Bicyclists’ travel behavior analysis: modeling bicycling demand, speed choice, destination choice, and usage of shared mobility services
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Orvin, Muntahith Mehadil
issued: 2020-04-23T20:53:29Z
issued: 2020
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: This thesis develops advanced modeling techniques to investigate bicycling demand and bicyclists’ user behavior including their speed choice, destination choice, and reasons to choose dockless bikeshare service (DBS). This study utilizes data from automatic bicycle counters, GPS records, and DBS user survey from the City of Kelowna. One of the major contributions of this thesis is to adopt a machine learning (ML) algorithm to improve the trip identification procedure using the GPS data. Another key feature of this thesis is to develop latent segmentation-based (LS) models to capture heterogeneity. For example, the destination choice analysis adopts a random parameter LS logit model to address multidimensional heterogeneity by assuming a discrete (i.e. inter-segment heterogeneity), and continuous (i.e. intra-segment heterogeneity) distribution of the parameters. This thesis examines the effects of weather, built environment, traffic, land use, neighborhood, socio-demographic, accessibility, and trip attributes on bicycle usage. In case of the model results, the proposed advanced models show improved goodness-of-fit measures. For instance, speed choice model validation results suggest that ML and LS modeling approach outperforms other methods in-terms of Welch’s t-test and Mean Absolute Deviation respectively. The model results provide important behavioral insights which are expected to assist in developing effective policies to promote bicycling more. For example, the model results suggest that bike index (BI) is an important target variable to increase bicycling demand and speed. Individuals are more likely to choose DBS as cheapest option while residing in bicycle-friendly neighborhoods with good transit accessibility. The model results also confirm the existence of multidimensional heterogeneity. For instance, trip destinations closer to designated dockless bike return areas (i.e. havens) show inter-segment heterogeneity. Specifically, urban trips are more likely …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/74133
note: BICYCLISTS’ TRAVEL BEHAVIOR ANALYSIS: MODELING BICYCLING DEMAND, SPEED CHOICE, DESTINATION CHOICE, AND USAGE OF SHARED MOBILITY SERVICES by Muntahith Mehadil Orvin B.Sc., Bangladesh University of Engineering and Technology (BUET), 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2020 © Muntahith Mehadil Orvin, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: BICYCLISTS’ TRAVEL BEHAVIOR ANALYSIS: MODELING BICYCLING DEMAND, SPEED CHOICE, DESTINATION CHOICE, AND USAGE OF SHARED MOBILITY SERVICES submitted by Muntahith Mehadil Orvin in partial fulfillment of the requirements of the degree of Master of Applied Science (MASc) in Civil Engineering. Dr. Mahmudur Fatmi, School of Engineering Supervisor Dr. Gordon Lovegrove, School of Engineering Supervisory Committee Member Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Anas Chaaban, School of Engineering University Examiner iii Abstract This thesis develops advanced modeling techniques to investigate bicycling demand and bicyclists’ user behavior including their speed choice, destination choice, and reasons to choose dockless bikeshare service (DBS). This study utilizes data from automatic bicycle counters, GPS records, and DBS user survey from the City of Kelowna. One of the major contributions of this thesis is to adopt a machine learning (ML) algorithm to improve the trip identification procedure using the GPS data. Another key feature of this thesis is to develop latent segmentation-based (LS) models to capture heterogeneity. For example, the destination choice analysis adopts a random parameter LS logit model to address multidimensional heterogeneity by assuming a discrete (i.e. inter-segment heterogeneity), and continuou…
has type: Thesis/Dissertation
date issued: 2020-05
is shown at: 10.14288/1.0389954
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/74133