episode-level model is to provide finer behavioural representation while modelling travel activity patterns such as the occurrence of multiple episodes of the same activity type and capture interdependencies among other travel choice dimensions that vary by episodes like destination (and possibly other related travel choices such as mode choice) in a consistent framework. The analysis in this study confirms the effects of sociodemographic, mobility tool ownership, and built environment characteristics on episode-level activity generation and destination choice 66 decisions. However, one of the key empirical findings of this study is that the destination location choices (i.e., destination accessibility) which are sensitive to each activity episode, captured through level-of-service, land use, and built environment measures positively impact episode-level activity engagement and duration decisions. Apart from behaviourally intuitive findings, this study confirms that incorporating destination accessibility with the activity choice dimensions at episode level within the same framework offers a statistically superior model as compared to a model that does not consider the influence of destination accessibility on activity generation and time allocation. This suggests that providing additional information not only improves the overall explanatory potential of the model but also enhances behavioural realism. The findings from this study can be further used to generate key policy implications. For instance, results suggest that destination accessibility increases the likelihood to engage in non-mandatory out-of-home activities, indicating that individuals’ activity generation is dependent on transportation and land use policies and investments. For example, higher travel time decreases the probability of engaging in non-mandatory travel activities. Thus, travel demand management policies and investment in road infrastructure are required to improve the level-of-service (LOS). Particularly, investing in alternative transportation infrastructures such as walk and bike facilities could be a way to improve LOS of roads, as it is likely to attract individuals to use alternative travel modes and will free up space on the traffic lane. In a similar line of investigation related to land use attributes, mixed land use areas are likely to attract individuals to engage in various non-mandatory activities. Hence, policies need to target mixed land use developments offering residential and commercial establishments to attract individuals for both living and engaging in activities at close-by destinations. 67 CHAPTER 5: MODELLING ACTIVITY SCHEDULING DECISIONS 5.1. Introduction This chapter aims to investigate activity start time, mode choice, companionship choice, and destination location choice decisions. These choices are often referred to as activity scheduling decisions and are interdependent and complex in nature (Pougala et al., 2022). Additionally, this joint decision may be influenced by a variety of observed and unobserved factor combinations. These unobserved variable accounts for, but is not limited to, factors such as personal habits/preferences/attitudes/traffic conditions etc., that might influence these activity-travel decision dimensions and are often not directly measured in conventional household travel diary surveys. For example, the “willingness” to travel with a companion to perform an activity (e.g., household member, non-household member) can influence the choice of destinations, which again can further dictate activity start time, and travel mode. Therefore, this study jointly models the aforementioned activity-travel decisions in a single unified framework. Methodologically, a joint discrete choice modelling framework is adopted by introducing unobserved variables common to these different activity-travel dimensions. In this regard, the proposed approach can be utilized to disentangle a multitude of interdependencies among the activity-travel choice dimensions. For instance, interdependencies between activity start time and mode-companionship decisions due to common unobserved factors influencing both choices. Similarly, the study explores the correlation between mode-companionship choices and destination location choices. Lastly, interdependency among activity start time and destination location choices are also investigated in the study. _____________________________________________________________________________________________Chapter 5 is derived from: Khaddar, S., Varghese, V., Fatmi, M. R., & Chikaraishi, M. (2023) A Joint Model for Activity Start Time, Travel Mode, Companionship, and Destination Location Decisions. Transportmetrica A: Transport Science (Under review 3rd round) 68 In addition to this, the model extensively tests the effect of factors such as sociodemographic attributes, vehicle ownership characteristics, built environment attributes, and land use measures on the joint choice using the 2018 OTS survey. 5.2. Methodology This section provides a brief description of the methodological approach adopted in this study. Methodologically, a framework is adopted while adding random components to the multinomial discrete choice structure (Paleti et al., 2013; Pinjari et al., 2011). The proposed framework captures interdependencies among activity-travel attributes such as activity start time, mode-companionship, and destination location choices. Where i (i = 1,2,…,I) represents the individual (i.e. decision maker), p (p = 1,2,…,P) represents activity start time alternatives, q (q= 1,2,…,Q) represents mode-companionship alternatives, and r (r= 1,2,…,R) represents the destination location alternatives. The respective utility functions for these choices can be defined as follows (Tran et al., 2016; Varghese et al., 2022): 𝑈𝑖𝑝 = 𝛽𝑝𝑋𝑖𝑝 + ∑ 𝜃𝑖𝑝𝑞𝑞 + ∑ 𝜇𝑖𝑝𝑟𝑟 + 𝜀𝑖𝑝 (5.1) 𝑈𝑖𝑞 = 𝛽𝑞𝑋𝑖𝑞 + ∑ ±𝜃𝑖𝑝𝑞𝑝 + ∑ 𝜑𝑖𝑞𝑟𝑟 + 𝜀𝑖𝑞 (5.2) 𝑈𝑖𝑟 = 𝛽𝑟𝑋𝑖𝑟 + ∑ ±𝜇𝑖𝑝𝑟𝑝 + ∑ ±𝜑𝑖𝑞𝑟𝑞 + 𝜀𝑖𝑟 (5.3) Here, 𝑋𝑖𝑝, 𝑋𝑖𝑞, 𝑋𝑖𝑟 represents the vector of explanatory variables tested in the study which mainly includes sociodemographic, vehicle ownership, built environment, and land use attributes; 𝛽𝑝, 𝛽𝑞, 𝛽𝑟 is the associated vector of parameters; and 𝜀𝑖𝑝, 𝜀𝑖𝑞, 𝜀𝑖𝑟 are the error terms following an identical and independent Gumbel distribution. This study introduces random components capturing the effect of common unobserved variables among different choice dimensions and 69 associated interdependencies (Figure 5.1). More specifically, 𝜃𝑖𝑝𝑞 represents the interdependencies among activity start time (p) and mode-companionship (q) choices, 𝜇𝑖𝑝𝑟 represents the interdependencies among activity start time (p) and destination location (r) choices, and 𝜑𝑖𝑞𝑟 represents the interdependencies among mode-companionship (q) and destination location (r) choices. These random components are normally distributed with mean 0 and variances 𝜎𝑝𝑞2 , 𝜎𝑝𝑟2 , 𝑎𝑛𝑑 𝜎𝑞𝑟2 respectively. Figure 5.1. Interdependencies Among Different Choice Dimensions As per the knowledge of the authors, there exists at least two strategies to take into consideration the correlations across alternatives. The first assumes that the random error components follow a multivariate distribution, and the correlation parameters are estimated from the distribution. This strategy assumes that the error components are dependent on each other. Meanwhile, in the second strategy, common random error components are introduced into two or more alternatives and are assumed to be independent of each other (Varghese et al., 2022). However, in the second strategy, the random error terms will capture the covariance between alternatives. In this study, the latter approach is adopted. Furthermore, ± sign in front of random terms indicates correlation among the 70 unobserved terms may be positive or negative. For example, + (plus) sign for 𝜃𝑖𝑝𝑞 indicates that the unobserved variables that increases the propensity for activity start time choice also increases the propensity for mode-companionship choice, whereas a – (negative) sign for 𝜇𝑖𝑝𝑟 reveals that unobserved variables that increases the propensity for activity start time choice decreases the propensity for associated destination location choice. Assuming that the chosen set of alternatives provides the highest utility, the conditional likelihood function can be written as (Tran et al., 2016): 𝐿(η|𝜃𝑖𝑝𝑞 , 𝜇𝑖𝑝𝑟 , 𝜑𝑖𝑞𝑟) = ∏ 𝑖 ∏ 𝑝 ∏ 𝑞 ∏ 𝑟 {𝑒𝛽𝑝𝑋𝑖𝑝+∑ 𝜃𝑖𝑝𝑞𝑞 +∑ 𝜇𝑖𝑝𝑟𝑟∑ 𝑒𝛽𝑝𝑋𝑖𝑝+∑ 𝜃𝑖𝑝𝑞𝑞 +∑ 𝜇𝑖𝑝𝑟𝑟𝑝×𝑒𝛽𝑞𝑋𝑖𝑞+∑ ±𝜃𝑖𝑝𝑞𝑝 +∑ 𝜑𝑖𝑞𝑟𝑟∑ 𝑒𝛽𝑞𝑋𝑖𝑞+∑ ±𝜃𝑖𝑝𝑞𝑝 +∑ 𝜑𝑖𝑞𝑟𝑟𝑞×𝑒𝛽𝑟𝑋𝑖𝑟+∑ ±𝜇𝑖𝑝𝑟𝑝 +∑ ±𝜑𝑖𝑞𝑟𝑞∑ 𝑒𝛽𝑟𝑋𝑖𝑟+∑ ±𝜇𝑖𝑝𝑟𝑝 +∑ ±𝜑𝑖𝑞𝑟𝑞𝑟}𝑑𝑖𝑝𝑑𝑖𝑞𝑑𝑖𝑟 (5.4) Where, 𝑑𝑖𝑝 is a dummy variable equal to 1 if an individual i chooses activity start time alternative p, otherwise 0, 𝑑𝑖𝑞 is a dummy variable equal to 1 if an individual i chooses mode-companionship alternative q, otherwise 0, and lastly, 𝑑𝑖𝑟 is a dummy variable if individual i chooses destination location alternative r, otherwise 0. The parameters estimated using equation 1, 2, and 3 consists of vector terms (𝛽𝑝, 𝛽𝑞 , 𝛽𝑟), variances (𝜎𝑝𝑞2 , 𝜎𝑝𝑟2 , 𝑎𝑛𝑑 𝜎𝑞𝑟2 ) of the respective random components. Therefore, let α represent the vector including all the parameters to be estimated and η represents all the parameters except variances. Then, the unconditional likelihood function can be represented as (Tran et al., 2016): 𝐿(𝛼) = ∫ ∫ ∫ [𝐿(η|𝜃𝑖𝑝𝑞 , 𝜇𝑖𝑝𝑟 , 𝜑𝑖𝑞𝑟) ×⬚𝜑𝑞𝑟 ⬚𝜇𝑝𝑟⬚𝜃𝑝𝑞∅(𝜃𝑖𝑝𝑞|𝜎𝑝𝑞)∅(𝜇𝑖𝑝𝑟|𝜎𝑝𝑟)∅(𝜑𝑖𝑞𝑟|𝜎𝑞𝑟)]𝑑𝜃𝑖𝑝𝑞𝑑𝜇𝑖𝑝𝑟𝑑𝜑𝑖𝑞𝑟 (5.5) Here, ∅(𝜃𝑖𝑝𝑞|𝜎𝑝𝑞), ∅(𝜇𝑖𝑝𝑟|𝜎𝑝𝑟), ∅(𝜑𝑖𝑞𝑟|𝜎𝑞𝑟) are normally distributed with mean as 0 and variances as 𝜎𝑝𝑞2 , 𝜎𝑝𝑟2 , 𝑎𝑛𝑑 𝜎𝑞𝑟2 , respectively. Therefore, when all the interactions are taken into 71 consideration, the total dimension of the integral would be 47 (5 activity start time alternatives, 3 mode-companionship alternatives, and 4 destination location choice alternatives, i.e., (5x3)+(3x4)+(4x5) = 47). This study only considers 6 random components in the final empirical specification. Although it would be ideal to estimate all dimensions, it is exceedingly challenging to make stable estimates with all 47 random components (Tran et al., 2016; Varghese et al., 2022). Therefore, 6 random components included in the model are between the alternatives: • Mode-companionship: Auto modes shared with household member & Activity start time: Night (PM Off Peak). • Mode-companionship: Auto mode shared with non-household member/alone & Activity start time: PM Peak. • Activity start time: PM Peak & Destination Location: 0 – 1 km. • Activity start time: Night (PM Off Peak) & Destination Location: 5 km and above. • Destination Location: 0 – 1 km & Mode-companionship: Auto modes shared with non-household member/alone. • Destination Location: 5 km and above and Mode-companionship: Auto mode shared with household member. The model estimation was conducted by using Markov Chain Monte Carlo (MCMC) method utilizing WinBUGS software (v1.4.3) (Lunn et al., 2000). A total of 110,000 iterations were performed with the first 10,000 iterations for burn-in to moderate starting effects and remaining 100,000 iterations to generate 10,000 draws (every 10th iteration was retained in the model). 72 5.3. Descriptive Statistics This study utilizes data from the 2018 OTS. Information such as activity start time, mode choice, companionship configuration, and origin-destination information were extracted for each individual performing any travel-activity(s) on the survey day. However, the study focuses on exploring the activity-travel patterns for shopping, personal business, and recreation activities only. Therefore, the final sample retained for further analysis consists of 3949, 2688, and 1299 shopping, personal business, and recreation activities performed, respectively. To generate activity start time choices the 24-hour clock was split into the following exhaustive and mutually exclusive discrete categories: am peak (6:00 am – 9:00 am), am off-peak (9:00 am – 12:00 pm), pm off-peak (12:00 pm – 4:00 pm), pm peak (4:00 pm – 7:00 pm), and night (7:00 pm – 6:00 am). The mode-companionship choices were categorized as: auto modes shared with household members; auto modes shared with non-household members/alone, non-auto modes shared with household members/non-household members/alone. Lastly, to accurately mimic the destination location choices of an individual past literature suggests that accessibility should not only be included in a choice modeling process as a determinant, but also in a choice set derivation (Scott and He, 2012). Therefore, to model destination location choice in a more behaviorally realistic manner an accessibility-based approach is utilized in the study. Keeping this in mind, the current study utilizes road network-based travel distance between origin and destination as an accessibility-based measure to replicate destination location choices which were further discretized as: 0 -1 km, 1-3 km, 3-5km, and 5 km and above. The descriptive statistics are calculated based on individuals who participated in that particular activity type. In terms of activity start time, it is evident that a large share of non-mandatory 73 activities was performed during off peak hours (Figure 5.2). For example, 43% of the trips made for shopping activity were during PM Off Peak hours. A similar trend was noticed for personal business and recreational activities. Interestingly, 18.1% of the trips made for recreation activity were during the early hours. In terms of travel mode and companionship distribution, it is quite evident that the majority of the non-mandatory activities are performed alone or with non-household members using auto mode (Figure 5.3). Interestingly, 18.4% of the trips made for shopping activity were performed with household members using auto mode. However, this share for personal business and recreation activity was only 13.5% and 14.3%, respectively. In addition, the share of non-auto modes for all the non-mandatory activities was less as compared to auto mode. Lastly, in terms of destination location choice, majority of the individuals travelled longer distances and chose destinations that were farther than 5 km from their origin (Figure 5.4). In this context, 52%, 50%, and, 44% of the trips made to perform shopping, personal business, and recreational activity, respectively chose farther destinations. However, 15.2% of the shopping activity was made within 1 km from the origin. Whereas 17.1% of the respondents travel for 3-5km to engage in recreational activity. 74 Figure 5.2 Distribution of Activity Start Time Alternatives Figure 5.3 Distribution of Mode-companionship Choice Alternatives Figure 5.4 Distribution of Destination Location Choice Alternatives 5.4. Independent Variables Tested The model tests the influence of sociodemographic attributes (e.g., age, gender, annual household income, employment status, household size), vehicle ownership attributes (e.g., number of 75 vehicles, number of bikes), land use attributes (e.g., land use mix index, share of residential, commercial, industrial, and other areas), accessibility measures (e.g., distances from home to the nearest urban center, bus stop, point of interest), and built environment attributes (e.g., length of bike lane and sidewalk), among others. The built environment and land-use attributes were generated for 250-metre buffer area based on the respondent’s home location, whereas accessibility measures were generated based on the road network-based distance from their home location using ArcGIS (v.10.6.1). The summary statistics of the variables retained in the final model are presented in the Table 5.1. Table 5.1. Descriptive statistics of the variables retained in the final model Variables Definition Mean/ Distribution SD Recreation Male Dummy, if an individual is male =1, 0 otherwise 45.80% - Age 18 to 35 Dummy, if an individual age is more than 18 and less than 35 years old =1, 0 otherwise 15.31% - Age 65 and above Dummy, if an individual’s age is 65 year and above =1, 0 otherwise 39.49% - Part-time worker Dummy, if an individual is a part-time worker =1, 0 otherwise 10.47% - Full-time worker Dummy, if an individual is a full-time worker =1, 0 otherwise 31.25% - Annual household income $50,000 to $80,000 Dummy, if an individual’s annual household income is more than $50,000 and less than $80,000 =1, 0 otherwise 18.94% - Annual household income above $125,000 Dummy, if an individual’s annual household income is more than $125,000 =1, 0 otherwise 23.17% - Number of bikes per household member Ratio of number of bikes in a household to total number of household members 0.96 0.83 Number of vehicles per household member Ratio of number of vehicles in a household to total number of household members 0.90 0.38 Number of household members Total number of household members 2.38 1.10 76 Variables Definition Mean/ Distribution SD Share of residential area near home location Share of residential area near in the neighborhood 0.39 0.25 Land use mix index Land use mix index of the neighborhood 0.43 0.17 Distance to nearest transit stop from home location Distance to the nearest transit stop from residential location of an individual 0.75 1.35 Available length of sidewalk and bike lane near home location Total length of sidewalk and bike lane in the neighborhood 1.06 0.98 Personal Business Male Dummy, if an individual is male =1, 0 otherwise 42.22% - Age 18 to 35 Dummy, if an individual age is more than 18 and less than 35 years old =1, 0 otherwise 10.45% - Age 65 and above Dummy, if an individual’s age is 65 year and above =1, 0 otherwise 45.24% - Part-time worker Dummy, if an individual is a part-time worker =1, 0 otherwise 11.53% - Full-time worker Dummy, if an individual is a full-time worker =1, 0 otherwise 22.54% - Annual household income $50,000 to $80,000 Dummy, if an individual’s annual household income is more than $50,000 and less than $80,000 =1, 0 otherwise 18.75% - Number of bikes per household member Ratio of number of bikes in a household to total number of household members 0.75 0.76 Number of vehicles per household member Ratio of number of vehicles in a household to total number of household members 0.91 0.42 Number of household members Total number of household members 2.19 0.96 Share of residential area near home location Share of residential area near in the neighborhood 0.34 0.27 Distance to nearest transit stop from home location Distance to the nearest transit stop from residential location of an individual 0.90 1.76 Available length of sidewalk and bike lane near home location Total length of sidewalk and bike lane in the neighborhood 0.97 0.98 Shopping Male Dummy, if an individual is male =1, 0 otherwise 40.24% - 77 Variables Definition Mean/ Distribution SD Age 18 to 35 Dummy, if an individual age is more than 18 and less than 35 years old =1, 0 otherwise 8.93% - Age 65 and above Dummy, if an individual’s age is 65 year and above =1, 0 otherwise 48.06% - Part-time worker Dummy, if an individual is a part-time worker =1, 0 otherwise 11.40% - Full-time worker Dummy, if an individual is a full-time worker =1, 0 otherwise 19.95% - Annual household income less than $50,000 Dummy, if an individual’s annual household income is less than $50,000 =1, 0 otherwise 24.18% - Annual household income $50,000 to $80,000 Dummy, if an individual’s annual household income is more than $50,000 and less than $80,000 =1, 0 otherwise 21.73% - Annual household income above $125,000 Dummy, if an individual’s annual household income is more than $125,000 =1, 0 otherwise 14.89% - Number of bikes per household member Ratio of number of bikes in a household to total number of household members 0.73 0.70 Number of vehicles per household member Ratio of number of vehicles in a household to total number of household members 0.91 0.44 Number of household members Total number of household members 2.22 1.00 Share of residential area near home location Share of residential area near in the neighborhood 0.35 0.27 Land use mix index Land use mix index of the neighborhood 0.43 0.18 Density of destination points Density of destination points in the neighborhood 0.14 0.04 Distance to nearest transit stop from home location Distance to the nearest transit stop from residential location of an individual 0.96 1.84 Available length of sidewalk and bike lane near home location Total length of sidewalk and bike lane in the neighborhood 0.95 0.97 Note: SD- standard deviation 78 5.5. Model Results This section is further categorized into the following sub-sections. First, the goodness-of-fit measures of the proposed joint model are presented along with a comparison with independent models. Further, the estimation results of the activity start time sub-model are discussed, followed by the mode-companionship sub-model, and the destination location choice sub-model. In addition, the effect of unobserved variables introduced to different choice dimensions is discussed. Lastly, the contribution of the unobserved terms is discussed by calculating the total variance of utility differences. 5.5.1. Goodness-of-fit Measures To evaluate the proposed joint model, multiple goodness-of-fit measures were deployed (Table 5.2). Furthermore, to clarify the advantage of the proposed model, a comparison was made between a) a joint discrete choice model for activity start time, mode-companionship, and destination location decisions with common unobserved factors; b) an independent model for activity start time, mode-companionship, and destination location decisions. Table 5.2. Goodness-of-fit measures of the joint and independent models Recreation Personal Business Shopping Log-likelihood for independent models -4418.5 -8305.6 -12456.35 Log-likelihood for joint models -3888.3 -7177.4 -11873.8 ?̅? 7776.7 14354.8 23747.7 ?̂? 6966.4 12508.7 22705.9 pD 810.3 1846.1 1041.8 DIC 8587.0 16201 24789.6 Note: ?̅?: posterior mean of -2 x Log-likelihood, ?̂?: -2 x Log-likelihood at posterior means of parameters, pD: ?̅? − ?̂?, Deviance Information Criterion (DIC): ?̅? + pD. 79 This study considers goodness-of-fit measures such as log-likelihood values, ?̅?, ?̂?, pD, and Deviance Information Criterion (DIC). Based on the comparison of log-likelihood values, it is evident that the proposed joint model outperforms the independent model. Therefore, further discussion is based on the proposed joint discrete choice model. 5.5.2. Parameter Estimation Results: Activity Start Time Table 5.3 depicts the parameter estimation results for the activity start time component. One must note that AM Off Peak is considered as a base alternative in the study. Among sociodemographic variables, results suggest that males are more likely to engage in personal business activity early in the day. Such individuals might engage in personal business activity while going to work due to time constraints associated with such activities (e.g. closing of banks after 7 PM, limited doctor’s appointment after 7 PM). On the other hand, males are less likely to engage in non-mandatory activities such as recreation during the PM peak hours. This might be due to willingness to perform such activity with household members (e.g., spouse/children) and constraints associated with work schedule, which circumvents their possibility to engage in such activities while returning from work. Younger individuals revealed propensity to engage in shopping activities later in the day (i.e. during PM peak hours), which might be due to work-related conflicts (Golshani et al., 2018). However, a negative relationship was observed for personal business activity during the night hours, as these activities (e.g. going to bank, doctor etc.) are often performed during early hours of the day. On the other hand, older individuals revealed a lower likelihood towards all three activities during the early and later hours of the day. This might be due to their tendency to avoid traffic congestion in peak hours and willingness to participate in activities such as recreation with family members, who are often occupied with work during this time. Moreover, full-time workers are more likely to choose PM peak or night hours to engage in recreational and shopping activities, 80 which reaffirms an understanding that workers engage in non-mandatory activities while returning from work (Shabanpour et al., 2017), or after arriving at home with family members. However, a negative relationship is observed for recreational and personal business activity during PM off peak hours, as they spend majority of their time at work. Moreover, they are more prone to engage in personal business activity while going/returning from work during AM/PM peak hours. In the case of part-time workers, a negative relationship was observed during the early hours of the day i.e. AM peak and PM Off peak for recreation and shopping activity, respectively. This finding manifests their involvement in work during the daytime and manifests their expenditure boundaries. Among household-level attributes, individuals living in low-income households (less than $50,000) depicted probability towards shopping activity during AM/PM Peak hours. However, a negative relationship was observed for night hours, as expected. In addition to this, individuals living in medium-income households ($50,000-$80,000) are less likely to engage in non-mandatory activities during the early hours (e.g., AM peak and PM Off peak) of the day as they might have work-related conflicts and most likely to dedicate these hours to work. Lastly, high income household residents revealed a positive relationship with the recreation activity during the night hours, which might be due to their willingness to perform such activities with family members after work and maintain work-life balance. 5.5.3. Parameter Estimation Results: Mode-companionship Table 5.3 reveals the parameter estimation results for the mode-companionship model. In this case, non-auto modes shared with household members/non-household members/alone is considered as the reference category. Among sociodemographic attributes, males are less likely to engage in non-mandatory activities such as shopping, recreation, or personal business with non-household members/alone while using auto mode. This might be due to an understanding that males prefer to 81 engage in such activities with their family members such as spouse or children and share household-related responsibilities while driving them for different activities using auto mode. In the case of younger individuals, a lower propensity was noticed while jointly engaging in shopping activity with household members using auto modes. Whereas, younger individuals are more likely to perform recreational activity with non-household member/alone using auto modes. On the other hand, older individuals tend to participate in recreational and personal business activities with household member using auto modes. This finding reflects their living arrangements and dependency on household members which leads to joint participation in such activities. Moreover, older individuals have higher tendency to socialize with them while performing activities such as jogging or going to parks. Similarly, older individuals depict lower propensity to perform shopping activity with non-household member/alone using auto modes. This again manifests their dependency on household members and physical limitation to perform such tasks alone. In the context of annual household income, high-income household residents are more likely to engage in recreational activity jointly with household members using auto modes. This finding suggests that such individuals consider this as an opportunity to socialize with household members and maintain work-life balance. In terms of vehicle ownership, individuals with higher number of vehicles in a household are more likely to perform non-mandatory activities using auto modes to travel, as expected. However, they are less likely to perform recreational activities (e.g., jogging, going for a walk) using auto modes. Lastly, individuals residing in larger households are more likely to perform non-mandatory activities with household members, while choosing auto modes, as expected. 82 5.5.4. Parameter Estimation Results: Destination Location In the context of the destination location choice model, alternative 3 – 5km is considered as the reference category. Results suggest that individuals’ destination location choice is governed by built environments, land use patterns, access to activity location points, access to transit stops, along with access to travel modes, such as vehicle/bike ownership (Table 5.3). In terms of access to activity destination points, results suggest that individuals residing in areas with a higher density of destination points are more prone to travel shorter distances and choose nearby destinations (Table 5). In the case of land use attributes, individuals residing in neighborhoods with a higher share of residential area choose destinations that were farther away (i.e., need to travel longer distances), reaffirming that such areas provide fewer nearby opportunities to engage in recreation, personal business, or shopping activities. For example, a positive relationship was observed for share of residential area with 1-3 km for all three non-mandatory activities. In addition, the presence of higher mixed land use in the neighborhood offers individuals opportunity to choose nearby destinations (i.e. in proximity within 0-1/1-3 km) circumventing the need to choose farther (i.e. 5 km and above) activity points, as most of the activity locations are typically located close to/in such mixed land use areas. In context with distance to nearest bus stop from home location, individuals residing far from the bus stop are more likely to travel for longer distances and choose farther destinations. This finding manifests that such individuals living in sub-urban or rural areas do not offer better destination accessibility as compared to an urban setting, making them travel longer distances and choose farther destinations to engage in an activity. Results confirm that vehicle and bike ownership significantly impact one’s destination location choice. For instance, households with a higher number of vehicles choose farther destinations (i.e. 5 km and above) which might be due to accessibility gained through vehicle ownership allowing them to travel 83 longer distances. On the contrary, a negative relationship was observed for nearby destinations, as expected. In terms of bike ownership, households with a higher number of bikes revealed a negative relationship with 1-3 km for personal business and recreational activity. This finding reaffirms that individuals prefer traveling much shorter distances to engage in an activity while using non-auto modes to travel. Lastly, individuals with higher access to sidewalks and bike lanes are less likely to choose farther destinations. This finding indicates such residents live in an urban setting with closer access to destination points, enabling them travel shorter distances to engage in activities using non-auto modes by utilizing available active transportation network. Table 5.3. Parameter estimation results of the activity start time, mode-companionship, and destination location choice components Recreation Personal Business Shopping Coefficient (t-stat) Activity start time choice Reference: AM Off Peak AM Peak Constant 1.36 (2.20) 1.38 (1.84) -3.34 (-16.77) Male 0.12 (1.77) 0.64 (2.90) Age 18 to 35 -3.39 (-4.53) Age 65 and above -1.71 (-2.78) Full-time worker 1.14 (5.88) 0.56 (1.87) Part-time worker -0.39 (-1.28) Annual household income less than $50,000 0.49 (2.03) Annual household income $50,000 to $80,000 -0.34 (-1.69) PM Off Peak Constant -0.28 (-2.89) -0.16 (-2.65) 0.34 (7.92) Full-time worker -0.58 (-3.21) -0.67 (-4.53) Part-time worker -0.35 (-3.37) Annual household income $50,000 to $80,000 -0.25 (-1.35) -0.22 (-1.79) -0.20 (-2.48) PM Peak Constant -0.81 (-5.45) -1.84 (-15.37) -8.05 (-1.74) Male -0.20 (-1.20) 84 Recreation Personal Business Shopping Age 18 to 35 6.65 (1.43) Age 65 and above -1.36 (-5.70) -0.46 (-2.58) Full-time worker 1.83 (10.26) 2.09 (11.69) 2.13 (18.55) Annual household income less than $50,000 0.07 (1.59) Night Constant -2.55 (-15.25) -1.40 (-2.19) -2.19 (-12.96) Age 18 to 35 -1.60 (-2.49) Age 65 and above -1.10 (-5.00) Full-time worker 2.32 (10.76) 2.05 (9.02) 1.40 (7.55) Annual household income less than $50,000 -0.13 (-1.61) Annual household income above $125,000 0.53 (2.31) Mode-companionship choice Reference: Non-auto mode with shared travel/alone Auto mode shared with household member Constant -0.34 (-1.08) -1.48 (-4.75) 5.36 (1.65) Number of vehicles per household member -0.96 (-3.72) 1.22 (5.23) 1.55 (7.43) Age 18 to 35 -6.08 (-1.86) Age 65 and above 0.42 (1.92) 0.31 (2.17) Annual household income above $125,000 0.82 (3.70) -0.27 (-1.98) Number of household members 0.21 (2.68) 0.26 (3.40) 0.12 (2.54) Auto mode shared with non-household member/alone Constant 0.19 (0.27) 0.90 (5.66) 0.72 (5.03) Male -0.20 (-1.28) -0.17 (-1.59) -0.28 (-3.50) Age 18 to 35 1.45 (2.48) Number of vehicles per household member 0.64 (2.67) 2.28 (11.98) 2.28 (12.53) Age 65 and above -0.29 (-3.47) Part-time worker 0.51 (2.50) Destination location choice Reference: 3-5 km 0-1 km Constant -0.87 (-1.74) -0.11 (-0.57) -0.76 (-4.74) Number of vehicles per household member -0.66 (-1.83) -0.58 (-3.13) Land use mix index 1.22 (2.14) 1.05 (3.81) Share of residential area near home location -0.80 (-2.34) Density of destination points 140.9 (1.48) 85 Recreation Personal Business Shopping Distance to nearest transit stop from home location -0.83 (-3.01) -0.08 (-1.63) -0.09 (-3.37) 1-3 km Constant -0.37 (-0.98) 0.44 (3.01) 0.43 (5.53) Land use mix index 1.23 (2.48) Number of vehicles per household member -0.18 (-1.39) Number of bikes per household member -0.21 (-2.26) -0.11 (-1.63) -0.0005 (-1.19) Density of destination points -223.6 (2.00) Share of residential area near home location 0.70 (2.14) 0.50 (2.94) 0.18 (1.29) 5 km and above Constant 0.90 (4.17) 1.35 (7.88) 0.97 (9.50) Number of vehicles per household member 0.47 (2.46) 0.17 (1.09) 0.30 (3.72) Distance to nearest transit stop from home location 0.28 (3.98) 0.15 (4.28) 0.05 (2.58) Available length of sidewalk and bike lane near home location -0.34 (-4.44) -0.49 (-9.35) -0.29 (-7.07) 5.5.5. Effects of Common Unobserved Terms Table 5.4 depicts the correlation matrix for the estimated joint model. The correlation among the alternatives is calculated using the estimated variances (𝜎𝑝𝑞2 , 𝜎𝑝𝑟2 , 𝑎𝑛𝑑 𝜎𝑞𝑟2 ) of the introduced common unobserved terms (𝜃𝑖𝑝𝑞 , 𝜇𝑖𝑝𝑟 , 𝜑𝑖𝑞𝑟) between the utilities, which can be represented as follows (Varghese et al., 2022): 𝑐𝑜𝑟𝑟(∑ 𝜃𝑖𝑝𝑞 + 𝜀𝑖𝑝,𝑃𝑝=1 ∑ ±𝜃𝑖𝑝𝑞 + 𝜀𝑖𝑞𝑄𝑞=1 ) =𝜎𝑝𝑞2√∑ 𝜎𝑝′𝑞2𝑃𝑝′=1+𝜋2 6⁄ √∑ 𝜎𝑝𝑞′2 +𝜋2 6⁄𝑄𝑞′=1 (5.6) The above equation calculates the correlation among activity start time and mode-companionship alternatives. Similarly, correlation among other alternatives (a) activity start time and destination location (b) mode-companionship and destination location, can be calculated. The parameters signify the interdependencies among the choice alternatives. All the estimated parameters are found to be statistically significant and positive. In the case of recreation activity, results reveal 86 that unobserved terms commonly impact activity start time and mode-companionship choices. For instance, unobserved factors that increase the propensity to engage in recreational activities during night hours also increase the propensity to perform them jointly with household members using auto modes. Similar results were observed for shopping activities. However, results were not the same for PM peak and PM off-peak hours. For instance, unobserved factors that increase the likelihood to engage during PM peak hours also increase the propensity towards independent or activity participation with non-household members for recreation, shopping, and personal business activity. On the other hand, unobserved factors that increase the likelihood to perform personal business activity during PM off-peak hours also increase the propensity to perform them jointly with household members using auto modes. Moreover, unobserved terms commonly introduced to activity start time and destination location choice revealed a positive effect for all three activities. For example, unobserved terms that increase the propensity to engage in all three non-mandatory activities during PM peak hours also increase the likelihood to choose destinations which are in proximity of 0-1 km. On the other hand, a positive relationship was noticed between unobserved factors increasing the probability to engage in recreation and shopping activity during night hours while traveling longer distances (i.e. 5 km and above). Similar findings were observed for personal business activity but for PM off peak hours. Lastly, unobserved terms are found to have common effects on mode-companionship and destination location choices. Note that similar findings were observed for all three activities. For instance, unobserved factors that increase the probability to perform an activity jointly with household members using auto modes also increase the likelihood to choose farther (5 km and above) locations as destinations. On the other hand, unobserved factors that increase consideration 87 of destination which are in proximity of 0-1 km, also increase the probability to perform the activity independently or with non-household members using auto modes. Table 5.4. Correlation matrix for joint model Recreation Personal Business Shopping PM Peak & Auto mode shared with non-household member/alone 0.20*** 0.24*** 0.01* Night & Auto mode shared with household member 0.21*** 0.17*** PM Off Peak & Auto mode shared with household member 0.25*** Auto mode shared with household member & 5 km and above 0.22*** 0.26*** 0.13*** Auto mode shared with non-household member/alone & 0-1 km 0.29*** 0.23*** 0.002*** 0-1 km & PM Peak 0.24*** 0.27*** 0.20*** 5 km and above & PM Off Peak 0.19*** 5 km and above & Night 0.32*** 0.01*** Note: (*) significant at 10% level, (**) significant at 5% level, (***) significant at 1% level 5.5.6. Contribution of Common Unobserved Terms Recalling the above discussion, goodness-of-fit measures clearly indicate that joint model accounting for unobserved factors outperforms the independent model and can be utilized for further analysis. However, improvement in the log-likelihood should not be used as the sole criterion for confirming the effects of common unobserved factors (Pinjari et al., 2008). Therefore, to identify the contribution of unobserved factors, total variance of utility differences can be disintegrated as the sum of variations in observed and unobserved factors (Chikaraishi et al., 2011). A similar approach was adopted to identify the contribution of observed (sociodemographic, land use, travel attributes) and unobserved factors towards total variance in utility differences in different empirical contexts (Tran et al., 2016; Varghese et al., 2022). Therefore, the total variance of utility differences can be represented as follows: Activity Start Time Choice Component: 88 𝑉𝑎𝑟(𝑈𝑖𝑝 − 𝑈𝑖𝑝′ ) = 𝑣𝑎𝑟(𝛽𝑝𝑋𝑖𝑝 − 𝛽𝑝′𝑋𝑖𝑝′) + 𝜎𝑝𝑞2 + 𝜎𝑝𝑟2 + 𝜋2 3⁄ (5.7) Mode-Companionship Choice Component: 𝑉𝑎𝑟(𝑈𝑖𝑞 − 𝑈𝑖𝑞′ ) = 𝑣𝑎𝑟(𝛽𝑞𝑋𝑖𝑞 − 𝛽𝑞′𝑋𝑖𝑞′) + 𝜎𝑝𝑞2 + 𝜎𝑞𝑟2 + 𝜋2 3⁄ (5.8) Destination Location Choice Component: 𝑉𝑎𝑟(𝑈𝑖𝑟 − 𝑈𝑖𝑟′) = 𝑣𝑎𝑟(𝛽𝑟𝑋𝑖𝑟 − 𝛽𝑟′𝑋𝑖𝑟′) + 𝜎𝑝𝑟2 + 𝜎𝑞𝑟2 + 𝜋2 3⁄ (5.9) Here, 𝑈𝑖𝑝, 𝑈𝑖𝑞, 𝑈𝑖𝑟 are the utilities of activity start time choice, mode-companionship choice, and destination location choice, respectively. Whereas, 𝑈𝑖𝑝′ , 𝑈𝑖𝑞′ , 𝑈𝑖𝑟′ represents the utilities for the base alternatives in the respective discrete choices. In the case of activity start time choice – ‘AM Off Peak, and mode-companionship choice – ‘non-auto shared with household members/non-household members/alone’, have been selected as base alternatives. Moreover, 3 - 5 km was considered as base for destination location choice component. One must note that the first component on the right side in equations 8-10, depicts the variance described by observed variables. Furthermore, the second and third components show the variances observed by common unobserved factors establishing the interdependencies between different activity-travel choice dimensions. Lastly, the fourth component represents the variance by the Gumbel distributed error term. Table 5.5 reveals the contribution of different variables towards total variance of utility differences. It is quite evident that common unobserved factors and error terms play a dominating role in activity start time, mode-companionship, and destination location choice decisions. For instance, the contribution of error terms towards the total variance of utility differences for activity start time, mode-companionship, and destination location alternatives vary from 36.80% - 71.69%, 89 45.49% - 75.37%, 53.73% - 88.51%, respectively. As the error terms account for the influence of unobserved factors on activity-travel decisions, a high proportion of variance suggests that more observed factors can be introduced in such models, for better accuracy and model fit. Furthermore, the influence of common unobserved factors is relatively substantial for all the three alternatives. More specifically, up to 16.32% of total variance of activity start time utilities can be explained by common unobserved terms. For example, in case of recreational activity, 𝜃𝑖𝑝𝑞 and 𝜇𝑖𝑝𝑟 terms capturing the magnitude of influence of the common unobserved variables between a) Night and auto mode shared with household members, and b) night and 5 km and above, explains 11.76% and 12.60% of the variance in the utility difference between choosing night and AM Off peak, respectively. This suggests that individuals choose pairs of alternatives that are compatible with their travel preferences. However, the share of variance in the utility difference between night and AM Off peak hours for shopping activity is quite low (𝜃𝑖𝑝𝑞: 8.06% and 𝜇𝑖𝑝𝑟: 0.22%). This reflects constraints associated with such activities after certain hours of the day. Furthermore, up to 18.12% of total variance of mode-companionship utilities can be explained by common unobserved terms. For example, in case of personal business activity, (𝜃𝑖𝑝𝑞 , 𝜑𝑖𝑞𝑟) terms capturing the influence between a) auto mode shared with non-household member/alone and PM peak, and b) auto mode shared with non-household member/alone and 0-1 km, reveals 13.66% and 12.45% of the variance in the utility difference between choosing auto mode shared with non-household member/alone and non-auto mode shared with household member/non-household member/alone. In addition, contribution of total variance in destination location choice utilities ranges from 0.11% - 18.01% by common unobserved terms. In the case of shopping activity, unobserved common term (𝜇𝑖𝑝𝑟) between PM peak and 0 – 1 km explains 11.11% of the variance in the utility difference between choosing 0-1 km and 3-5 km, respectively. However, this share 90 was low for recreation activity, as the unobserved common term (𝜇𝑖𝑝𝑟) between PM peak and 0 – 1 km explains 9.43% of the variance in the utility differences. Lastly in case of observed variables, their magnitude of influence on the three choices is quite diverse. For instance, the contribution of observed factors towards the total variance in difference between the utilities of activity start time, mode-companionship, and destination location alternatives vary from 1.85% - 58.39%, 5.17% - 50.64%, 1.72% - 24.75%, respectively. In particular for activity start time, total variance of utilities (i.e. 58.39%) with respect to PM peak are mostly explained by sociodemographic variables (e.g. age, gender, annual household income, and employment status) for the shopping activity. As for recreation activity, the observed factors explain 27.47% of the total variance with respect to PM peak. Regarding mode-companionship choice, factors such as vehicle/bike ownership, annual household income, age, gender explained 50.64% of the total variance with respect to auto mode shared with household members for shopping activity. Interestingly, observed factors have a larger influence on mode-companionship choice - auto mode shared with household members as compared to auto mode shared with non-household member/alone for the recreation and shopping activities. Lastly, built environment and land use attributes revealed influences on one’s destination location choices. For example, 24.75% of the total variance with respect to 0-1 km for recreational activity was explained by land use measures. However, this share was quite low for shopping activity with respect to 0-1 km (1.72%). Overall the results suggest that such measures significantly impact one’s decision while choosing destinations. 91 Table 5.5. Contribution of different variables towards total variance of utility differences Recreation Personal Business Shopping Activity Start Time Choice PM Peak Night PM Off Peak PM Peak PM Peak Night Observed factors 27.47 22.09 1.85 16.82 58.39 20.02 Common unobserved terms (𝜃𝑖𝑝𝑞) 9.94 11.76 16.32 13.25 0.11 8.06 Common unobserved terms (𝜇𝑖𝑝𝑟) 11.49 12.60 12.08 14.76 4.70 0.22 Error terms 51.10 53.55 69.74 55.17 36.80 71.69 Mode-Companionship Choice Auto mode shared with household member Auto mode shared with non-household member/alone Auto mode shared with household member Auto mode shared with non-household member/alone Auto mode shared with household member Auto mode shared with non-household member/alone Observed factors 7.69 6.54 5.17 17.03 50.64 24.31 Common unobserved terms (𝜃𝑖𝑝𝑞) 18.12 11.98 14.99 13.66 0.14 0.23 Common unobserved terms 𝜑𝑖𝑞𝑟) 13.36 13.27 15.77 12.45 3.73 0.09 Error terms 60.83 68.21 64.06 56.78 45.49 75.37 Destination Location Choice 0-1 km 5km and above 0-1 km 5km and above 0-1 km 5km and above Observed factors 24.75 7.30 2.16 7.97 1.72 3.95 Common unobserved terms (𝜑𝑖𝑞𝑟) 12.06 18.01 14.41 15.96 0.11 7.26 92 Recreation Personal Business Shopping Common unobserved terms (𝜇𝑖𝑝𝑟) 9.43 14.23 17.61 11.23 11.11 0.27 Error terms 53.73 60.46 65.83 64.83 87.06 88.51 Note: All values are in % 5.6. Concluding Remarks This chapter proposes an integrated modelling framework for investigating activity-travel decisions, and their inter-dependencies. Specifically, a joint discrete choice modelling structure has been developed to investigate activity start time, mode of travel, companionship, and destination location choice decisions, while introducing unobserved terms common to these choices. These unobserved terms account for individual’s perception/attitudes/habits etc. which jointly influence these choices’ dimensions. The model results confirm the presence of interdependencies among the choice dimensions. In this regard, unobserved factors affecting activity start time also affect mode-companionship dimensions. For example, unobserved factors that affect the likelihood to engage in non-mandatory activities during night hours also increase the propensity of joint activity participation with household members using auto modes for travel. In addition, this study confirms that activity start time and destination location decisions are intricately interrelated. For instance, unobserved factors influencing the participation in an activity during PM peak hours also increases the choice of nearby destinations which are in proximity of 0-1 km. Similarly, unobserved factors positively influencing joint activity participation with household members also increase the probability of traveling to farther destinations. In addition to these unobserved factors, this study reaffirms the effects of sociodemographic, vehicle ownership, and built environment attributes on activity-travel choices. The results suggest 93 that males, full-time workers, and younger individuals are more likely to perform non-mandatory activities, such as recreational and shopping activities, later in the day (after working hours). Regarding mode choice and joint activity participation decisions, males and full-time workers reveal a higher likelihood of performing activities with household members using auto modes. Lastly, in the case of destination location choice, results reveal that the urban characteristics, such as proximity to destination points, proximity to bus stops, and presence of mixed land use, encourage individuals to choose closer destinations, avoiding long-distance travel and farther destinations. To further understand the contribution of these observed and unobserved factors introduced in the model, the total variance of utility differences was calculated. Results suggest that unobserved factors have significant effects on the activity-travel dimensions while their influencing magnitudes are also substantial. In terms of implications, the findings of this study suggest the need to jointly model activity-travel decisions to improve our behavioural understanding. The results can also assist in catering policies and strategies to increase the share of sustainable mode usage for the non-mandatory activities at different times of day. For instance, many individuals prefer to perform non-mandatory activities later in the day. During this period, transit services run at an infrequent basis which makes it more difficult for transit to be an attractive alternative. Therefore, transit agencies and cities should review their schedules and investigate ways to accommodate more non-mandatory travel. This review is particularly important for transit agencies, given the reduction in work travel during and after the pandemic due to preference towards working from home. The results also suggest a strong preference for joint travel with household members using car mode. Incentivizing transit passes for multiple purchases within the same household could increase the share of transit usage. In addition, targeted mixed land-use developments can encourage individuals to choose nearby 94 destinations. This will in turn facilitate the opportunity to travel to these nearby destinations using active modes (walk/bike). Overall, the findings of the study provide important insights on one’s activity-travel behaviour and can be utilized to take informed policy related decisions. 95 CHAPTER 6: MODELLING ACTIVITY GENERATION DECISIONS OF TELECOMMUTERS AND NON-TELECOMMUTERS 6.1. Introduction This chapter aims to investigate the differences in daily activity time-use behaviour of telecommuters (individuals who work from home) and non-telecommuters (individuals who work at regular workplace). Telecommuting has been considered as a potential travel demand management strategy to reduce peak-hour commute travel, roadway congestion, and emissions, among other things. However, it can substantially influence non-mandatory activity participation and further dictate travel outcomes, which needs further investigation. In this regard, telecommuting provides individuals more time each day due to the elimination of commute travel times and flexible working hours, compared to non-telecommuters (i.e. individuals working at the workplace). This could potentially result in significant differences in the 24-hour activity patterns of telecommuters and non-telecommuters, which has not been captured to significant extents in current literature at a disaggregated episode level. Therefore, this study utilizes MDCEV-OP model that jointly deals with activity engagement and duration choices at a disaggregated-episode level. This episode-level granularity of the framework provides more detailed disaggregated level information about the two worker profiles. More specifically, answering the following research question: how do the activity engagement patterns, including duration and frequency of non-mandatory activities, at the episode-level differ between telecommuters and non-telecommuters? Chapter 6 is derived from: Khaddar, S., Fatmi, M. R., & Nikodym, T. (2023). Are Telecommuters’ and Non-Telecommuters’ Daily Time-Use Behaviors Different? An Episode-Level Model for Non-Mandatory Activities. Transportation Research Record, 2677 (11), 96-110. https://doi.org/10.1177/03611981231164390 96 Further, the effects of various sociodemographic, mobility tool ownership, and built environment attributes, on an individual's activity participation and duration choices, are tested in the study using data from the 2018 OTS survey. Lastly, this study also adopts a forecasting procedure to examine how the proposed model could predict observed activity engagement and duration rates. 6.2. Methodology This section provides a brief description of the modelling approach adopted in the study. Methodologically, this study investigates the activity participation (i.e. discrete choice) and corresponding durations (i.e. continuous dimension) while accounting for multiple occurrences of each activity type. This newly proposed model assumes that individuals allocate certain times to different episodes of different activities such that the utility derived by engaging in the various activities and corresponding episodes is maximized. However, this is subject to a budget constraint of 1440 minutes (i.e. total time available in a day). An additively separable non-linear utility is utilized in this study which can be formulated as (Bhat, 2008; Saxena et al., 2022): 𝑈(𝑡𝑝) = 𝜓𝑝1𝑙𝑛(𝑡𝑝1) + ∑ ∑ 𝜓𝑝𝑘,𝑗𝑘𝛾𝑝𝑘𝑙𝑛 (𝑡𝑝𝑘,𝑗𝑘𝛾𝑝𝑘+ 1)𝐽𝑘𝑗𝑘=1𝐾𝑘=2 (6.1) Here 𝑈(𝑡𝑝) is the maximum utility individual p tries to acquire by allocating time to each of the 𝑗𝑘 episodes of the K activities. Further, 𝑡𝑝 represents the time allocation vector 𝑡𝑝𝑘,𝑗𝑘∀𝑗𝑘 ∈ 𝐽𝑘 and 𝑘 ∈ 𝐾, where 𝑡𝑝𝑘,𝑗𝑘 is the time allotted by individual p to 𝑗𝑘𝑡ℎ episode of the kth activity. Equation 1 is valid if 𝜓𝑖1 > 0, 𝜓𝑖𝑘,𝑗𝑘 > 0, and 𝛾𝑖𝑘 > 0 ∀ 𝑘 ∈ 𝐾. In addition, 𝜓𝑝1 represents the baseline marginal utility for time allotted to the outside good which is at the aggregate level. This study considers in-home activity duration, duration of work/education, and travel duration as outside goods. Where, 𝜓𝑝𝑘,𝑗𝑘 is the baseline marginal utility of allocating time to 𝑗𝑘𝑡ℎ episode of the kth 97 activity controlling discrete and continuous decisions of whether to assign time to the corresponding episode of an activity and the amount of time. Here, 𝛾𝑝𝑘 is the satiation parameter controlling for time allocation to an episode of an activity. In this study, satiation parameter is presumed to be similar across different episodes of an activity type. Further, the baseline parameter and satiation parameter utility can be represented as: 𝜓𝑝𝑘,𝑗𝑘 = 𝑒𝑥𝑝(𝛽′𝑋𝑝𝑘𝑗𝑘 + 𝜀𝑝𝑘𝑗𝑘) (6.2) 𝛾𝑝𝑘 = 𝑒𝑥𝑝(𝜌′𝑍𝑝𝑘) (6.3) where, 𝑋𝑝𝑘𝑗𝑘 and 𝑍𝑝𝑘 are the vectors of baseline marginal utility and satiation parameters respectively, and corresponding estimable coefficients are represented by 𝛽′ and 𝜌′. 𝜀𝑝𝑘𝑗𝑘 is a random error term identifying unobserved factors that influence one’s choices. One must note that baseline marginal utilities are parameterized as a function of observed and unobserved variables. As this framework deals with episode-level choices, it is important to label episodes of an activity which clearly differentiates them from one another. One of the approaches could be labeling them based on time of day or location. However, this does not determine the number of occurrences of each activity type that might occur during that time of day or location. Another approach could be labeling them based on the duration without predicting their chronology of occurrence. In other words, the episode with the longest duration is considered as the first episode, the second longest as the second, and so forth. However, one can engage in a lower duration of an episode early in the day and might engage in higher durations later in the day, but this model is not agnostic to such scheduling decisions and just deals with the time use decisions at an disaggregate level. To do so, a logical ordering is deployed ensuring that lowered numbered episodes occurred first. For example, the 2nd episode of an activity is not predicted without the occurrence of the 1st episode of 98 that activity. This model imposes a decreasing baseline preference across episodes of an activity, while defining episodes in the non-decreasing order of their observed duration: 𝜓𝑝𝑘,𝑗𝑘 ≥ 𝜓𝑝𝑘,(𝑗𝑘+1)∀ 𝑘 ∈ {2, … . 𝐾}, 𝑗𝑘 ∈ {1,2,3 … . 𝐽𝑘 − 1} (6.4) The above condition imposes that the distribution of baseline marginal utility parameters accounting for each episode of an activity is right-truncated by that of the previous episode. This accommodates the logical ordering confirming that higher numbered episodes are never chosen without the lower numbered episodes. While it is difficult to specify such truncations using the assumed stochastic distributions, to ensure the conditions in Equation (4), the likelihood function of the traditional MDCEV model (that is defined at an episode-level) is conditioned on the set of conditions in Equation (4). Keeping this discussion in mind, the likelihood function of the traditional MDCEV defined at a disaggregated-episode level is conditioned on the set of constraints mentioned in Equation (4). The resulting conditional likelihood in a closed-form can be expressed as: ℒ{𝑡1∗, … , (𝑡𝑚,1∗ , 𝑡𝑚,2∗ , … 𝑡𝑚,𝐼𝑚∗ ), . . , (𝑡𝑀,1∗ , 𝑡𝑀,2∗ , … 𝑡𝑀,𝐼𝑀∗ ), 0, . .0} = |𝐽|𝜇𝛿−1(𝛿 −1)! x∏ ∏ exp (𝜇𝑉𝑚,𝑖𝑚)𝐼𝑚𝑖𝑚=1𝑀𝑚=1∏ ∏ (𝑒𝜇?̅?𝑘,𝑗𝑘∑ 𝑒𝜇?̅?𝑘,𝑠𝐽𝑘𝑠=𝑗𝑘)∗(∑ ∑ exp (𝜇𝑉𝑘,𝑗𝑘𝐽𝑘𝑗𝑘=1𝐾𝑘=1 ))𝛿𝐼𝑘𝑗𝑘=1𝑀𝑘=1 (6.5) Where 𝑡1∗ and 𝑡𝑚,𝐼𝑚∗ represents the time allotted to the outside good and each episode of an activity respectively. 𝑚 and 𝑀 is the index for the chosen activity and set of activity respectively. 𝐼𝑚 is the number of episodes of a activity m, and 𝑖𝑚 is the episode number for that activity. Furthermore, δ denotes the total number of chosen episodes across all activities. 𝑉𝑘,𝑗𝑘 = 𝛽𝑘,𝑗𝑘′ 𝑧𝑘,𝑗𝑘 −𝑙𝑛 (𝑡𝑘,𝑗𝑘∗𝛾𝑘+ 1) and |𝐽| is the determinant of the Jacobian matrix, which is of a similar structure as 99 derived by Bhat et al. (Bhat, 2005). The final likelihood equation is very similar to traditional disaggregated MDCEV models, scaled by the rank-ordered logit terms of the baseline marginal utilities of the corresponding episodes being in the decreasing order for all chosen activities. For more details of the likelihood derivation refer to Saxena et al. (Saxena et al., 2022). 6.3. Descriptive Statistics The data comes from the 2018 OTS. To identify telecommuters and non-telecommuters in the dataset, this study only considers individuals working based on their employment status (i.e., full time and/or part time workers). To further identify their work arrangement type response towards type of work location was utilized. This question includes options such as “work from home”, “no fixed workplace/no usual place of work”, and “work away from home”. Individuals working from home with no commute trips on the travel diary day are referred to as “telecommuters”, whereas individuals working at office/in-person setting with commute travel were considered as “non-telecommuters”. Furthermore, activity engagement and time allocation information was extracted for both the worker profiles. Further, the activity time allocation was categorized as in-home (IH) and out-of-home (OH) activities. Where, OH activities were disaggregated into the following non-mandatory activities: escorting (pickup/drop-off), personal business (visiting bank, visiting doctor, etc.), recreation (sports, swimming, etc.), eat-out (dine-in restaurant), social (meeting friends/relatives, social/civic events, etc.), and shopping (purchasing groceries, supplies, etc.). The final sample size includes 1684 non-telecommuters and 369 telecommuters. Later, this OH activity information was defined at an episode level. The maximum number of episodes considered for each activity type was three, since no/very less individuals were observed to have undertaken more than three episodes for an activity in a day. Descriptive statistics of activity participation and time allocation for telecommuters and non-telecommuters are presented in Table 6.1. One must note 100 that 100% engagement is observed for in-home activity, and average time allocation is calculated only for those individuals who engaged in that out-of-home activity type. In terms of activity participation rates, telecommuters engage more in personal business, recreation, social, and shopping activities compared to non-telecommuters. However, results were contrary for escorting and eat-out activities. Interestingly, more telecommuters engage in higher episodes of activities in comparison with non-telecommuters. Table 6.1. Summary of activity participation and time allocation of the OTS Sample Activity level participation Episode level participation (% (number) of individuals participating in an episode) Average time allocation (minutes) Activity Purpose % (number) of individuals participating an activity 1 or more 2 or more 3 or more Activity level Episode level Telecommuters Escorting 26.56 (98) 26.56 (98) 15.45 (57) 2.71 (10) 33.4 5.6 Personal business 31.71 (117) 31.71 (117) 6.78 (25) 1.63 (6) 138.5 22.9 Recreation 26.29 (97) 26.29 (97) 2.71 (10) 0.27 (1) 106.0 55.7 Eat-out 10.84 (40) 10.84 (40) 0.54 (2) 0.27 (1) 70.4 10.5 Social 19.51 (72) 19.51 (72) 2.44 (9) 0.27 (1) 214.6 48.6 Shopping 39.84 (147) 39.84 (147) 9.21 (34) 1.90 (7) 64.3 17.6 Non-telecommuters Escorting 31.06 (461) 31.06 (461) 14.69 (218) 1.82 (27) 17.0 4.3 Personal business 22.44 (333) 22.44 (333) 4.65 (69) 0.81 (12) 56.7 12.6 Recreation 18.67 (277) 18.67 (277) 1.15 (17) 0.27 (4) 99.2 25.7 Eat-out 22.64 (336) 22.64 (336) 2.09 (31) 0.20 (3) 51.9 12.6 Social 18.19 (270) 18.19 (270) 2.22 (33) 0.27 (4) 159.0 28.0 Shopping 31.27 (464) 31.27 (464) 5.46 (81) 0.47 (7) 36.3 13.4 Note: Number of observations are shown in parenthesis. 101 For instance, 9.21% of telecommuters participated in a 2nd episode of shopping activity, whereas the share of non-telecommuters was only 5.46%. On the contrary, a higher percentage of non-telecommuters engage in a second episode of eat-out activity. In terms of activity duration, telecommuters depicted higher time allocation to different activities as compared to non-telecommuters. For example, telecommuters spend an average of 64.3 minutes on shopping activity, which is 28 minutes higher than that of non-telecommuters. For personal business, a telecommuter’s average duration was 138.5 minutes, whereas for non-telecommuters this was 56.7 minutes (almost half). Similarly, at an episode-level, telecommuters are spending higher duration in all non-mandatory activities as compared to non-telecommuters. As expected, escorting has the lowest share of time allocation at episode and activity levels for both worker profiles. 6.4. Independent Variables Tested The model tests the influence of sociodemographic attributes (e.g., age, gender, annual household income, employment status, occupation type, household size), vehicle ownership attributes (e.g., number of vehicles), land use attributes (e.g., land use mix index, share of residential, commercial, industrial, and other areas), accessibility measures (e.g., distances from home to the nearest urban center, bus stop, point of interest), and built environment attributes (e.g., length of bike lane and sidewalk), among others. The built environment and land-use attributes were generated for 250-metre buffer area based on the respondent’s home location, whereas accessibility measures were generated based on the road network-based distance from their home location using ArcGIS (v.10.6.1). The summary statistic of the variables retained in the final model are presented in Table 6.2. 102 Table 6.2. Descriptive statistics of the variables retained in the final model Variables Definition Mean/Distribution SD Telecommuters Gender: Female Dummy, if an individual is female =1, 0 otherwise 54.74% - Age Age of an individual 48.83 13.92 Annual household income more than $80,000 Dummy, if an individual’s annual household income is more than $80,000 =1, 0 otherwise 54.47% - Full-time workers Dummy, if a full-time worker is female =1, 0 otherwise 60.16% - Household with more than 3 children Dummy, if an individual resides in a household with more than 3 children =1, 0 otherwise 35.23% - No. of vehicles in a household No. of vehicles in a household 2.02 0.95 Distance from home to nearest urban core less than 2KM Dummy, if distance to the nearest urban core is less than 2 km from residential location of an individual = 1, 0 otherwise 19.78% - Land use mix index Land use mix index of the neighborhood 0.43 0.17 Non-telecommuters Gender: Female Dummy, if an individual is female =1, 0 otherwise 55.73% - Age Age of an individual 44.42 12.34 Annual household income more than $80,000 Dummy, if an individual’s annual household income is more than $80,000 =1, 0 otherwise 60.91% - Full-time workers Dummy, if an individual is a full-time worker =1, 0 otherwise 84.32% - Household with more than 3 children Dummy, if an individual resides in a household with more than 3 children =1, 0 otherwise 43.85% - No. of vehicles in a household No. of vehicles in a household 2.14 0.97 Distance from home to nearest urban core less than 2KM Dummy, if distance to the nearest urban core is less than 2 km from residential location of an individual = 1, 0 otherwise 27.28% - Land use mix index Land use mix index of the neighborhood 0.43 0.18 Note: SD- standard deviation 103 6.5. Model Results This section is further categorized into the following sub-sections. First, the goodness-of-fit measures of the proposed model are presented along with a comparison with the conventional MDCEV model. Further, the parameter estimation results are discussed for both the worker profiles. Lastly, forecasting analysis is conducted to understand how the proposed model could replicate the observed activity engagement and duration rates at an episode level. 6.5.1. Goodness-of-fit Measures The goodness of fit measures considered in this study are log-likelihood at convergence, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) values (Table 6.3). A comparison was made based on goodness-of-fit measures between two empirical models: 1) the proposed MDCEV-OP model and 2) MDCEV model considering alternatives at a disaggregated level. Results clearly reveal that the MDCEV-OP model provides a superior statistical fit than the MDCEV model (Table 6.3). Furthermore, this study estimates three separate models for telecommuters, non-telecommuters, and for all workers while accounting for telecommuting as a dummy variable – to empirically demonstrate the variation in time use patterns of the different worker profiles. The goodness-of-fit measures clearly indicate that separate models must be adopted to address the differences in the 24-hour time patterns. As separate models for telecommuters and non-telecommuters outperforms the all-sample model (Table 6.3). For brevity, further discussion is based on the separate MDCEV-OP models estimated for both the worker profiles (Table 6.4). 104 Table 6.3. Goodness-of-fit Measures MDCEV-OP Model Telecommuters Non-telecommuters All Sample N 369 1484 1853 Log-likelihood at convergence -5180.76 -17466.7 -20927.30 AIC 10447.52 35035.36 41956.60 BIC 10615.68 35305.79 42283.35 Disaggregated MDCEV Telecommuters Non-telecommuters All Sample N 369 1484 1853 Log-likelihood at convergence -5560.32 -18804.70 -22147.10 AIC 11206.64 37711.40 44396.20 BIC 11373.98 37987.97 44677.95 Table 6.4 represents the parameter estimation results for the MDCEV-OP model. One must note that constants are estimated in the baseline preference for each episode of the activity considering “outside good” as the reference. The activity-level constants correspond to the first episode of an activity as it enters the baseline utility of all the episodes of that activity. In addition, the activity-level constants depict the aggregate influence of unobserved factors on the propensity to engage in one or more episodes of an activity. On the other hand, episode specific constants capture the differential effect for that episode with respect to the activity level constants. For example, in case of non-telecommuters the baseline preference constant for the second episode of ‘escorting’ activity is -7.16 (because it is the sum of associated activity level constant and the 2nd episode specific constant, i.e. (-8.18 + (1.02) = -7.16). In the adopted MDCEV-OP framework, the baseline utility parameters across different episodes of an activity are always in non-increasing order. Because of this inherent property, overall baseline preferences are in decreasing order even if episode-specific parameters are positive or zero. This ordering in the baseline utility circumvents the need to estimate each episode-specific parameter in the model and they turned out to be insignificant and were dropped from the final model. In addition to this, a reason for insignificant 105 episode-specific parameters is the activity engagement rates at an episode level. Moreover, the percentage of individuals participating in higher number of activity episodes is quite low in the data (Table 6.1). One must note that, the scale parameter was also estimated for both the models. The estimated scale parameter for telecommuters and non-telecommuters is 0.47 (21.33) and 0.25 (34.94), respectively. The next subsection explores telecommuters’ and non-telecommuters’ activity-time-use behaviour by testing the effect of sociodemographic (individual and household level), vehicle ownership, and built environment attributes. 6.5.2. Non-telecommuters: Baseline Preference and Satiation Parameter Estimates Among socio-demographic attributes, females working in a standard work arrangement (i.e. commuting to work) are more likely to engage in higher episodes of escorting (i.e. episode specific parameter for the 3rd episode is 0.05) and personal business activity (i.e. episode specific parameter for the 3rd episode is 0.12), along with shopping activity (i.e. episode specific parameter for the 2nd episode is 0.11). This might be due to females taking more household responsibilities and detouring to accomplish multiple tasks, while returning back to home after work. Older workers commuting to work, are less likely to engage in higher episodes of pick-up/drop-offs, recreational, and eat out activities. However, they depicted propensity towards engaging in higher episodes of personal business and shopping activities. Individuals residing in high income households, are likely to engage in higher episodes of recreational activity, which might be due to their association with financial stability. Similar results were observed for escorting activity. This suggests that individuals commuting daily might engage in household related responsibilities such dropping-off/picking-up children to daycare to/from work. However, they revealed lower propensity towards engaging in multiple episodes of social and personal business activity, revealing work related constraints. Full-time workers commuting daily depicted lower propensity towards escorting and 106 multiple episodes of shopping activity. However, they might be sharing this responsibility with other household members who might be working part-time or from home. They revealed a positive relationship with eat out (i.e. episode specific parameter for the 2nd episode is 0.20). This might be due to going out for lunch/dinner with colleagues and/or family members. Lastly, similar findings were depicted for social activity (i.e. episode specific parameter for the 3rd episode is 0.08), which might mainly occur after working hours. In terms of household-level attributes, the presence of more children revealed a negative relationship for higher episodes of social and shopping activity, as individuals tend to allocate a majority of their time to child-care responsibilities after returning from work. However, they tend to engage in multiple episodes of escorting activity, as they might be required to drop-off/pick-up children to/from school/childcare. Interestingly, such individuals are likely to engage in recreational activity but limit themselves to one episode per day. This finding manifests the need to take children to parks or recreational areas but also the time constraints associated with work, preventing them to engage in recreational activity more than once in a day. The presence of a higher number of vehicles in a household reveal a negative relationship with escorting activity, as expected. However, a positive relationship was retained for the personal business, eat-out, social, and shopping activities. This indicates that the travel freedom gained through increased vehicle ownership encourages individuals to engage in more non-mandatory out-of-home activities after work. This might also reflect the budgetary flexibility of individuals owning more vehicles. Among the built environment attributes, workers residing closer to urban cores are less likely to engage in escorting activities. This might be due to presence of shared mobility services and good public transit accessibility circumventing pick-up/drop-offs. As expected, such residents are likely to engage in social and shopping activity because of closer proximity to destination locations (e.g. 107 shopping centers, cinemas). Similarly, workers residing in mixed land use showed propensity towards engaging in personal business, recreational, and shopping activity. This finding manifests the utility gained from urban characteristics allowing them engage in multiple episodes in a day. Lastly, the magnitudes of satiation parameters controlling the duration of engagement in different out-of-home activities are consistent with corresponding durations presented in Table 6.1. For example, social activity yields the highest parameter estimate, reaffirming that non-telecommuters spend majority of their time socializing after work. On the other hand, the lowest magnitude was observed for escorting activity, as expected, and is consistent with the average duration of the respondents (Table 6.1). 6.5.3. Telecommuters: Baseline Preference and Satiation Parameter Estimates Among the sociodemographic attributes, model results suggest that female telecommuters are likely to engage in higher episodes of personal business activity (i.e. episode specific parameter for the 2nd episode is 0.38) and shopping activity (i.e. episode specific parameter for the 3rd episode is 0.63). This finding reaffirms an understanding that females tend to perform a higher share of household responsibilities such as buying household related supplies or visiting banks while working from home. Moreover, a positive relationship was revealed for the second episode of recreational activity. This might be due to understanding that as females take higher responsibility for household-related activities such as child-care (Astroza et al., 2018), however, to maintain a healthier and active lifestyle they may engage in recreational activities such as swimming, and gym, among others. Older telecommuters are less likely to engage in higher episodes of escorting activity, as they tend to share such household-related responsibilities with their younger counterparts. In addition, they depicted preference towards personal business and shopping activities. On the other hand, results were not the same for recreational activity. Telecommuters 108 residing in higher income households showed propensity towards higher episode of escorting activity (i.e. episode specific parameter for the 3rd episode is 0.34). This finding demonstrates their work schedule flexibility allowing them to perform responsibilities such as picking-up or dropping-off children to day-care. Interestingly, such individuals tend to engage in recreational and shopping activity utilizing their expenditure capabilities to go out to compensate for the increased sedentary lifestyle associated with telecommuting. However, this is restricted to one episode only. Moreover, full-time workers working from home revealed a negative relationship with activities such as social. They are likely to engage in escorting and eat out activities, exploiting the flexibility associated with working from home. In the case of household-level attributes, households with a higher number of children are more likely to engage in higher episodes of escorting activity in a day. Again, this indicates the multiple child-care responsibilities which prompt more escorting episodes (Garikapati et al., 2014). This indicates that telecommuters might be taking the lead in pick-up/drop-off activities often of higher episodes. As they spend the majority of time at work and multiple childcare responsibilities, the propensity to engage in other activities (e.g. personal business, eat-out, social, and shopping) is low, as expected. In terms of vehicle ownership, households with a higher number of vehicles revealed a positive relationship with second episode of escorting activity. This indicates that despite multiple vehicle ownership in a household, telecommuters tend to share household related responsibilities and perform pick-up and drop-offs for their household members. In the case of built environment attributes, telecommuters residing closer to regional-urban cores are more likely to engage in higher episodes of escorting activity. This finding indicates that the presence of urban characteristics such as active transportation networks, accessibility to bus stops, and work-related flexibility, encourage telecommuters to go out and engage in household related 109 responsibilities such as pick-up/drop-off children, compared to their suburban and rural counterparts. Similar findings were observed for recreational activity, manifesting the effect of nearby destinations such as cinemas or parks. Furthermore, individuals living in mixed land use areas revealed propensity towards multiple episodes (up to the 3rd episode) of personal business, eat-out, and shopping activities. This finding manifests that individuals with greater accessibility to nearby destinations and work-related flexibility, engage in multiple non-mandatory activities in a day. Lastly, the magnitudes of satiation parameters controlling the duration of engagement in different out-of-home activities are consistent with corresponding durations presented in Table 6.1. For example, social activity revealed the highest satiation parameter (6.09) which is consistent with the longest time invested in social activity among the sample population of telecommuters. Interestingly, recreational activity yields the second highest satiation parameter estimate of 5.28, reaffirming that telecommuters allocate significant amounts of time to recreational activities too. Meanwhile, escorting activity had the lowest satiation parameter (2.91) with an average duration of 33.4 minutes (Table 6.1). Table 6.4. Parameter estimation results of the MDCEV-OP model for non-mandatory activity participation and duration of telecommuters and non-telecommuters Activity-level 2nd episode 3rd episode Activity-level 2nd episode 3rd episode Telecommuters Non-telecommuters Baseline preference parameters Baseline preference parameters Coefficient (t-stat) Coefficient (t-stat) Baseline preference constants Baseline preference constants Escorting -8.36 (-37.08) - - -8.18 (-64.45) 1.02 (7.01) - Personal Business -8.74 (-33.58) - - -7.93 (-95.37) - - Recreation -8.49 (-26.01) - - -7.90 (-77.36) - - Eat Out -8.79 (-34.06) - - -7.91 (-110.80) - - Social -8.11 (-74.67) - - -7.89 (-150.21) - - Shopping -8.62 (-35.80) - - -7.87 (-112.11) - - Gender: Female Gender: Female 110 Activity-level 2nd episode 3rd episode Activity-level 2nd episode 3rd episode Escorting - - - 0.04 (1.41) - 0.05 (1.00) Personal Business - 0.38 (1.93) - - - 0.12 (2.50) Recreation - 0.75 (2.65) - 0.04 (1.26) - - Eat Out -0.23 (-1.35) - - - - - Social - - - - - - Shopping - - 0.63 (2.98) - 0.11 (2.60) - Age Age Escorting -0.006 (-1.76) - - - -0.008 (-5.48) - Personal Business - 0.01 (2.38) - - 0.004 (1.87) - Recreation -0.003 (-1.00) - - - -0.001 (-1.03) - Eat Out - - - - -0.004 (-1.00) - Social - - - - - - Shopping - 0.006 (1.10) - - - 0.008 (4.58) Annual household income more than $80,000 Annual household income more than $80,000 Escorting - - 0.34 (2.23) - - 0.08 (1.82) Personal Business - - - -0.04 (-1.60) - - Recreation 0.71 (3.21) -1.46 (-1.38) - - 0.45 (3.37) - Eat Out - - - - - - Social - - - - -0.14(-1.52) - Shopping 0.40 (2.11) - -0.75 (-1.85) - - - Full-time workers Full-time workers Escorting 0.18 (1.92) - - -0.06 (2.00) - - Personal Business - - - - - - Recreation - - - - - - Eat Out 0.21 (1.15) - - - 0.20 (1.48) - Social -0.14 (-1.22) - - - - 0.08 (1.01) Shopping - - - - - -0.08 (-1.22) Household with more than 3 children Household with more than 3 children Escorting - - 0.94 (6.57) - - 1.01 (8.59) Personal Business -0.24 (-2.08) - - - - - Recreation - - - 0.13 (2.14) -0.37 (-1.23) - 111 Activity-level 2nd episode 3rd episode Activity-level 2nd episode 3rd episode Eat Out -0.35 (-1.72) - - - - - Social -0.62 (-3.80) - - - - -0.23 (-1.92) Shopping - - -0.61 (-2.03) - - -0.25 (-1.34) No. of vehicles in a household No. of vehicles in a household Escorting - 0.20 (2.29) - - - -0.10 (-3.83) Personal Business - - 0.13 (1.50) - - 0.04 (1.90) Recreation - - - -0.13 (-1.43) - Eat Out - - - 0.04 (2.95) - - Social - - - - 0.06 (2.16) - Shopping - 0.11 (1.63) - - 0.07 (3.95) -0.09 (-1.92) Distance from home to nearest urban core less than 2KM Distance from home to nearest urban core less than 2KM Escorting - - 0.16 (1.00) - -0.05 (-1.55) - Personal Business - - - - - - Recreation - 0.28 (1.06) - - - - Eat Out -0.29 (-1.13) - - - - - Social - - - - - 0.14 (2.09) Shopping - - - 0.04 (1.71) - - Land use mix index Land use mix index Escorting - - - - - Personal Business - - 1.30 (2.99) - - 0.38 (3.48) Recreation - - - 0.07 (1.00) - - Eat Out - - 1.11 (2.11) - - - Social - - - - - - Shopping - - 1.30 (2.81) 0.08 (1.31) - - Satiation parameters Satiation parameters Constant Constant Escorting 2.91 (23.14) 3.37 (54.83) Personal Business 4.72 (29.67) 4.93 (64.90) Recreation 5.28 (19.87) 6.07 (45.78) Eat Out 4.59 (18.47) 5.01 (72.13) Social 6.09 (22.72) 6.62 (53.48) 112 Satiation parameters Satiation parameters Shopping 4.35 (21.33) 4.73 (64.23) Note: “-“ – Insignificant. 6.5.4. Forecasting Analysis The estimated MDCEV-OP model was further applied to detect how the proposed model could replicate the observed activity engagement and duration rates at an episode level. The forecasting procedure adopted in this study was outlined by Saxena et al. (Saxena et al., 2022). The forecasting algorithm was applied to the hold out sample of 200 non-telecommuters, and prediction was performed on its own estimation sample for telecommuters, because of the low sample size. In this study, 50 sets of error draws were simulated to cover the distribution of error terms for each individual. For comparison purposes, in addition to the developed MDCEV-OP model, a disaggregated MDCEV model has been deployed for telecommuters and non-telecommuters. The forecasting accuracy of the models have been compared based on weighted root mean squared error (RMSE) value as follows: 𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝑅𝑀𝑆𝐸 = √(∑ ∑ 𝑜𝑘,𝑗𝑘∗𝑟𝑘,𝑗𝑘2𝑗𝑘𝑘∑ ∑ 𝑜𝑘,𝑗𝑘𝑗𝑘𝑘) (6.6) Where, 𝑟𝑘,𝑗𝑘 is the relative error in predictions and can calculated as 𝑟𝑘,𝑗𝑘 =(𝑝𝑘,𝑗𝑘−𝑜𝑘,𝑗𝑘)𝑜𝑘,𝑗𝑘. Here 𝑝𝑘,𝑗𝑘 and 𝑜𝑘,𝑗𝑘 are the predicted and observed activity engagement (duration) rates for the activity episode (𝑘, 𝑗𝑘). The goodness of fit measure suggests that the MDCEV-OP model outperforms the disaggregated MDCEV model and does well in predicting the observed activity participation and duration rates (Table 6.5). Furthermore, the observed and predicted average activity participation (i.e., episode-level) and duration rates for the MDCEV-OP model are represented in Table 6.6. 113 Table 6.5. Fit measures for the predictions Non-telecommuters Telecommuters MDCEV-OP Disaggregated MDCEV MDCEV-OP Disaggregated MDCEV Weighted RMSE Activity participation 0.12 0.94 0.07 0.98 Activity duration 0.10 0.51 0.05 0.10 The results suggest that the model accurately predicts the general trend in higher participation rates among telecommuters in personal business, recreation, social, and shopping activities compared to non-telecommuters. However, the model predicts a lower participation rate in escorting and eat-out activities among the telecommuters. In the case of accuracy, prediction is within a few percentage points of the observed values. For example, in case of activity participation rates, 29.5% of non-telecommuters participated in the 1st episode of escorting activity whereas, the model’s prediction is 29.2% for the same. Similarly, the difference in average observed and predicted participation rate for 2nd episode of eat out activity for telecommuters is 0.27%. Lastly, in case of activity duration predictions, non-telecommuters engage in 2nd episode of shopping activity for an average of 18.5 minutes whereas, the predicted value is 18.0 minutes. Furthermore, the difference in observed and predicted average durations for telecommuters for the 1st episode of social activity is ~3%. 114 Table 6.6. Observed and predicted average time allocations (participation rates) from the proposed MDCEV-OP model Activity Purpose Duration of 1st episode (participation in %) Duration of 2nd episode (participation in %) Duration of 3rd episode (participation in %) Non-Telecommuters Shopping Observed 36.9 (30.5) 18.5 (6.5) 1.0 (0.5) Predicted 40.8 (27.8) 18.0 (5.8) 9.4 (0.6) Eat Out Observed 55.5 (21.0) 6.3 (2.0) 5.0 (0.5) Predicted 48.0 (19.2) 22.3 (1.9) 4.4 (0.1) Social Observed 173.3 (22.5) 51.3 (2.0) 1.0 (0.5) Predicted 146.3 (18.1) 44.6 (1.1) 0.3 (0.01) Personal Business Observed 51.5 (26.0) 27.6 (5.0) 15.0 (0.5) Predicted 47.9 (21.7) 20.2 (3.4) 7.0 (0.3) Recreation Observed 101.8 (20.0) 65.0 (1.0) 1.0 (0.5) Predicted 107.4 (16.8) 32.3 (1.1) 3.8 (0.1) Escorting Observed 14.8 (29.5) 3.0 (11.0) 5.0 (1.0) Predicted 12.2 (29.2) 5.8 (10.0) 4.3 (2.6) Duration at activity level (participation in %) Outside good Observed 1334.5 (100) Predicted 1354.9 (100) Telecommuters Shopping Observed 64.3 (39.84) 23.7 (9.21) 11.6 (1.90) Predicted 73.0 (35.88) 27.1 (10.12) 18.7 (1.49) Eat Out Observed 70.4 (10.84) 20.0 (0.54) 1.0 (0.27) Predicted 77.8 (10.19) 26.0 (0.81) 5.4 (0.07) Social Observed 214.6 (19.51) 82.2 (2.44) 15.0 (0.27) Predicted 221.3 (18.98) 73.2 (1.50) 4.0 (0.04) Personal Business Observed 138.6 (31.71) 24.3 (6.78) 21.7 (1.63) Predicted 93.0 (29.27) 33.7 (7.13) 17.9 (0.76) Recreation Observed 106.0 (26.29) 61.5 (2.71) 50.0 (0.27) Predicted 138.8 (23.62) 44.0 (2.95) 11.2 (1.19) Escorting Observed 33.5 (26.56) 7.3 (15.45) 3.9 (2.71) Predicted 20.4 (28.97) 8.1 (9.39) 4.9 (3.00) Duration at activity level (participation in %) Outside good Observed 1274.6 (100) Predicted 1288.8 (100) 115 6.6. Concluding Remarks This chapter investigates the activity-time-use patterns of two types of worker profiles: telecommuters and non-telecommuters, focusing on their engagement in non-mandatory activities. A newly proposed MDCEV-OP model deals with activity participation and duration decisions at a disaggregated episode-level while accounting for multiple occurrences of each activity type. A logical ordering of the episodes has been imposed to ensure that lowered numbered episodes occur first (i.e. the jth episode of an activity type is not predicted without the occurrence of the (j-1)th episode of that activity type). Furthermore, this study tests the effect of sociodemographic attributes, vehicle ownership, and built environment attributes on one’s activity time use patterns. The findings of this study confirm that there is a significant difference in activity-time-use patterns of telecommuters and non-telecommuters over a 24-hour period. For instance, in the case of activity participation, female non-telecommuters are more likely to take household-related responsibilities such as pick-up/drop-off of children to school/daycare, as well as running daily errands. They might be undertaking these activities while commuting. In contrast, female telecommuters showed higher propensity towards personal business, recreation, and shopping activities, conceivably due to extra time allocation from not having to commute. The higher propensity towards shopping activities likely comes from household-related responsibilities such as grocery shopping. Interestingly, full-time non-telecommuters are less likely to engage in non-mandatory activities including escorting and shopping, which might be due to time constraints imposed by longer working hours. Although initially, this negative relationship for escorting is somewhat counterintuitive, as full-time workers could pick-up/drop-off household members on their way to work. However, after further investigation, and the positive relationship for full-time 116 telecommuters, it is possible that non-telecommuters might be sharing this escorting responsibility with other household members with more flexible work arrangements. For example, full-time telecommuters are engaging in escorting activity, further implying flexibility associated with working from home and participating in household-related responsibilities. The presence of a higher number of children in the household indicates a lower likelihood to engage in non-mandatory activities for both telecommuters and non-telecommuters, which might be due to the tendency to spend more time with family after work, mostly at-home. Both groups showed positive relationships towards escorting activities, indicating the presence of multiple child-care responsibilities which might stimulate the need to engage in more escorting episodes (Garikapati et al., 2014). In the case of built environment attributes, both non-telecommuters and telecommuters residing closer to urban centers, are more likely to engage in various non-mandatory activities. This finding manifests that accessibility derived from urban settings motivates both the worker profiles to engage in non-mandatory activities. In a similar line of investigation, this study also deals with the activity duration controlled by satiation parameters. The parameter estimation results clearly reflect that, both worker profiles allocate the most time to social activity, followed by recreational activity. As expected, escorting activity shares the lowest satiation parameter estimates, confirming minimal time allocation to pick-up/drop-offs by workers. Overall, the findings of this study reveal that telecommuters are more likely to preform multiple episodes of an activity as compared to non-telecommuters. The findings of this study has several implications. For instance, one of the interesting findings is that telecommuters are more likely to participate in higher episodes of activities related to household-related responsibilities such as escorting and shopping. Therefore, as employers and government agencies adopt policies and 117 develop incentive programs to promote telecommuting, it is important to recognize the possible implications for telecommuters to be likely burdened with more household-related activities compared to their non-telecommuters counterparts. The results also indicate certain market segments could benefit substantially from adopting telecommuting. For example, the tendency of high-income workers (telecommuters and non-telecommuters) to engage in more non-mandatory activities compared to their lower income counterparts indicate a need to evaluate how more affordable and equitable access to such activities for marginalized groups can be created. Lastly, results of built environment variables reveal that more mixed land use development closer to urban cores might increase the non-mandatory activity participation of residents (i.e., telecommuters and non-telecommuters). 118 CHAPTER 7: MODELLING DECISION TO TELECOMMUTE OR NON-TELECOMMUTE AND ASSOCIATED TRAVEL OUTCOMES 7.1. Introduction This chapter aims to investigate work arrangement choices and associated travel outcomes. The work arrangement choices include telecommuting (work from home) and non-telecommuting (working at a regular workplace), and travel outcomes such as non-work related VKT are considered at a tour level. Work arrangement choices may lead to differences in non-work travel which needs to be further explored (Figure 7.1). Moreover, workers possess certain observed and unobserved attributes that encourage them to self-select into a particular work arrangement type and further govern their travel outcomes. Therefore, when estimating the effect of work arrangement on travel behaviour, self-selection is inherent, reaffirming that work arrangement is endogenic in nature. Methodologically, it is important to account for unobserved factors of an individual in a choice modelling process by having a proper structure of the error terms, if such data is not available through travel surveys (Tran et al., 2016). Keeping this in mind, an endogenous switching regression approach is adopted to jointly model work arrangement choices (discrete in nature) and associated travel outcomes (continuous in nature), while controlling for the self-selection bias. Furthermore, to quantify the effect of one’s selection into different types of work arrangement on VKT, this study calculates expected values/treatment effects providing a better interpretation of the results. _____________________________________________________________________________________________Chapter 7 is derived from: Khaddar, S., Varghese, V., Fatmi, M. R., & Chikaraishi, M. (2024) Jointly Modeling Work Arrangements and Vehicle Kilometer Traveled (VKT) for Non-work Travel: An Endogenous Switching Regression Modeling Approach. Published in the fully peer-reviewed proceeding of the 103rd Annual Meeting of Transportation Research Board (TRB), Washington D.C., USA. 119 Lastly, utilizing data from the 2018 OTS, the effect of sociodemographic, vehicle ownership, built environment, and land use characteristics are tested on these choices. Figure 7.1 Example of Travel Patterns 7.2. Methodology 7.2.1. Endogenous Switching Regression Model This section provides a brief discussion of the methodological approach adopted in this study. More specifically, a simultaneous equations model that comprises a discrete decision (whether to telecommute or not) and a continuous outcome (associated VKT consumption) function is adopted in this study. The proposed model describes behaviour of telecommuters and non-telecommuters, and their daily VKT consumptions for non-work home based tours. To do so, this study utilizes endogenous switching regression model which comprises of two parts (Lokshin, 2004; Maddala, 1986), first selection model can be represented as : 𝑈𝑖∗ = 𝑍𝑖𝛼 + 𝜇𝑖 (7.1) 120 Where i represents an individual, 𝑈𝑖∗ is a latent continuous variable depicting utility of the selection function, 𝑍𝑖 is a vector of explanatory variables, 𝛼 is a vector of estimated parameters, and 𝜇𝑖 is the error term. Assuming 𝜇𝑖 ~ 𝑁(0,1), binary probit model with probabilities can be expressed as: 𝑃(𝑈𝑖 = 1|𝑍𝑖) = Φ(𝑍𝑖𝛼) (7.2) 𝑃(𝑈𝑖 = 0|𝑍𝑖) = 1 − 𝑃(𝑈𝑖 = 1|𝑍𝑖) = 1 − Φ(𝑍𝑖𝛼) = Φ(−𝑍𝑖𝛼) (7.3) Where ϕ is the normal cumulative distribution function. Similarly, outcome decision model with two consumption functions can be represented as: 𝑌𝑖1 = 𝑋𝑖𝛽1 + 𝜀𝑖1 (7.4) 𝑌𝑖0 = 𝑋𝑖𝛽0 + 𝜀𝑖0 (7.5) where 𝑌𝑖1, 𝑌𝑖0 represents telecommuters (treated) and non-telecommuters (untreated) daily VKT consumption, respectively. 𝑋𝑖 is a vector of explanatory variables for outcome model, 𝛽0 and 𝛽1 are vectors of estimated parameters, and 𝜀𝑖1 and 𝜀𝑖0 are error terms. One must note that, 𝑌𝑖1 (𝑌𝑖0) is observed only when 𝑈𝑖∗ > 0 (𝑈𝑖∗ ≤ 0). In the given system error terms follow the trivariate normal distribution as follows: (𝜇𝑖𝜀𝑖1𝜀𝑖0) ~𝑁 [(000) , (1 𝜌1𝜎1 𝜌0𝜎0𝜌1𝜎1 𝜎12 0𝜌0𝜎0 0 𝜎02)] (7.6) The variance of 𝜇𝑖 is fixed as 1 for convenience and identification purposes. Whereas 𝜌1 𝑎𝑛𝑑 𝜌0 represent the correlation of the unobserved variables influencing selection decision with those influencing the respective behavioural outcomes i.e., VKT for telecommuters and non-telecommuters. Furthermore, covariance between 𝜀𝑖1 𝑎𝑛𝑑 𝜀𝑖0 are defined as zero since an individual can only be identified as telecommuter or non-telecommuter, and thus the correlation is 121 unidentifiable. The choice of zero is purely for convenience and does not impact the estimation process. In addition, 𝜀𝑖1 𝑎𝑛𝑑 𝜀𝑖0 are defined over the entire population as correlations are allowed with 𝜇𝑖 which is also defined over the entire population. Keeping the above discussion in mind, the log-likelihood function can be written as follows (Kim and Mokhtarian, 2023a): 𝐿𝐿 = ∑ 𝑙𝑛 [Φ (𝑍𝑖𝛼 + 𝜌1(𝑦𝑖 − 𝑋𝑖𝛽1)/𝜎1√1 − 𝜌12) ∗ (1𝜎1𝜙 ((𝑦𝑖 − 𝑋𝑖𝛽1)𝜎1))]𝑢=1+ ∑ 𝑙𝑛 [Φ (−𝑍𝑖𝛼 + 𝜌0(𝑦𝑖 − 𝑋𝑖𝛽0)/𝜎0√1 − 𝜌02) ∗ (1𝜎0𝜙 ((𝑦𝑖 − 𝑋𝑖𝛽0)𝜎0))]𝑢=0 (7.7) Where Φ , 𝜙 denotes normal cumulative distribution function and normal probability density function, respectively. Finally, the full information maximum likelihood (FIML) process was utilized for estimation purposes as it allows error correlations subject to the constraint -1 ≤ ρ ≤ 1 with a proper reparameterization and better efficiency in estimation (Kim et al., 2015). Detailed information about the methodology can be found here (Cameron and Trivedi, 2005). 7.2.2. Expected Values This study also estimates expected conditional and unconditional values. One must note that, in such cases the continuous outcome dependent variable (i.e., VKT) is log-transformed to meet the normality assumption. However, results might be nuanced while calculating expected values/treatment effects when the log-transformed dependent variable is back-transformed to its original units. To overcome this limitation, the present study adopts newly proposed set of equations to calculate expected values and treatment effects which can be applied to endogenous switching regression model when the dependent variable is log-transformed. For brevity, adopted 122 set of final equations is presented below, and detailed derivation of the equations can be found here (Kim and Mokhtarian, 2023a). Furthermore, 𝜌1𝜎1 (𝜙(𝑍𝑖𝛼)Φ(𝑍𝑖𝛼)) serves as the bias correction term for the treated group i.e., the telecommuters, whereas 𝜌0𝜎0 (−𝜙(𝑍𝑖𝛼)1−Φ(𝑍𝑖𝛼)) serves as the bias correction term for the untreated group i.e., the non-telecommuters. Incorporation of these terms will ensure the calculation of desired unbiased differences between both groups. Conditional expected value: exp(𝑋𝑖βu +σu22⁄ ) • Conditional expected value for the treatment group: exp (𝑋𝑖𝛽1 +𝜎122⁄ )Φ(𝑍𝑖𝛼+𝜌1𝜎1)Φ(𝑍𝑖𝛼) • Conditional expected value for the untreated group: exp (𝑋𝑖𝛽0 +𝜎022⁄ )Φ(−𝑍𝑖𝛼−𝜌0𝜎0)Φ(−𝑍𝑖𝛼) • Counterfactual expected value for the treatment group: exp (𝑋𝑖𝛽1 +𝜎122⁄ )Φ(−𝑍𝑖𝛼−𝜌1𝜎1)Φ(−𝑍𝑖𝛼) • Counterfactual expected value for the untreated group: exp (𝑋𝑖𝛽0 +𝜎022⁄ )Φ(𝑍𝑖𝛼+𝜌0𝜎0)Φ(𝑍𝑖𝛼) 7.2.3. Treatment Effects In terms of treatment effects, a few concepts are clarified here. In this regard, the average treatment effect (ATE) is defined as the average change in VKT if a randomly selected individual shifts from commuting to telecommuting. Therefore, ATE can be defined as 𝐸(𝑌𝑖1|𝑋𝑖) − 𝐸(𝑌𝑖0|𝑋𝑖), where 𝑌𝑖1 is the continuous outcome (i.e., VKT) for the individual i who is treated (i.e., telecommuting). On the other hand, 𝑌𝑖0 is the continuous outcome (i.e., VKT) for the individual i who is untreated (i.e., commuting), and E is the expectation operator. Furthermore, the average treatment effect on the treated (TT) is the expected change in VKT for those who have now become telecommuters, relative to what the outcome would have been if they continued to be non-telecommuters. TT can 123 be expressed as 𝐸(𝑌𝑖1|𝑈𝑖∗) > 0, 𝑋𝑖 , 𝑍𝑖) − 𝐸(𝑌𝑖0|𝑈𝑖∗) > 0, 𝑋𝑖 , 𝑍𝑖). Finally, the average treatment effect on the untreated (TUT) can be defined as expected difference in VKT for those individuals who now commute, if they were to become telecommuters (i.e., expected change in VKT for the current commuters as compared to a counterfactual scenario where they became telecommuters). Hence, TUT can be represented as 𝐸(𝑌𝑖1|𝑈𝑖∗) ≤ 0, 𝑋𝑖 , 𝑍𝑖) − 𝐸(𝑌𝑖0|𝑈𝑖∗) ≤ 0, 𝑋𝑖 , 𝑍𝑖). The equations mentioned below provides information on calculating the treatment effects in this empirical context. • ATE: 1𝑛∑ exp (𝑋𝑖𝛽1 +𝜎122⁄ ) − exp (𝑋𝑖𝛽0 +𝜎022⁄ )𝑛𝑖=1 (7.8) • TT: 1𝑛∑ exp (𝑋𝑖𝛽1 +𝜎122⁄ )Φ(𝑍𝑖𝛼+𝜌1𝜎1)Φ(𝑍𝑖𝛼)− exp (𝑋𝑖𝛽0 +𝜎022⁄ )⬚𝑢=1Φ(𝑍𝑖𝛼+𝜌0𝜎0)Φ(𝑍𝑖𝛼) (7.9) • TUT: 1𝑛∑ exp (𝑋𝑖𝛽1 +𝜎122⁄ )Φ(−𝑍𝑖𝛼−𝜌1𝜎1)Φ(−𝑍𝑖𝛼)− exp (𝑋𝑖𝛽0 +𝜎022⁄ )⬚𝑢=0Φ(−𝑍𝑖𝛼−𝜌0𝜎0)Φ(−𝑍𝑖𝛼) (7.10) 7.3. Descriptive Analysis This study utilizes data from a 2018 OTS where, non-work home based tour level data was extracted for telecommuters and non-telecommuters. More specifically, activity chains are identified to form tours based on their travel purpose. The focus of this study was to understand non-work home based tours (tours start at home and end at home for non-work related travel only) for both the worker profiles. Where non-work travel purposes include: escorting (pickup/drop-off), personal business (going to bank, going to see doctor, etc.), recreation (engaging in sporting activities, gym, etc.), eat-out (dine-in at a restaurant), social (meeting families, attending civic/community events, etc.), and shopping (buying pantry essentials, clothes etc.). Lastly, to measure VKT, Google Maps API was utilized to calculate the distance between the origin and destination of each trip which was further aggregated to generate tour level VKT. The final sample 124 size includes 527 and 723 non-work home based tours only made by 369 telecommuters and 723 non-telecommuters, respectively. In this case, the share of individuals working from home (telecommuters) is 42.16%. On the other hand, the share of individuals working from regular workplace (non-telecommuters) is 57.84%. Furthermore, telecommuters reveal higher VKT rates as compared to non-telecommuters (Table 7.1). Table 7.1. Summary of Dependent Variables Dependent Variables Definition Mean/ Distribution SD Telecommuters Dummy, if an individual is telecommuting =1, 0 otherwise 42.16% - Non-telecommuters Dummy, if an individual is non-telecommuting =1, 0 otherwise 57.84% - Ln (Telecommuters_VKT) Log of vehicle kilometre travelled by telecommuters for non-work home based tours 2.73 1.05 Ln (Non-telecommuters_VKT) Log of vehicle kilometre travelled by non-telecommuters for non-work home based tours in km 2.37 0.91 Note: SD- standard deviation 7.4. Independent Variables Tested The model tests the influence of sociodemographic attributes (e.g., age, gender, income, employment status, occupation type, dwelling type), travel characteristics (e.g., number of stops in a tour), vehicle ownership attributes (e.g., number of vehicles), land use attributes (e.g., land use mix index, share of residential, commercial, industrial, and other areas), accessibility measures (e.g., distances from home to the nearest urban center, bus stop, point of interest), and built environment attributes (e.g., length of bike lane and sidewalk), among others. The built environment and land-use attributes were generated for 250-metre buffer area based on the respondent’s home location, whereas accessibility measures were generated based on the road 125 network-based distance from their home location using ArcGIS (v.10.6.1). The summary statistic of the variables retained in the final model are presented in Table 7.2. Table 7.2. Descriptive statistics of the variables retained in the final model Variable Description Mean/ Distribution SD Independent variables Age Age of an individual 46.38 12.62 Age x Female Age of an individual x if an individual is female 24.81 24.63 Age square Age square of an individual 2310.47 1192.44 Age square x Female Age square of an individual x if an individual is female 1222.35 1399.86 Full-time worker x Female Dummy, if a full-time worker is female =1, 0 otherwise 33.20% - Annual household income more than $125,000 Dummy, if an individual’s annual household income is more than $125,000 =1, 0 otherwise 30.16% - Business/Management related occupations Dummy, if an individual’s occupation is management/business/finance/administration related =1, 0 otherwise 70.40% - Business/Management related occupations x Annual household income more than $125,000 Dummy, if an individual’s occupation is management/business/finance/administration related and have annual household income more than $125,000 =1, 0 otherwise 21.92% - Single detached dwelling Dummy, if an individual resides in single detached dwelling =1, 0 otherwise 70.32% - Row house/Townhouse/ Semi-detached dwelling Dummy, if an individual resides in row house/townhouse/semi-detached dwelling =1, 0 otherwise 13.52% - Number of stops in a tour Number of stops made by an individual while engaging in non-work home based tours 1.35 0.91 Number of vehicles in a household Number of vehicles present in a household 2.15 0.97 Ln (Land use mix index in the neighborhood) Log of land use mix index within a 250 m buffer from the home location -0.98 0.56 Land use mix index in the neighborhood Land use mix index within a 250 m buffer from the home location 0.41 0.17 Distance to nearest urban center from home Distance to the nearest urban center from residential location of an individual in km 3.91 3.52 126 Variable Description Mean/ Distribution SD Length of available active transportation network in the neighborhood Length of bike lane and sidewalk within a 250 m buffer from the home location in km 1.03 0.96 Distance to nearest activity points Distance to the nearest point of interest from residential location of an individual in km 0.97 1.53 Note: SD- standard deviation 7.5. Model Results This section highlights the parameter estimation results of the variables retained in the final model. Furthermore, marginal effects and treatment effects are discussed in the later sections. To evaluate the proposed joint model, multiple goodness-of-fit measures such as log-likelihood value, Akaike Information Criterion (AIC) value, and Bayesian Information Criterion (BIC) value were deployed. For comparison purposes, this study also estimates only constant endogenous switching regression model with correlated error terms. The goodness-of-fit measures demonstrate that the proposed model outperforms the only constant model hence, further discussion is based on the proposed model only (Table 7.3). 7.5.1. Parameter Estimation Results: Selection Model The selection component of the endogenous switching regression approach uses a binary probit model. It was deployed to account for selection bias as individuals telecommuting often pose different characteristics than their non-telecommuting counterparts. The parameter estimation results are depicted in Table 7.3. In case of selection model, among socio-demographic attributes, age was introduced both as an independent continuous and as a squared variable to capture linear and non-linear relationships. Results suggest propensity towards telecommuting raises with age up 127 to a certain extent and then reduces. This might be due to the inclination towards and/or familiarity with ICT (internet usage, computer/smartphone usage, website/app browsing, etc.) of young workers. However, this might not be the case for older individuals because they often struggle with technology, and the adoption of working from home at that age adds time constraints which reduces their overall productivity. On the other hand, results were not the same for older females. Here results reveal that likelihood towards telecommuting decreases with age up to a certain extent and then increases. This finding suggests that females are more inclined towards working from home after certain age due to household related responsibilities such as child-care. However, at young age females choose to be non-telecommuters. In terms of household-level attributes, individuals with annual household income of $125,000 and above showed higher propensity towards selecting telecommuting. This implies that high-income individuals who are often at a higher rank in their workplace may have the added negotiating power with their employers, and manage flexible jobs with work from home opportunities. However, this might not be true for certain occupation types. For instance, it was observed that individuals with business/management related occupations are less likely to telecommute irrespective of their income-level. This finding shows that certain occupation types such as business/management have spatial constraints that require individuals to have face-to-face interactions with clients and be present at work. In terms of dwelling type, results suggest that single-detached dwellers are more likely to choose telecommuting. This reflects that single-detached dwellings are often equipped with more space (e.g., extra room for home office) making it more suitable for telecommuting, as compared to their counterparts such as apartments. Moreover, such residents may also choose to telecommute to avoid longer commute distances as single-detached dwellings are usually located in sub-urban neighborhoods. On the contrary, a negative relationship was observed for row 128 house/townhouse/semi-detached dwelling type, which reflects space related constraints circumventing telecommuting option. Furthermore, a negative relationship was observed between vehicle ownership and telecommuting. This finding suggests that access to transport-related resources (i.e., owning vehicles) provides higher flexibility and may make it more convenient for workers to travel to work daily rather than telecommuting. Lastly, among built environment variables, the land use mix index depicts a negative relationship with telecommuting. This suggests that individuals residing in areas with a heterogenous land use mix are exposed to urban characteristics offering multi-modal travel opportunities, well-connected active transportation networks, and easy access to transit stops; which may encourage individuals to travel to work. 7.5.2. Parameter Estimation Results: Outcome Model The parameter estimation results of the outcome model with two functions are presented in Table 7.3. One must note that both the functions have same set of explanatory variables to easily estimate expected values/treatment effects (Chen et al., 2017). Therefore, statistically significant variable in either of function was retained in the final model. In terms of results, females working full time from home are less likely to travel longer distances for non-work home based tours. This finding suggests that females may engage more in household related responsibilities generating lower VKTs, perhaps their male counterparts may engage in such tours while commuting or after working from home. However, results are not the same for full time female workers who commute to work. Furthermore, individuals in business/management related occupation who work from home generate larger non-work VKT. In terms of dwelling type, telecommuters residing in single detached dwellings are negatively associated with larger VKTs. This might be due to an understanding that larger households often share household related responsibilities among themselves which can circumvent need to travel and generate lower VKT rates. However, results 129 were not the same for non-telecommuters, as expected. As consistent with the literature, telecommuters with higher vehicle ownership are linked with more VKTs. Interestingly, to examine whether trip chaining can further provide acceptable reductions in VKT or not, the study also considers the effect of tour characteristics. Results reveal that tours with higher number of stops (multiple destinations) depicted positive relationship with generated VKT for both telecommuters and non-telecommuters, which might be due to choosing farther destinations from home. In addition, results suggest that non-work home based tour VKT is also governed by residential location attributes such as built environment and land use patterns (Table 7.3). Variables such as distance to nearest activity points, distance to closest urban center from home depicted positive relationship with non-work related VKT for both telecommuter and non-telecommuter groups. This reaffirms that living in sub-urban or rural areas do not offer better destination accessibility as compared to an urban setting, making them travel longer distances and choose farther destinations to engage in an activity. On the contrary, a negative relationship was observed for the variable length of available active transportation networks in the neighborhood. This reflects the presence of accessible pedestrian infrastructure in the neighborhoods may encourage individuals to walk more and longer; thus, generating lower VKTs for both telecommuters and non-telecommuters. 7.5.3. Parameter Estimation Results: Error Correlation Lastly, statistically significant error correlation estimates confirm that selection of work arrangement type and VKT generation share notable unobserved attributes. Therefore, accounting them can circumvent inconsistent and biased estimation results. Interestingly, the study reveals presence of heterogeneity among the worker profiles, implying that unobserved factors have a different impact on one than the other (Kim and Mokhtarian, 2023b). Results suggest that 130 unobserved factors that increases the likelihood to telecommute decreases the propensity to generate higher VKT for non-work-related tours, and vice versa. These unobserved factors may include telecommuter’s attitudes/preferences such as dislike towards congestion and/or driving, travel time/cost saving, pro-environment friendly, and other lifestyle motivations. On the contrary, results reveal that unobserved factors that increases the propensity to commute also increases the propensity to generate higher VKT for non-work-related tours, which might be due to attitudes/preferences such as pro-driving attitudes. Table 7.3. Parameter Estimation Results Telecommuting (Class 1) Non-telecommuting (Class 0) Coefficient (t-stat) Coefficient (t-stat) Outcome Model Constant 2.52 (10.94) 2.43 (7.46) Female x Full time worker -0.15 (-1.65) 0.07 (1.15) Business/Management related occupations 0.21 (2.15) -0.33 (-1.54) Single detached dwelling -0.36 (-3.21) 0.04 (0.63) Number of vehicles in a household 0.09 (2.00) -0.01 (-0.30) Number of stops in a tour 0.07 (1.96) 0.34 (7.72) Land use mix index in the neighborhood 0.32 (1.22) -0.02 (-0.10) Distance to nearest urban center from home 0.06 (5.17) 0.05 (4.18) Length of available active transportation network in the neighborhood -0.16 (-3.15) -0.13 (-3.76) Distance to nearest activity points 0.05 (2.17) 0.02 (0.77) Selection Model Constant -0.05 (-0.11) - Age -0.02 (-1.30) - Age x Female 0.01 (1.52) - Age square 0.0005 (2.31) - Age square x Female -0.0002 (-1.50) - Annual household income more than $125,000 1.86 (8.70) Business/Management related occupations x Annual household income more than $125,000 -2.22 (-9.91) - Single detached dwelling 0.16 (1.51) - 131 Telecommuting (Class 1) Non-telecommuting (Class 0) Row house/Townhouse/ Semi-detached dwelling -0.19 (-1.45) - Number of vehicles in a household -0.16 (-3.94) - Ln (Land use mix index in the neighborhood) -0.11 (-1.40) - Additional Parameters Sigma 0.98 (29.17) 0.89 (18.33) Error Correlation -0.24 (-2.22) 0.48 (2.86) Goodness-of-fit Measures Proposed model N 1250 Log-likelihood value -2370.50 AIC 4811.00 BIC 4949.39 Constant only model N 1250 Log-likelihood value -2585.92 AIC 5185.84 BIC 5193.51 7.5.4. Marginal Effects Marginal effects are calculated for the outcome model to further quantify the parameter estimation results (Table 7.4). Marginal effects are calculated using the following equation: (eβ - 1)*100, where β is the estimated coefficient for the outcome model (Kim and Mokhtarian, 2023b). The values in Table 7.4 denotes “the percentage change in VKT associated with a one-unit increase for continuous variable”, and “the percentage change in VKT associated with switching from 0 to 1 for dummy variables”. Although the estimated signs of the coefficients are very similar in the outcome model, there are some substantial differences in terms of magnitude. The results suggest that full time female workers who choose to telecommute can reduce their VKT by 13.9%. On the contrary, increase in vehicle ownership in a telecommuting household may generate more non-132 work-related VKT by 9.4%. Similarly, increase in number of stops in a tour increases the VKT of non-telecommuters by 40.5%, where this increase is only 7.3% for telecommuters. On the other hand, increase in the length of available active transportation networks in the neighborhood of telecommuters substantially decreases their VKT by 14.8%, whereas this reduction was 12.2% for non-telecommuters. Lastly, increase in distance to nearest urban center from home increases the VKT of telecommuters and non-telecommuters by 6.2% and 5.0%, respectively. Table 7.4. Marginal Effects Telecommuting (Class 1) Non-telecommuting (Class 0) Female x Full time worker -13.9 7.3 Business/Management related occupations 23.4 -28.1 Single detached dwelling -30.2 4.1 Number of vehicles in a household 9.4 -1.0 Number of stops in a tour 7.3 40.5 Land use mix index 37.7 -2.0 Distance to nearest urban center from home 6.2 5.1 Length of available active transportation network in the neighborhood -14.8 -12.2 Distance to nearest activity points 5.1 2.0 Note: Bold indicates 90% confidence level and above. 7.5.5. Treatment Effects Based on the parameter estimation results, ATE, TT, TUT are calculated. In this study the estimated ATE is 5.5 km. This suggests that a randomly selected individual is expected to increase his/her daily non-work related VKT by 5.5 km when they transition from commuting to telecommuting (with the average VKT of random individual telecommuting and commuting is estimated at 32.2 and 26.7 km, respectively). This calculation is substantially less than the observed average VKTs for both the worker profiles, implying that not controlling for self-selection effect may lead to overestimated treatment effect (i.e., the effect of transitioning from 133 commuting to telecommuting). Interestingly, TT was estimated to be -14.0 km, suggesting that individual telecommuting is expected to travel less by 14 km for non-work home based tours than the individuals who are commuting, if all other attributes remain same. Lastly, the estimated value for TUT is 23.4 km. This reveals the expected difference in VKT for those individuals who now commute, if they were to become telecommuters. By carefully controlling for self-selection effects, this study establishes that on average telecommuting would lead to an increase in VKT for non-work related travel. These results undermine the telecommuting policy push as a panacea for solving transport-related problems. 7.6. Concluding Remarks This chapter aims to explore interdependencies among work arrangements (telecommuting and commuting) and non-work home based tour VKTs. Methodologically, the study adopts an endogenous switching regression approach to model these choice dimensions where the selection function accounts for telecommuting (treated) and commuting (untreated), and outcome function deals with the associated VKTs of these two worker profiles. The study explores the influence of sociodemographics, vehicle ownership, land use and built environment characteristics on these choice dimensions. Furthermore, to quantify the effect of work arrangement on non-work-related VKT, expected values/treatment effects are calculated. To estimate these effects a newly proposed set of equations is adopted that is suitable for such modelling techniques when the dependent variable (VKT) is log-transformed and needs to be back-transformed to its original units. The model results confirm the presence of interdependencies among the selection and outcome dimensions. This reaffirms that workers possess certain habits, attitudes, preferences, motivations, and predispositions, among others that can encourage them to self-select into certain work arrangement type and govern their daily travel behaviour. 134 In terms of implications, results reveal heterogeneity among older and younger individuals (and females) while self-selecting into telecommuting. This demonstrates need to account for such differences while creating work from home related plans and policies for certain population groups. Interestingly, single detached dwellers revealed higher propensity towards telecommuting. Therefore, willingness to telecommute can encourage workers to move to single detached dwellings as it offers amenities like home offices, low space constraints which can further lead to the reconsidering of residential location. Typically, single-detached dwellings are prevalent in the sub-urban areas. To prevent generation of higher non-work related VKT from such sub-urban areas, urban areas should offer a mix of housing options and/or multi-family developments with co-working spaces. Moreover, the idea that telecommuting reduces VKT maybe relevant for commute to work travels, and not necessarily it also indicates a reduction in VKT for non-work travels. For example, the results reveal that an individual shifting from commuting to telecommuting may increase his/her daily non-work VKT by around 5.5 km. Although this result maybe relevant to the local context as Okanagan is a highly vehicle dependent region in Canada, still this indicates a need to further understand non-work travels of telecommuters. In this line of investigation, it appears that residential location plays a critical role. Results of the residential location-related important policy variables such as land use mix index, availability of bike/walk infrastructure, and distance to activity destinations reveal tendency of sub-urban/rural dwellers to telecommute and have higher VKT for non-work travels. This may be the result of higher car use of these sub-urban dwellers, as these neighborhoods are not well connected by transit and other active transportation infrastructure, nor they are closer to destinations. These results suggest to develop neighborhoods targeting to increase their land use indices with close by destinations and well-connected active transportation infrastructure. The evidence generated by 135 this study suggests to re-evaluate a comprehensive set of plans and policies, which advocate for the consideration of telecommuting strategies with other sustainable transportation and land use-related measures. 136 CHAPTER 8: MODELLING PREFERENCES TOWARDS TELECOMMUTING AND TELESHOPPING IN THE POST-PANDEMIC ERA 8.1. Introduction This chapter aims to investigate preferences towards telecommuting and teleshopping in the post-pandemic context. The COVID-19 pandemic has caused drastic changes in day-to-day mobility patterns and lifestyles (De Vos, 2020). The countermeasures adopted due to the COVID-19 pandemic encouraged individuals to adopt teleactivities instead of in-person activity participation. Due to the continuous engagement in teleactivities during the pandemic (for over a year), individuals have become accustomed to this ‘new normal’ (Shen et al., 2022). However, it is important to know if they will continue to engage in teleactivities in the post-pandemic period or prefer returning to the pre-pandemic culture. Therefore, this chapter examines the effect of previous activity engagement behaviour, such as the frequency of online activity engagement, and related experiences during different time points – i.e., pre-pandemic (i.e., before March 2020), early stages of pandemic (i.e., during March 2020), and later stages of pandemic (i.e., during the survey which was November 2020 to January 2021) on post-pandemic choices. This chapter extends the understanding of how teleshopping and telecommuting are interconnected and can influence each other. To understand the effect of unobserved factors jointly influencing telecommuting and teleshopping engagement behaviour, this chapter adopts a Multivariate Ordered Probit (MVOP) modelling framework. In addition, this chapter considers possible _____________________________________________________________________________________________Chapter 8 is derived from: Khaddar, S., & Fatmi, M. R. (2024). Modeling Telecommuting and Teleshopping Preferences in the Post-Pandemic Era. Transportation Research Record. https://doi.org/10.1177/03611981231222238 137 endogenous effects among the teleactivities, exploring the effect of one life domain, such as working from home, on another life domain, such as online grocery shopping and online food ordering. Moreover, this study tests the effect of sociodemographic, accessibility to different travel modes, and built environment attributes. Overall, this chapter explores the preferences towards online grocery shopping, online food orders, and working from home in the post-pandemic context. The data comes from a web-based survey conducted in the Okanagan region of British Columbia, Canada from November 2020 to January 2021. 8.2. Methodology This chapter utilizes the MVOP framework to investigate the post-pandemic frequency of online grocery shopping, online food orders, and working from home. The model deals with three dependent variables measured on an ordinal scale: 0 (never/few times a year), 1 (few times a month), and 2 (few times a week/every day). Let i be an index for individuals (i = 1, 2, …., I) and q be the index for ordered variables (q = 1,2…., Q), where Q is the total number of dependent variables for an individual i. Let the observed level for individual i and variable q be 𝑌𝑖𝑞, and let the number of ordinal levels for the dependent variables be 𝐾𝑞. Instead of modelling the frequency directly, ordered response models assume that there is an underlying latent continuous variable Y* depicting online grocery, online food order, and work from home propensity with respect to frequency (Zhen et al., 2016): 𝑌𝑖𝑞∗ = 𝛽𝑞𝑋𝑖𝑞 + 𝜀𝑖𝑞, 𝑌𝑖𝑞 = 𝑘 𝑖𝑓 𝜇𝑞𝑘 < 𝑌𝑖𝑞∗ < 𝜇𝑞𝑘+1 (8.1) Where, 𝛽𝑞 is a vector of parameters associated to 𝑋𝑖𝑞, 𝑋𝑖𝑞 is a vector of explanatory variables, and 𝜀𝑖𝑞 is the unobserved error terms, and 𝜇𝑞𝑘 is the lower bound threshold for frequency category k of the dependent variable q. In this study, the model allows correlation in the 𝜀𝑖𝑞 terms across the 138 variables q for each individual i. 𝜀𝑖 is multivariate normally distributed if 𝜀𝑖 = (𝜀𝑖1, 𝜀𝑖2, … . . 𝜀𝑖𝑄), with a mean vector of zero and a correlation matrix ∑. The off-diagonal terms of ∑ capture the error covariance across the underlying latent continuous variables of the different ordinal variables which can be formulated as (Bhat and Srinivasan, 2005): ∑ = [1 𝜌12 𝜌13𝜌21 1 𝜌23𝜌31 𝜌32 1] (8.2) They account for the effect of common unobserved factors influencing the propensity of each teleactivity engagement. This study also utilized the simultaneous equation modelling approach to test the endogeneity as exhibited in Kim and Wang (Kim and Wang, 2021). In this regard, simultaneous endogenous effect can be determined as follows: 𝑌𝑖1∗ = 𝛽′1𝑌𝑖2∗ + 𝛽1𝑋𝑖1 + 𝜀𝑖1 (8.3) 𝑌𝑖2∗ = 𝛽′2𝑌𝑖1∗ + 𝛽2𝑋𝑖2 + 𝜀𝑖2 (8.4) Further, the parameter vector of the multivariate ordered probit model can be represented as: 𝛿 = (𝛽1, 𝛽2, … 𝛽𝑄; 𝜇1, 𝜇2, … . , 𝜇𝑄; 𝑅) (8.5) Where, 𝜇𝑞 = (𝜇𝑞1 , 𝜇𝑞2, … . , 𝜇𝑞𝐾𝑞) for q = 1, 2,…., Q and R denote the off-diagonal terms of the error correlation matrix. Assume the actual observed response level for individual i and dependent variable q be 𝑚𝑖𝑞. Therefore, the likelihood function (L) for an individual can be represented as: 𝐿𝑖(𝛿) = Pr(𝑌𝑖𝑞 = 𝑚𝑖1, 𝑦𝑖2 = 𝑚𝑖2, … . , 𝑦𝑖𝑄 = 𝑚𝑖𝑄) (8.6) This study accounts for the correlation by estimating the MVOP model through CMP module in Stata 15.0 (Roodman, 2011). The estimation of the model requires a simulation approach using 139 Geweke-Hajivassiliou-Keane (GHK) simulator (Groh and Ziegler, 2018). Furthermore, 200 Halton draws are used in the GHK simulator (Dupont and Renzetti, 2013). Figure 8.1 depicts the framework conceptualized in this study. The hypothesis behind this framework is engagement in one teleactivity type, for instance, telecommuting (i.e., working from home) can influence the propensity to engage in other activity such as teleshopping (i.e., online grocery shopping, online food orders), and vice versa. Keeping this in mind, the study tests the endogenous effects of one decision on another, denoted through solid arrows. In addition to this, these choices are also governed by presence of common unobserved factors that jointly influences each other’s propensity to engage in these teleactivities, which is accommodated in the proposed model, denoted through dashed arrows. Figure 8.1 Conceptual Framework 8.3. Descriptive Analysis This chapter utilizes data from a web-based survey conducted to capture preferences towards different teleactivities in the post-pandemic context. The final sample size used in this study was 197, post-data cleaning and removing missing values. Figure 8.2 illustrates the proportion of respondents 140 engaging in online grocery shopping, online food orders, and working from home few times a week/everyday during the pre-pandemic (i.e., before March 2020), early stages of pandemic (i.e., during March 2020), and post-pandemic periods. It is evident that a substantial rise in telecommuters was observed during the early stages of the pandemic as compared to the pre-pandemic era. Regarding the post-pandemic situation, more individuals engaged in work from home, depicting their continued preference towards telecommuting. In terms of online food orders, a sudden increase in consumers was noticed during the early stages of the pandemic as compared to the pre-pandemic period. However, for the post-pandemic situation, a reduction in online food orders is noticed as compared to later stages of pandemic, indicating their desire to return to dining-in and socializing. Lastly, an increase in online grocery shopping was observed in the early stage of the pandemic as compared to the pre-pandemic period. Furthermore, Figure 8.3 depicts the distribution of individuals engaging in telecommuting and teleshopping activities in the later stage of the pandemic. Here they reported their frequency of teleactivity participation in the past 7 days when the survey was taken. The largest participation was noticed for individuals working from home, with almost 25% of the respondents telecommuting 5 or more days per week. On the contrary, 17.3% of the individuals perform online grocery shopping once per week, whereas the share of individuals performing online food orders at the same rate was 18.5%, as expected. 141 Figure 8.2 Distribution of Individuals who Engaged in Online Grocery Shopping, Online Food Ordering, and Work from Home Few times a week/Everyday Figure 8.3 Distribution of Individuals who Engaged in Online Grocery Shopping, Online Food Ordering, and Work from Home in the Later Stage of the Pandemic (in past 7 days of survey) 8.4. Independent Variables Tested The model tests the influence of past teleactivity engagement frequency (e.g., teleactivity engagement rates at different time points), teleactivity related experiences (e.g., poor internet, website navigation), sociodemographic attributes (e.g., age, gender, income, education, household 142 tenure type, household size), access to travel modes (e.g., vehicle ownership), and built environment attributes (e.g., land use mix index, available length of sidewalk/bike lane), among others. Moreover, built environment attributes were generated for 500-metre buffer areas from the home location of the respondents using ArcGIS (v.10.6.1). Lastly, in case of accessibility measures, distance to various points of interest from respondent’s home location is calculated using network analysis tool of ArcGIS. The summary statistics of the variables retained in the final model are presented in Table 8.1. Table 8.1 Descriptive Statistics of the Explanatory Variables Variables Definition Mean/ Distribution SD Previous Online Activity Engagement Frequency Pre-pandemic x Early Stage of pandemic x Later Stage of pandemic Online Grocery Shopping Never x Participation x Participation If individual never participated before pandemic, but participated in early and later stage of pandemic =1, else 0 4.56% - Participation x Participation x Participation If individual participated before, early and later stage of pandemic =1, else 0 18.27% - Online Order of Food Participation x Participation x Participation If individual participated before, early and later stage of pandemic =1, else 0 31.97% - Work from Home Never x Participation x Participation If individual never participated before pandemic, but participated in early and later stage of pandemic =1, else 0 11.67% - Participation x Participation x Participation If individual participated before, early and later stage of pandemic =1, else 0 25.88% - Previous Online Activity Engagement Experience Sporadically website is difficult to navigate If individual sporadically faces difficulty in website navigation = 1, else 0 31.97% - Work from home is highly productive If individual finds working from highly productive = 1, else 0 11.16% - 143 Variables Definition Mean/ Distribution SD Always Experience poor internet If individual always experiences poor internet = 1, else 0 35.53% - Sociodemographic Attributes Age 18 to 34 If individual’s age is 18 to 34 = 1, else 0 23.35% - Age 40 to 54 If individual’s age is 40 to 54 = 1, else 0 31.97% - Age 65 and above If individual’s age is 65 and above = 1, else 0 12.69% - Annual household income $50,000-$80,000 x Single_Detached_Dwelling If individual’s annual household income $50,000-$80,000 and lives in a single detached dwelling = 1, else 0 6.59% - Annual household income $80,000-$100,000 x Gender_Female If individual’s annual household income $80,000-$100,000 and is a female = 1, else 0 10.15% - Household size of 2 children or more If a household has more than 2 children = 1, else 0 5.07% - Education level_Bachelors_and_Above x Occupation_Management If individual’s education level is bachelors and above, and works in management related occupations = 1, else 0 3.04% - Education level_Bachelors_and_Above x Occupation_Natural_and_Applied_Science If individual’s education level is bachelors and above, and works in natural and applied science related occupations = 1, else 0 5.58% - Accessibility to Travel Modes and Built environment No vehicle in a household If there is no vehicle in a household = 1, else 0 4.56% - Own Vehicle x Own house If individual owns a house and vehicle = 1, else 0 69.03% - Land use mix index Land use mix index within a 500 m buffer from the home location 0.47 0.24 Length of sidewalk (in km) Available length of sidewalk within 500m buffer of home location 5.58 4.15 Note: SD- Standard deviation 8.5. Model Results This section provides brief description about goodness-of-fit measures utilized in the study. Furthermore, parameter estimation results are discussed, followed by discussion about the marginal effects. 144 8.5.1. Goodness-of-fit Measures To empirically demonstrate that the proposed approach with error correlation outperforms the model assuming zero correlation across error terms, a likelihood ratio (LR) test is conducted. The LR value can be calculated as follows: 𝐿𝑅 = 2(𝐿𝐿𝑀𝑉𝑂𝑃 − 𝐿𝐿𝑂𝑃1 − 𝐿𝐿𝑂𝑃2 − 𝐿𝐿𝑂𝑃3) (8.7) Where, 𝐿𝐿𝑀𝑉𝑂𝑃 is the log-likelihood of the proposed MVOP model, and 𝐿𝐿𝑂𝑃1 , 𝐿𝐿𝑂𝑃2 , 𝐿𝐿𝑂𝑃3 represents log-likelihood values of the independent ordered probit models corresponding to three dependent variables. The LR value is found to be 31.50 which is significantly higher than the critical chi-squared value for five degrees of freedom at 99% level of confidence. This confirms that there is a need to adopt a joint equation modelling system to model the preferences towards online grocery shopping, online food orders, and work from home, while accounting for correlation across the error terms. Therefore, joint model is utilized for further discussion. Moreover, to test for multicollinearity among the variables retained in the final model variance inflation factor (VIF) is utilized. In this regard, values ranging from 0 to 5 depicts no significant collinearity and significant collinearity exists if the value is above 5 (Craney and Surles, 2002). The VIF results confirm that no such collinearity exists among the variables retained in the final model, as the maximum VIF value was found to be 2.02. 8.5.2. Parameter Estimation Results This section provides a brief discussion about the parameter estimation results presented in Table 8.2. First the effect of previous teleactivity engagement rates is discussed, followed by 145 sociodemographic, access to travel modes, and built environment attributes. Lastly, the section also discusses endogeneity effects and error correlation results. Previous Online Activity Engagement Frequency and Experiences The estimated MVOP model incorporates the effect of previous teleactivity frequencies and experiences on the post-pandemic preferences. For instance, individuals who never participated in online grocery shopping before the pandemic but started participating in early and later stages of the pandemic are more likely to engage in online grocery shopping after the pandemic. This finding suggests that individuals with longer exposure to online grocery shopping during the initial stages of pandemic are more likely to continue performing them after the pandemic as well. This indicates their long-term behaviour and familiarity towards online grocery shopping encourages them to continue doing it even after the pandemic. Furthermore, individuals who participated in online grocery shopping before the pandemic, early stage of the pandemic, and later stage of the pandemic revealed higher propensity towards online grocery shopping after the pandemic. This depicts the effect of long-term behaviour and familiarity with the ICT usage making them predominant volunteers of online grocery shopping even after the pandemic. Interestingly, engagement in online grocery shopping throughout the three timeframes depicted a higher magnitude as compared to participation in just early and later stages. This reaffirms that consumers with longer exposure to teleactivities are likely to continue to shop online in the future at a higher rate as compared to consumers with recent exposure. Similar results were observed for online food ordering and working from home, reaffirming individuals with higher exposure of performing these activities are likely to continue performing them even in the post-pandemic world. In the case of cross-effects, results reveal that individuals who participated in online grocery shopping before the pandemic, early stage of the pandemic, and in the later stage of the pandemic, 146 revealed a positive relationship with online food orders after the pandemic. Similarly, individuals exposed to online food ordering in the past are more likely to work from home in a post-pandemic world. Regarding experiences related to teleshopping and telecommuting, individuals who only sporadically faced difficulty in website navigation are more likely to continue online grocery shopping. On the other hand, individuals with poor internet connection revealed lower propensity towards online food ordering, reaffirming the importance of ICT for online activity engagement (Lee et al., 2021). Lastly, results depict those employees who were extremely productive working from home during the pandemic are more likely to continue working remotely. This reaffirms that working from home may boost their productivity by reducing family-work conflicts, social isolation, and distracting environments (Galanti et al., 2021). Sociodemographic Attributes This study confirms the effects of sociodemographic attributes such as gender, age, education, household income, household size, and dwelling type. The results reveal that younger individuals (18-34 age group) have a higher propensity towards online food orders, which is consistent with the past literature (Rostami et al., 2023). There is an understanding that younger individuals are more tech savvy and familiar with ICT usage that links them with more frequent teleactivity participation. Interestingly, younger individuals still show a lower propensity to grocery shop online in the post-pandemic context, which may seem counter-intuitive based on the existing literature (Schmid and Axhausen, 2019; Wang et al., 2023; Wieland, 2023). This may be related to the unavailability of cost-effective grocery delivery services in the study area compared to other parts of the world. For example, additional costs such as annual subscription fee, service charges, and delivery fee associated with available delivery services in the region leads to higher grocery bills which might not be applicable in metropolitan cities (Bezirgani and Lachapelle, 2020). This 147 additional cost burden may deter younger individuals from frequent online grocery shopping. Furthermore, middle-aged individuals (40-54) revealed lower preferences for working from home and online food ordering after the pandemic, indicating that the adoption to this new lifestyle of teleactivities at this age adds time constraints which reduces their overall productivity (Loo and Wang, 2018; Sener and Reeder, 2012). Similarly, older individuals revealed a lower preference towards online food ordering in the post-pandemic period. This might be due to their unfamiliarity with ICT usage and online food orders eliminating the opportunity to socialize with people, which is an essential component of one’s life. In terms of education and occupation, individuals with higher education (bachelor and above) working in the natural and applied science sector revealed a higher propensity toward working from home for the post-pandemic period. Similar findings were observed for individuals working in the management sector with a bachelor’s degree and above. This is presumably linked to their familiarity and adaptability with ICT uses or perhaps the effect of flexibility associated with their job due to high income. Among household-level attributes, individuals with an annual household income of $50,000-$80,000 living in single-detached dwellings showed a higher propensity towards online grocery shopping and online food ordering after the pandemic. This reaffirms an understanding that medium-income groups residing in single detached suburban areas have higher expenditure capabilities and that living farther from grocery stores and restaurants encourages them to engage in these activities remotely (Hossain et al., 2022). Interestingly, females residing in households with an annual income between $80,000-$100,000 are more likely to work from home in the post-pandemic period. This reflects that females residing in medium/high income households take higher responsibility for household-related activities such as childcare and prefer jobs that are flexible in nature, allowing them to work from home. Similarly, they revealed a positive 148 relationship with online food ordering in the post-pandemic period, which might be again due to household-related responsibilities and preferences towards socializing among family/friends at home. Household size was found to be a significant factor affecting post-pandemic choices. For instance, it was determined that individuals living in households with a higher number of children are more likely to engage in online food ordering and work from home. This finding shows that households with more children prefer to eat at home together and socialize amongst themselves and it can be very challenging to make trips with younger ones (Lavieri et al., 2018). Moreover, their propensity towards working from home might be due to the childcare responsibilities such as drop/pickup from daycare/schools encouraging one to choose flexible option of telecommuting. Accessibility to Travel Modes and Built Environment Access to tools such as a driver’s license, transit pass, and household vehicles that provide an opportunity to travel is referred to as mobility tool ownership (Fatmi et al., 2014), allowing one to travel with their preferred mode. Access to these transport-related resources can have a significant impact on teleshopping and telecommuting behaviour. For instance, vehicle owners residing in owned (non-rented) dwellings exhibited a lower propensity for online grocery shopping after the pandemic. This might be due to the travel flexibility associated with transport-related resources, which allows them to choose their preferred mode to perform an activity. Interestingly, the absence of a vehicle in a household encourages working from home even after the pandemic. This suggests that vehicle deficient individuals prefer to choose employment positions that are flexible in nature (allowing work from home) (Blumenberg et al., 2021). Similarly, results were observed for online food ordering. Among built environment variables, the land use mix index depicts the share of residential, commercial, and other areas, which includes industrial, in the neighborhood. Interestingly, a negative relationship is observed between the land use mix index and online food 149 ordering. This suggests that individuals residing in urban areas are often equipped with multi-modal travel opportunities, well-connected active transportation networks, and easy access to transit stops, which makes their travel easier to restaurants closer to their residences. Similarly, individuals with higher available length of sidewalk within 500m of home location are less likely to work from home, which again indicates the effect of urban characteristics. Endogeneity Effects and Error Correlation This study reveals a complex inter-relationship between telecommuting and teleshopping choices. In terms of endogenous effects, the preference to work from home post-pandemic positively influences the preference for online grocery shopping after the pandemic. This indicates that decisions of one life domain such as working from home can influence other domain decisions such as online grocery shopping. Due to the adoption of working from home, individuals are more likely to spend time at home and reduce daily mobility, which has a spillover effect on other daily travel decisions such as grocery shopping. Past literature suggested that such individuals who are spending more time at home appreciate the convenience of online shopping (e.g., scheduled delivery, low exposure to public spaces, ability to purchase high-demand items during such crises, and avoiding long queues at stores), and hence prefer online grocery shopping (Dias et al., 2020). On the contrary, a negative relationship was observed for online food orders. This could be triggered by their willingness to “go out” to compensate for increased sedentary tendencies that come with working from home and performing other activities such as grocery shopping remotely. Such individuals consider dining-in at a restaurant as an opportunity to travel outside and socialize with friends/family/colleagues or prefer cooking at home instead. However, other endogenous relationships such as the effect of online food ordering after the pandemic on post COVID-19 150 online grocery shopping and vice versa turned out to be statistically insignificant and were dropped from the final model estimation. Table 8.2. Parameter Estimation Results Post-COVID-19 Online Grocery Shopping Post-COVID-19 Online Order of Food Post-COVID-19 Work from Home Explanatory Variables Coefficient (t-stat) Previous Online Activity Engagement Frequency Pre-pandemic x Early Stage of pandemic x Later Stage of pandemic Online Grocery Shopping Never x Participation x Participation 1.17 (2.71) Participation x Participation x Participation 1.82 (4.78) 1.25 (3.42) Online Order of Food Participation x Participation x Participation 1.34 (4.64) 1.03 (4.55) Work from Home Never x Participation x Participation 0.20 (1.10) Participation x Participation x Participation 0.39 (1.76) Previous Online Activity Engagement Experience Sporadically website is difficult to navigate 0.98 (3.36) Work from home is highly productive 1.08 (3.00) Always Experience poor internet -0.13 (-0.57) Sociodemographic Attributes Age 18 to 34 -0.56 (-1.92) 0.70 (2.37) Age 40 to 54 -0.57 (-1.88) -0.52 (-2.16) Age 65 and above -1.06 (-2.03) Annual household income $50,000-$80,000 x Single_Detached_Dwelling 1.56 (2.82) 1.17 (2.76) Annual household income $80,000-$100,000 x Gender_Female 0.53 (1.46) 0.62 (1.90) Household size of 2 children or more 2.61 (3.02) 2.83 (5.56) Education_level_Bachelors_and_Above x Occupation_Natural_and_Applied_Science 2.03 (2.96) 1.98 (3.32) Education_level_Bachelors_and_Above x Occupation_Management 1.42 (2.89) 151 Post-COVID-19 Online Grocery Shopping Post-COVID-19 Online Order of Food Post-COVID-19 Work from Home Accessibility to Travel Modes and Built environment No vehicle in a household 0.93 (1.83) 1.05 (2.27) Own Vehicle x Own house -0.57 (-2.13) Land use mix index -0.94 (-1.67) Length of sidewalk within 500m of home location (in km) -0.03 (-1.67) Endogenous Variable Future Work from Home 0.79 (4.23) -0.37 (-1.77) Threshold Parameters Threshold 1 2.00 (4.45) 0.54 (1.78) 0.86 (4.48) Threshold 2 3.74 (5.15) 2.02 (4.93) 1.04 (5.31) Error Correlation Post-COVID-19 Online Grocery Shopping and Post-COVID-19 Online of Food 0.24 (1.35) Post-COVID-19 Online Grocery Shopping and Post-COVID-19 Work from Home -0.57 (4.24) Post-COVID-19 Online of Food and Post-COVID-19 Work from Home 0.47 (2.09) Goodness-of-fit Measures Log-likelihood value at convergence value for joint model -261.41 Furthermore, this study accounts for unobserved factors that may jointly influence the post-pandemic decisions such as frequency of online grocery shopping, online food orders, and working from home. The error correlation terms are found to be statistically significant, depicting that it is appropriate and more efficient to estimate the three equations jointly instead of developing them separately. A positive error correlation between post-COVID-19 online grocery shopping and post-COVID-19 online food ordering reveals that unobserved attributes that increased the propensity to engage in online grocery shopping are positively correlated with unobserved attributes that increased the propensity to engage in online food ordering. Similarly, post-COVID-19 online food orders from restaurants and post-COVID-19 working from home revealed a positive 152 error correlation term. On the contrary, a negative relationship is revealed between online grocery shopping and work from home. This indicates that the unobserved factors have an inverse relationship, which suggests unobserved factors that increase the propensity for online grocery shopping decrease the propensity of working from home, and vice versa. 8.5.3. Marginal Effects To further understand the impact of various attributes retained in the MVOP model on telecommuting and teleshopping choices, marginal effects were estimated (Table 8.3). For instance, variable indicating long-term past exposure towards online grocery shopping exhibits impact of 16% on post-COVID-19 online grocery shopping (for few times a month alternative). This suggests that individuals’ long-term exposure towards online grocery shopping increases the probability of participating in online grocery shopping after COVID-19 by 16%. Similar results were observed for online food orders, where past exposures increase the probability to perform them at least few times a month post-COVID-19 by 11%. In addition, working from home pre-pandemic, early and later stage of the pandemic increases the probability to work from home post-COVID-19 few times a week/everyday by 2%. Furthermore, older individuals revealed 11% reduction in probability to engage in online food orders few times a week/every day after the pandemic, as expected. Interestingly, females living in household with annual household income of $80,000-$100,000 increases the probability to work from home in post-pandemic era few times a week/everyday by 8%. Presence of higher number of children in a household increases the probability to perform online food orders and working from home few times a week/everyday by 16% and 4% respectively, in the post-pandemic era. Similarly, presence of no vehicles in a household increases the propensity of working from home after the pandemic few times a week/everyday by 5%. Lastly, urban characteristics such as presence of mixed land use and access 153 to sidewalk have substantial impact on telecommuting and teleshopping choices in the post-pandemic context. Table 8.3. Marginal effects of the variables retained in the MVOP model Variables Post-COVID-19 Online Grocery Shopping Post-COVID-19 Online Order of Food Post-COVID-19 Work from Home never/few times a year few times a month few times a week/ every day never/few times a year few times a month few times a week/ every day never/few times a year few times a month few times a week/ every day Never x Participation x Participation_ Online Grocery Shopping -0.16*** 0.10*** 0.06*** - - - - - - Participation x Participation x Participation_ Online Grocery Shopping -0.26*** 0.16*** 0.09*** -0.28*** 0.14*** 0.13*** - - - Participation x Participation x Participation_ Online Order of Food - - - -0.21*** 0.11*** 0.10*** -0.01** 0.0001 0.01* Never x Participation x Participation_ Work from Home - - - - - - 0.03** -0.0002 -0.03* Participation x Participation x Participation_ Work from Home - - - - - - -0.02* 0.0001 0.02* Sporadically website is difficult to navigate -0.14*** 0.08*** 0.05*** - - - - - - Work from home is highly productive - - - - - - -0.008 -0.0001 0.008 Always Experience poor internet - - - 0.03*** -0.01*** -0.01*** - - - 154 never/few times a year few times a month few times a week/ every day never/few times a year few times a month few times a week/ every day never/few times a year few times a month few times a week/ every day Age 18 to 34 0.08*** -0.05*** -0.03*** -0.15*** 0.08*** 0.07*** - - Age 40 to 54 - - - 0.08*** -0.04*** -0.03*** -0.02** 0.0001 0.02* Age 65 and above - - - 0.23*** -0.12*** -0.11*** - - - Annual household income $50,000-$80,000 x Single_Detached_Dwelling -0.22*** 0.13*** 0.08*** -0.26*** 0.13*** 0.12*** - - - Annual household income $80,000-$100,000 x Gender_Female - - - -0.06*** 0.03*** 0.03*** -0.08*** 0.0004 0.08*** Household size of 2 children or more - - - -0.34*** 0.18*** 0.16*** -0.04*** 0.0002 0.04** Education level_Bachelors_and_Above x Occupation_Natural_and_Applied_Science - - - -0.28*** 0.15*** 0.13*** -0.04** 0.0002 0.04** Education level_Bachelors_and_Above x Occupation_Management - - - - - - -0.11*** 0.0006 0.11** Own Vehicle x Own house 0.08*** -0.05*** -0.03*** - - - - - - 155 never/few times a year few times a month few times a week/ every day never/few times a year few times a month few times a week/ every day never/few times a year few times a month few times a week/ every day No vehicle in a household - - - -0.12*** 0.06*** 0.05*** -0.05** 0.0003 0.05** Land use mix index - - - 0.21*** -0.11*** -0.10*** - - - Length of sidewalk within 500m of home location (in km) - - - - - - -0.04*** 0.0002 0.04*** Note: * 90% confidence level; ** 95% confidence level; *** 99% confidence level 8.6. Concluding Remarks This chapter investigates individuals’ preferences towards teleshopping and telecommuting in the post-pandemic era. Particularly, it investigates the frequency of online grocery orders, online food orders, and working from home. A MVOP framework is adopted to account for the correlation and endogenous effects among the three choices of teleactivities. Evidence from this study provides essential input on how post-pandemic activity-travel patterns will shape up and to what extent pre-pandemic and during the pandemic travel behaviour influences them. In addition, this chapter also tests the effect of sociodemographic features, accessibility to travel modes, and built environment attributes. The endogenous variable effect is one of the most important findings of this study. Results reveal that an individual’s one life domain decision, such as working from home in the post-pandemic era, will positively influence another life domain decision such as online grocery shopping. On the other hand, working from home in the post-pandemic era revealed a negative relationship with online food ordering after the pandemic. Through this line of investigation, the hypothesis that individuals are likely to continue engaging in online grocery shopping, online food orders, and to work from home even after the pandemic was confirmed. For example, results suggest that 156 individuals engaging in teleactivities before, during the early stage, and in the later stage of pandemic are more likely to continue performing them online as compared to individuals with recent exposures. Furthermore, in terms of socio-economic attributes, results reveal that individuals with higher annual household income, presence of children in a household, and higher education level are more likely to engage in teleactivities. However, results were not the same for older individuals, individuals with vehicle ownership, and urban dwellers. Several implications can be drawn from this study to increase the adoption of teleactivities after the pandemic. For example, enhancing consumer experience with website navigation and promoting digitalization/ICT usage among consumers of all age groups and business sizes are some of the ways to accomplish this. Furthermore, in terms of socio-economic attributes, results reveal that individuals with vehicle ownership are less likely to engage in online grocery shopping after the pandemic. This indicates that these individuals utilize travel flexibility associated with vehicle ownership. Therefore, future policies and investments are required to improve the level-of-service (LOS), particularly investing in alternative transportation infrastructures such as walk and bike facilities. This could significantly improve the LOS of roads as it is likely to attract individuals to use alternative travel modes and will free up space on the traffic lane. In a similar line of investigation related to land use attributes, results suggest that individuals residing in urban areas are more likely to dine-in at a restaurant. Hence, policies need to target mixed land use developments offering residential and commercial establishments to attract individuals for both living and engaging in activities at close-by destinations. Overall, the findings of this chapter provide newer insights on the teleactivity engagement behaviour of an individual in the post-pandemic context. 157 CHAPTER 9: CONCLUSION 9.1. Contributions This thesis aims to contribute to the existing literature by developing innovative econometric modelling techniques and applying them to provide granular level insights on one’s activity-travel decisions made over a 24-hour time period. Following is a brief discussion of the major contributions of this thesis: • An advanced econometric modelling approach has been developed to investigate activity participation and time allocation decisions at an disaggregate episode-level, which is referred to as MDCEV-OP model. The proposed model is a modified version of the existing MDCEV model. The proposed MDCEV-OP model accommodates an ordering of activity episodes to ensure logical consistency that a higher numbered episode does not occur without the occurrence of lower-numbered episodes. The goodness-of-fit measures and the forecasting exercise suggest that the proposed MDCEV-OP outperforms the aggregate-level MDCEV model. • A single unified modelling framework has been developed to investigate time use (i.e. activity start time choice), travel-related (i.e. mode and companionship choice), and land use (i.e. destination location choice) decisions simultaneously. The model accommodates complex interdependencies by introducing unobserved factors that are common to these dimensions. The goodness-of-fit measures reveal that the proposed joint model outperforms independent models, which indicates the need for joint estimation of these choices. Moreover, model results confirm that unobserved factors play a significant role in 158 shaping one’s activity-travel behaviour and presence of interdependencies among time use, travel, and land use-related decisions. • This study provides important evidence towards the need to accommodate the effects of destination choice on activity generation and activity scheduling related decisions. For example, a logsum-based coupling mechanism has been adopted to provide feedback from the destination choice model to the activity generation model. This analysis reveals that the destination location choices (i.e., destination accessibility) which are sensitive to each activity episode, captured through level-of-service, land use, and built environment measures positively effect episode-level activity engagement and duration decisions. Apart from behaviourally intuitive findings, this exercise confirms that incorporating destination accessibility offers a statistically superior model as compared to a model that does not consider the influence of destination accessibility on activity generation and time allocation. • This thesis provides newer insights; particularly, related to telecommuting work arrangement and how it changes individuals' travel activity generation, VKT, and engagement in other online activities. For example, o The newly developed MDCEV-OP model has been used to investigate variations in activity generation patterns of telecommuters and non-telecommuters over a 24-hour time period. The model results reveal substantial differences between the two groups of workers; for instance, telecommuters are more likely to engage in higher episodes of non-mandatory activities, compared to non-telecommuters. o An advanced econometric method has been utilized to capture the presence of observed and unobserved factors encouraging one to self-select themselves into a 159 particular work arrangement type and further dictate their VKTs. To accommodate such interdependency, an endogenous switching regression model is developed. The model results confirm that workers encourage themselves to self-select into certain work arrangement type and govern their non-work home based tour VKTs. Lastly, treatment effects suggest that randomly selected individual shifting from commuting to telecommuting may increase his/her non-work VKT. o This thesis provides insights towards the preferences for telecommuting (work from home) and teleshopping, including online grocery shopping and online food ordering in the post-pandemic era. The findings of the study reveal that engagement in telecommuting can influence engagement in teleshopping due to presence of common unobserved factors. Furthermore, results reveal that an individual’s one life domain decision, such as working from home in the post-pandemic era, will positively influence another life domain decision such as online grocery shopping. Furthermore, individuals with longer exposure to teleactivities are more likely to continue performing them in the post-pandemic period as compared to individuals with shorter exposures. 9.2. Limitations and Future Works Although this thesis provides newer insights on activity-travel choices, it has certain limitations and provides basis for future research. Following is a summary outlining the limitations and future research avenues: • The current research utilizes one day travel diary to explore activity-travel choices. This circumvents the possibility to explore complex behaviour of an individual. For instance, the influence of hybrid work arrangements (work from home some days of the week and 160 travel to work rest of the days) on activity-travel choices. This limitation of the thesis is largely attributed by the unavailability of such week-long travel data. Therefore, future studies can focus on efficiently designing week-long travel surveys capturing activity-travel choices without making them burdensome and resource-intensive. In this line of investigation, there is also a need to modify current modelling approaches enabling them to tackle week-long activity-travel choices and associated correlations. • The current research investigates preference towards teleactivities however, there is a need to incorporate temporal allocations of such teleactivities and associated choices in the activity generation and activity scheduling module. One avenue is to extend the current trip-based travel surveys to time use surveys to capture in-home/online activity participation and their temporal allocations. From a modelling perspective, future models need to capture in-home/online and out-of-home activity engagement and duration simultaneously. Moreover, there is a need to investigate how these two choice dimensions (in-home and out-of-home activity participation) interact with each other. • The econometric models, such as model for preference towards teleactivities are developed using a small sample size. This leads to challenges such as model validation. Collecting such data with longitudinal information about one’s activity participation at different time points, experiences with technology adoption, attitudinal/preference towards certain scenarios, and sociodemographic information, among other factors can pose challenges such as rate of response. Therefore, there is a need to develop newer comprehensive data collection methods making such surveys less burdensome and resource-intensive. • The current research considers random sampling technique (with smaller number of alternatives in the choice set) to investigate destination choice decisions. However, 161 availability of limited alternatives might not reflect the actual decision-making behaviour of an individual. Therefore, a larger size of choice set is essential and future research can focus on determining the appropriate number of alternatives in the choice set. Furthermore, to replicate destination choice behaviour more realistically, an accessibility-based measure is also adopted. However, future research can focus on adopting a hybrid sampling technique by integrating destination location accessibility measures and more logical stratified sampling approach for choice set generation in the destination choice modelling process. • The proposed activity generation model captures interaction among household members to some extent by exogenously testing the influence of household members (through household size variable) on the activity engagement and duration decisions. However, this may not adequately capture the interactions among the household members and how it can further influence the activity generation decisions. Therefore, this is considered as a limitation of the current research, and further efforts are required to accommodate interactions among household members at such disaggregated episode level and further utilize models for forecasting purposes. • The current research did not account for disaggregated companionship choices based on the relationship (e.g., father/mother, spouse, friend, colleagues, etc.) which can further provide granular level information, and is largely attributed by the unavailability of data. In this context, future travel surveys should be designed to capture such choices at a disaggregated level. Furthermore, from a modelling perspective, there is a need to include such granular level household-level interactions while investigating activity-travel decisions. 162 • The proposed model captures the interactions among work arrangement choice and non-work travel outcomes. In addition to self-selection, work arrangement choices are also governed by an individual’s occupation type. Therefore, the proposed model tests the influence of occupation type on the work arrangement choice. Different occupation types such as business and management related, natural and applied science related, health service related, trades-related, agriculture-related, and among others were tested in the model. However, some of them were not retained in the final model because of statistical insignificance, which is largely attributed by the small sample size. In this line of investigation, there is a possibility that not all workers have the same flexibility of self-selecting themselves to work from home because of their occupation type and/or employer’s policy. Therefore, to further understand if work arrangement decisions are not only governed by worker’s self-selection but also occupation type and/or employer’s policy there is a need to capture such work-related information in the future travel surveys. • To examine the activity-travel behaviour of individuals in the future (e.g., post-pandemic) by implementing the proposed models, there is a need to check if these models are temporally transferable or not – i.e., the ability to produce accurate forecasts for the future. This is considered as one of the future works where various tests such as Transferability Test Statistic (TTS) can be conducted to check if the proposed models are applicable in a post-pandemic context or not. • One of the immediate avenues for future work should be utilizing current models to provide behavioural information to an ABM. 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Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Fatmi, Mahmudur Rahman
Modelling activity generation and scheduling decisions, and exploring the interplay with telecommuting
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