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Pre-implementation evaluation of safety improvement programs Lin, Fred C. 2000

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PRE-IMPLEMENTATION EVALUATION OF SAFETY IMPROVEMENT PROGRAMS  by FRED C. LIN  B.A.Sc. (Civil Engineering), The University of British Columbia, 1998  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF CIVIL ENGINEERING  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA April, 2000 © Fred C. Lin, 2000  In presenting this thesis in partial fulfillment of the requirements for an advanced degree at the University of British Columbia, I agree that the library shall make it freely available for reference and study.  I further agree that permission for extensive copying of this thesis for scholarly  purposes may be granted by the head of my department or by his or her representatives.  It is  understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  Department of Civil Engineering The University of British Columbia Vancouver, Canada  Date  2 ,  Abstract  11  ABSTRACT  This thesis describes the development of an economic-based prediction and evaluation procedure that can be utilized in prioritizing safety improvement projects. The objective of the procedure is to alleviate the problems associated with traditional economic analysis of road safety improvement programs.  Traditionally, the overall effectiveness of safety improvement programs is normally based on the benefits anticipated from a reduction in road collisions. Although the procedures for performing the economic analysis of road safety improvements in general are reasonably straightforward and well documented in the literature, these procedures fail to accurately estimate the safety benefits or disbenefits of these improvements on a consistent basis. The problems can be categorized into two parts: system-wide versus project-level analysis, and dealing with the uncertainties in the effectiveness and applicability of the proposed countermeasure. To resolve these problems, this thesis first describes the development of a new safety analysis software known as ISECR (the Information System for Estimating Crash Reductions) which can be used to determine the expected collision reduction due to a specific countermeasure. ISECR is an intelligent database that uses a case-based reasoning approach and consists of past safety research efforts on collision reduction factors (CRFs) associated with different countermeasures. The system can be used to determine the expected CRFs and the associated range and reliability of the proposed countermeasure when applied to a particular problem at hand.  The safety benefits of implementing a countermeasure at a location can be represented by the expected reduction in collision frequency, which is normally calculated by the product of CRFs and the expected number of collisions. With the ISECR predictions on CRFs and the expected  Pre-Implementation Evaluation of Safety Improvement Programs  Abstract  iii  number of collisions determined by the multivariate and Empirical Bayes methods, this thesis then illustrates the use of the moment approach to evaluate the expected collision reduction and its uncertainty. The results can then be used to assist in evaluating the economic feasibility of a countermeasure prior to its implementation. Specifically, the probability of achieving a preset economic goal (i.e., a specific benefit-cost ratio) by implementing a countermeasure at a specific location can be determined.  Finally, the prototype ISECR has been verified and validated using several case studies. The results of the verification and validation have shown that ISECR produced results that are comparable to the results obtained from real cases.  Pre-Implementation Evaluation of Safety Improvement Programs  Table of Contents  iv  TABLE OF CONTENTS  ABSTRACT  ii  T A B L E OF CONTENTS  iv  LIST OF FIGURES LIST OF T A B L E S ACKNOWLEDGEMENTS 1.0 I N T R O D U C T I O N  viii x xi 1  1.1 Background  1  1.2 Problem Definition  2  1.3 Thesis Objectives  3  1.4 Methodology  4  1.5 Thesis Structure  4  2.0 L I T E R A T U R E R E V I E W  6  2.1 Introduction  6  2.2 Case-Based Reasoning  6  2.2.1 C B R Applications in Transportation Engineering  8  2.2.2 General C B R Issues and Procedures  9  2.2.3 Representation Issues of C B R  9  2.2.4 Control Issues of C B R  12  2.2.4.1 Case Retrieval  13  2.2.4.2 Case Adaptation  15  2.2.4.3 Case Combination  16  2.2.5 C B R Methodologies  Pre-Implementation Evaluation of Safety Improvement Programs  17  Table of Contents  v  2.2.5.1 Nearest Neighbour Approach  18  2.2.5.2 Collaborative Approach  18  2.3 Collision Analysis  21  2.3.1 The Generalized Linear Modeling (GLIM) Approach  21  2.3.2 The Empirical Bayes (EB) Approach  23  3.0 C B R A P P L I C A T I O N IN ISECR  25  3.1 Introduction  25  3.2 Case Representation in ISECR  26  3.3 Case Retrieval in ISECR  33  3.4 Quality Score  34  3.4.1 Changes in Traffic Volume  35  3.4.2 Inclusion of Unrelated Effects  35  3.4.3 Regression to the Mean (RTM) Artifact  36  3.4.4 Calculation of Quality Scores  37  3.5 Relevance Score  39  3.5.1 Calculation of Relevance Scores  39  3.6 Calculating Case Distance in ISECR  41  3.7 Solution Construction in ISECR  43  3.7.1 Nearest Neighbour Approach  43  3.7.2 Collaborative Approach  43  4.0 DESCRIPTION OF ISECR  48  4.1 Main Menu  48  4.2 View A l l Documents  49  4.3 Search Documents  53  Pre-Implementation Evaluation of Safety Improvement Programs  Table of Contents  vi  4.4 Predict C R Factors (CRFs)  54  4.5 Input New Documents  64  5.0 V A L I D A T I O N OF ISECR  65  5.1 Introduction  65  5.2 ISECR Results in the Context of Published Studies  65  6.0 E X P E C T E D COLLISION R E D U C T I O N  68  6.1 Introduction  68  6.2 Collision Reduction and Its Uncertainty  69  7.0 E C O N O M I C A N A L Y S I S  71  7.1 Introduction  71  7.2 Benefit-Cost Ratio (BCR) and Its Uncertainty  71  7.3 Probability Density Function of B C R  72  8.0 APPLICATIONS  74  8.1 Problem Definition  74  8.2 Countermeasure Effectiveness  75  8.3 Expected Collision Reduction  84  8.4 Economic Analysis  -.  85  9.0 C O N C L U S I O N  87  BIBLIOGRAPHY  90  A P P E N D I X A : C O U N T E R M E A S U R E TYPES  94  A P P E N D I X B : L O C A T I O N CHARACTERISTICS CONSIDERED F O R E A C H OF THE EIGHT L O C A T I O N TYPES  98  A P P E N D I X C: ISECR WINDOWS: C O U N T E R M E A S U R E TYPES CONSIDERED FOR DIFFERENT C O U N T E R M E A S U R E CATEGORIES  Pre-Implementation Evaluation of Safety Improvement Programs  105  Table of Contents  vii  A P P E N D I X D: ISECR WINDOWS: INPUT F O R M S U S E D TO E N T E R L O C A T I O N CHARACTERISTICS FOR DIFFERENT L O C A T I O N TYPES  116  A P P E N D I X E: P R O B A B I L I T Y DENSITY PLOT OF B C R FOR T H E P U B L I C LIGHTING E X A M P L E  Pre-Implementation Evaluation of Safety Improvement Programs  122  List of Figures  viii  LIST OF FIGURES  Figure 2.1. Representation of a Case in Case Base  12  Figure 2.2. Nearest Neighbour Approach  19  Figure 2.3. Collaborative Approach  20  Figure 3.1. Representation of a Case in ISECR  32  Figure 4.1. ISECR Window: Main Menu  49  Figure 4.2. ISECR Window: View A l l Documents  50  Figure 4.3. ISECR Window: Summary of Documents  51  Figure 4.4. ISECR Window: A n Example of One Page Summary  52  Figure 4.5. ISECR Window: Selecting Query Parameters  53  Figure 4.6. ISECR Window: Countermeasure Categories  55  Figure 4.7. ISECR Window: Countermeasure Types for Delineation  56  Figure 4.8. ISECR Window: Location Types  57  Figure 4.9. ISECR Window: No Match Found  58  Figure 4.10. ISECR Window: Selecting to Enter Location Characteristics  59  Figure 4.11. ISECR Window: Input Location Characteristics for Signalized Intersections  59  Figure 4.12. ISECR Window: Selecting the Criteria to Calculate Case Distances  60  Figure 4.13. ISECR Window: Summary of Ranked Documents  61  Figure 4.14. ISECR Window: Predicted CRFs and their Standard Deviations  63  Figure 8.1. Example: Cumulative Distribution Plot of B C R  86  Figure C . l . ISECR Window: Countermeasure Types for Area-Wide Schemes  106  Figure C.2. ISECR Window: Countermeasure Types for Bridge Improvements  106  Figure C.3. ISECR Window: Countermeasure Types for Cyclist/Pedestrian Facilities  107  Figure C.4. ISECR Window: Countermeasure Types for Delineation  107  Pre-Implementation Evaluation of Safety Improvement Programs  List of Figures  ix  Figure C.5. ISECR Window: Countermeasure Types for Geometric Improvements  108  Figure C.6. ISECR Window: Countermeasure Types for Intersection Improvements  109  Figure C.7. ISECR Window: Countermeasure Types for Lane/Shoulder Treatment  110  Figure C.8. ISECR Window: Countermeasure Types for Lighting Improvements  110  Figure C.9. ISECR Window: Countermeasure Types for Object Removal/Relocation  Ill  Figure C.10. ISECR Window: Countermeasure Types for Parking Improvements  Ill  Figure C . l 1. ISECR Window: Countermeasure Types for Pavement Treatment  112  Figure C.12. ISECR Window: Countermeasure Types for Railway Improvements  112  Figure C.13. ISECR Window: Countermeasure Types for Regulation Change  113  Figure C.14. ISECR Window: Countermeasure Types for Safety Barriers  113  Figure C.15. ISECR Window: Countermeasure Types for Traffic Controls/Signs  114  Figure C.16. ISECR Window: Countermeasure Types for Traffic Signals  115  Figure D . l . ISECR Window: Input Location Characteristics for General Intersections  117  Figure D.2. ISECR Window: Input Location Characteristics for Signalized Intersections  118  Figure D.3. ISECR Window: Input Location Characteristics for Unsignalized Intersections  119  Figure D.4. ISECR Window: Input Location Characteristics for Road Sections  120  Figure D.5. ISECR Window: Input Location Characteristics for Freeways  121  Figure D.6. ISECR Window: Input Location Characteristics for Bridges, Rails, and Construction Zones Figure E . l . Example: Probability Density Plot of B C R  Pre-Implementation Evaluation of Safety Improvement Programs  121 123  List of Tables  x  LIST O F TABLES  Table 3.1. Representation of Location Characteristics for Signalized Intersections  30  Table 3.2. Example: Representation of the Location Characteristics  40  Table 3.3. Example: Determining Non-Weighted and Weighted CRFs  45  Table 4.1. Example: Ranking of Evaluation Studies  62  Table 4.2. Example: Final Output to the Summary Window  62  Table 5.1. Summary of CRFs: ISECR Results vs. Published Results  66  Table 8.1. Example: Retrieved Cases and their Quality Scores  77  Table 8.2. Example: Retrieved Cases and their Relevance Scores  79  Table 8.3. Example: Retrieved Cases (Ranked) and their Case Distances and Results  81  Table A . l . Countermeasure Types  95  Table B . l . Representation of Location Characteristics for General Intersections  99  Table B.2. Representation of Location Characteristics for Signalized Intersections  100  Table B.3. Representation of Location Characteristics for Unsignalized Intersections  101  Table B.4. Representation of Location Characteristics for Road Sections  102  Table B.5. Representation of Location Characteristics for Freeways  103  Table B.6. Representation of Location Characteristics for Bridges, Rails, and Construction Zones  Pre-Implementation Evaluation of Safety Improvement Programs  104  Acknowledgements  xi ACKNOWLEDGEMENTS  I would firstly like to express my gratitude to my thesis advisor, Dr. Tarek Sayed, for his constant guidance and supervision throughout the duration of this research. He was very patient, helpful, and provided me with constructive suggestions to this research.  I would also like to thank the Insurance Corporation of British Columbia for providing the financial support for this research and G.D. Hamilton & Associates Consulting L T D . for providing the document summaries used in the ISECR database. Furthermore, I would also like to thank Joan N g and Vikki Ngan for locating and copying the evaluation studies, and Shaun Bidulka for entering them into the ISECR database.  Most important of all, I would like to specially thank my family for their support and encouragement throughout, not just this research, but my whole life.  Their influence and  example have inspired me to reach many goals in my life. Thank you, mom and dad, and of course, Frank and Kelly.  Last but not least, special thanks to that special person (you know who you are, Gillian!) for the loving support and most important of all, patience during this endeavor. You are simply the best!  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 1: Introduction  1.0 INTRODUCTION  1.1 Background  In response to limited budgets and growing fiscal constraints, it has become very important to ensure the funding available for road safety improvements is efficiently utilized. Traditionally, funding allocated for road safety improvement programs has been proportionally low compared to other road projects. Nevertheless, due to the increasing public awareness and high social and economic costs of road collisions in recent years, it has become apparent to road agencies responsible for road investment and improvement that the importance of road safely can not be overemphasized. Consequently, it is crucial to improve safety evaluation procedure to ensure an optimal allocation of the available funding.  In an attempt to maximize the overall safety benefit to road users, safety professionals have developed and invoked a standard process to evaluate the cost-effectiveness of road safety projects and programs. Typical safety improvement programs, or commonly referred to as Black Spot Programs, usually include the identification, diagnosis, and remedy of collision-prone locations. In evaluating these programs, the overall effectiveness is normally based on the safety benefits anticipated from a reduction in road collision frequency and/or severity following the implementation of a safety improvement. Procedures for performing the economic analysis of road improvement programs in general are reasonably straightforward and are well documented in the literature. However, the problem with these procedures, when applied to estimating the effectiveness of road safety improvements, is that they do not always accurately estimate the safety benefits of these improvements. It is generally felt by many professionals associated with  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 1: Introduction  2  road safety, that a comprehensive and systematic economic-based approach for the accurate preimplementation evaluation of road safety programs does not currently exist  1.2 Problem Definition  The problem associated with the traditional economic evaluation procedures arises from the estimation of safety benefits of the proposed improvements. The safety benefits are represented by the expected reduction in the number and/or severity of collisions following  the  implementation of the improvement. The collision reduction is calculated as the product of the countermeasure effectiveness and the expected number of collisions.  The problem can be  categorized into two parts: system-wide versus project-level analysis, and dealing with the uncertainties in the effectiveness and applicability of the proposed countermeasure.  A very important step in estimating collision reduction is determining the effectiveness of countermeasures, or what is known in the literature as collision reduction factors (CRFs). A collision reduction factor (CRF) can be considered simply as a value representing the percentage of collisions that a safety improvement is expected to eliminate from a location, or a group of locations receiving the same treatment type.  Several agencies, such as the Federal Highway Administration (FHWA) and the Institute of Transportation Engineers (ITE), have developed CRFs for different safety improvements. Despite the common usage of these CRFs in practice, there are two major deficiencies in using them to generate project-level safety estimates. Firstly, these CRFs are developed as system-wide factors where the variability of collision patterns and geometric configuration among different  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 1: Introduction  3  locations are not considered. Secondly, the use of these CRFs does not account for the treatment of the uncertainty issues related to the effectiveness of the proposed safety improvement. Means to resolve these two problems will be presented in this thesis.  1.3 Thesis Objectives  The objective of this thesis is to present a systematic procedure that aims to assist in prioritizing safety improvement projects and ultimately ensure the optimal allocation of funding available for road safety improvements. The followings are the key focal points:  1. Investigate methods to evaluate the effectiveness (CRFs) of various safety improvements on a project-level basis where different collision patterns and site characteristics of a location are considered. 2.  Explore techniques to deal with the uncertainty issues involved in determining the effectiveness (CRFs) of various safety improvements. Specifically, the expected values of CRFs and the associated standard deviations will be calculated.  3.  Present an economic-based prediction and evaluation procedure to increase the confidence of predictions associated with the current practice of evaluating the expected safety benefits from improvement programs. Specifically, the probability of a proposed safety improvement achieving a specific economic goal (e.g., a pre-determined benefit-cost ratio) prior to its implementation will be calculated.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 1: Introduction  4  1.4 Methodology  To estimate CRFs on a project-level basis, a new safety analysis software called the Information System for Estimating Crash Reductions (ISECR) is developed in this thesis. ISECR is an intelligent database that uses case-based reasoning and consists of past safety research efforts on CRFs associated with the different safety improvement measures. ISECR has the following functions:  1. Data entry: ISECR permits the entry of evaluation studies that report CRFs associated with different safety improvements. 2. Data retrieval and analysis: ISECR employs a case-based reasoning (CBR) approach for data retrieval and analysis.  The system is designed to accept queries, analyze and display  information from the database that matches the queries. 3.  CRFs estimation and reporting: ISECR computes the effectiveness and the associated range and reliability of the proposed countermeasure related to the total number of collisions, and different collision types and severity.  4.  Benefit-cost ratio estimation: ISECR provides results that can be used in evaluating the benefit-cost ratio pertaining to the proposed safety improvement.  1.5 Thesis Structure  This thesis is divided into nine chapters. Following this introductory chapter, Chapter Two presents the results of a comprehensive literature review on the topics of case-based reasoning and collision analysis.  Chapter Three explains how CBR is utilized in the design of the  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 1: Introduction  5  intelligent database, ISECR. As well, Chapter Three details the methodology which ISECR uses to estimate CRFs and their uncertainties. Chapter Four describes the various functions provided by ISECR. The validation of SECR is presented in Chapter Five. In Chapters Six and Seven, the uncertainty issues involving the expected reduction in collision frequency and economic analysis are examined respectively. A n example illustrating the procedures outlined in this thesis is included in Chapter Eight. Finally, the conclusion and some suggestions for further research are included in Chapter Nine.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  6  2.0 LITERATURE REVIEW  2.1 Introduction  This chapter presents the results of a comprehensive literature review on the following two topics:  1. Case-base reasoning (CBR) 2. Collision analysis, with a focus on the generalized linear modeling (GLIM) approach and the Empirical Bayes (EB) technique used to determine the expected number of collisions at a location.  The first part of this chapter provides a literature review on C B R , which is utilized in the development of ISECR to predict collision reduction factors (CRFs) and the associated uncertainties. As noted in Chapter One, most of collision analyses utilize the products of CRFs and the expected number of collisions to estimate the potential safety benefits, i.e., the expected collision reduction, of implementing a safely improvement at a location. Thus, the second part of this chapter focuses on describing how the G L I M and EB approaches can be used to estimate the expected number of collisions and the associated uncertainties at a location.  2.2 Case-Based Reasoning  Knowledge-based systems (KBS) utilize rule-based and model-based reasoning techniques to build design automation and design decision support systems.  Although there was notable  success in some areas, difficulties have been encountered with K B S when formalizing  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  7  generalized design experiences such as rules, logic, and domain models. These in turn, have led to the development of case-based reasoning (CBR) systems.  C B R systems have proven to be effective in providing desired results in various fields, such as in architectural design, structural design, software specifications design, and etc.  C B R is a  relatively recent concept, with the original research conducted in the early 1980s as first described conceptually by Schank (1982). It is a methodology used to solve new problems by reusing and adapting (or combining) solutions that worked for similar problems in the past.  In CBR, past problem solving experiences (cases) are stored in a database (case base) and upon request, cases similar and/or relevant to the current problem (design case) can be retrieved from the case base. The cases retrieved (retrieved cases) from the case base can then be adapted or combined to better fit the design case. Differentiating from the more traditional rule-based and model-based reasoning techniques, i.e., the former captures knowledge in the form of if-then rules while the latter formulates knowledge in the form of principles and/or models to cover various aspects of a problem domain, C B R is an experience-based method that utilizes prior problem solving experiences as its main knowledge source.  C B R is a growing field as it resembles, to some degree, the psychological process at which a person follows when attempting to utilize his/her knowledge and experience in solving a new problem. Using the C B R approach, modeling of domains that are not completely understood and open-ended is possible. Consequently, C B R can be considered as an alternative to paradigms such as rule-based and model-based reasoning.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  8  2.2.1 CBR Applications in Transportation Engineering  In the transportation field, there are few applications that employ a C B R approach. For example, Khattak and Kanafani (1996) first developed a planning tool for ITS (Intelligent Transportation Systems) using a C B R approach. As an enhanced system for their first proposed C B R planning tool, Khattak and Renski (1999) then focused their effort on planning H O V (High Occupancy Vehicle) lanes by integrating the C B R approach in a GIS (Geographic Information System) environment.  In the field of road safety analysis, only one system was found in the literature that employed a C B R approach. Capus and Tourigny (1998) developed a C B R system (called R O S A C , ROad Safety Analysis with Cases) capable of retrieving similar cases from the case base. Furthermore, R O S A C can reuse, adapt, and save the new problem and adapted solution as a new case in the case base. In R O S A C , each case consists of a problem, as represented by site characteristics and collision statistics, and its solution, as described by collision patterns, collision causes, and the implemented safety improvements. Case retrieval is realized when the site characteristics and collision statistics of the current situation match, to some degree, with the ones stored in the case base. The retrieved cases can then be reused and adapted in R O S A C as the solution to the design case.  The following sections provide a brief introduction to some of the issues and procedures involved in a C B R system design, with a focus on the techniques utilized in the development of ISECR. However, for a detailed and comprehensive description on CBR, books and/or reports written by  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  9  authors such as Kolodner (1993), Watson (1997), Leake and Plaza (1997), etc., should be consulted.  2.2.2 General CBR Issues and Procedures  Regardless of the domain of application, the followings are the two general issues concerning the application and design of any C B R system:  1. Representation issues: Representation issues concern with the contents (information), representation, indexing, and memory organization of cases. 2.  Control issues: Control issues, on the other hand, deal with the general processes of a C B R system, which are comprised of the retrieval, adaptation, and combination of cases.  Depending on the context of each individual project, the representation and control issues involved in a C B R system design may vary from one project to another. Nevertheless, the main purpose of any C B R system is to facilitate the solving of a similar problem in a somewhat similar context.  Hence, general design issues and procedures involved in all C B R systems can be  observed and are further discussed below.  2.2.3 Representation Issues of CBR  In any C B R system, the database (case base) contains a representation of a set of previously solved problems (cases).  It is extremely important to select an appropriate model for case  representation when designing a C B R system. A n appropriate model provides the very basis of  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  10  how cases are represented in the C B R case base. Furthermore, the subsequent C B R procedures, including the retrieval of relevant cases and their adaptation or combination, rely heavily on the representation model selected. For that reason, a systematic approach is required to identify and express the uniform representation of specific features that make up a case.  Case representation, as mentioned previously, is the first of two issues that needs to be addressed prior to carrying out any C B R system design. In general, issues related to developing an adequate and useful case base can be addressed by answering the following four fundamental questions:  1. What are the contents, or features, that are essential to represent a case? 2. How to express or represent these features (as in numbers, symbols, Boolean variables, texts, models, etc.)? 3. How to structure these features effectively and efficiently to minimize the storage and computation requirements? 4. What case indexing schemes should be used for the retrieval of cases?  Essentially, case representation is concerned with how the contents of each case are represented and organized in the case base. There are a number of ways to organize the information in a case, however, only two of these alternatives are relevant to this research and discussed further below, i.e., using a set of attribute-value pairs or part-subpart relationships. These two alternatives are illustrated in Figure 2.1.  When representing a case as attribute-value pairs, all features and associated values are represented with these sets of values. Information gathering is made simple in this manner as all  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  aspects of a case are kept in one case. This type of case representation is typically adequate for small and simple cases. When representing a case as a hierarchy of part-subpart relationships, information within a case is broken down into a number of subcases. Therefore, a higher degree of information is required to adequately represent the subcases, and to designate the relationship knowledge within a case.  Despite the higher degree of complexity with this type of case  representation when compared to the attribute-value pairs, using a part-subpart hierarchy is beneficial as it facilitates the representation of large and complex cases.  The representation and organization of a case should be consistent for all cases in the case base. In this regard, all cases are described by the same set of features, represented either by attributevalue pairs and/or part-subpart relationships. A n adequate representation scheme is the first step to ensure a successful C B R system design.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  12  Case A attribute 1: value 1 attribute 2: value 2  Case A attribute 1: value 1 attribute 2: value 2 attribute 3: value 3  Subcase A l attribute 3: value 3 attribute 4: value 4  Subcase A3 attribute 7: value 7 attribute 8: value 8 (a) Attribute-value pairs  Subcase A2 attribute 5: value 5 attribute 6: value 6  Subcase A 4 attribute 9: value 9 attribute 10: value 10  (b) Part-subpart relationships  Figure 2.1. Representation of a Case in Case Base  2.2.4 Control Issues of CBR  Once the above case representation issues are addressed, the following three general C B R processes, as often referred to as the control issues, can then be carried out sequentially:  1. Case retrieval, 2.  Case adaptation, and  3.  Case combination.  Case retrieval refers to the capability of a C B R system to accept queries and filters information from the case base which matches the queries. It is uncommon that the features in a design case  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  match completely with the ones in the retrieved cases.  13  The simplest approach would be to  retrieve and reuse the unchanged solution of the most similar case as the solution to the design case. While this simplistic approach reduces the computation and programming requirements needed to design a C B R system, it fails to account for the differences between the design and retrieved cases. Since no two problems are ever the same, it is necessary for any C B R system to have at least one of the adaptation or combination capability (Pu, 1998). Solution to the design case can then be constructed either by adapting or combining old solutions. In this manner, use of a C B R system is more advantageous as the adaptation or combination of previously solved solutions is easier than generating a new solution from scratch.  2.2.4.1 Case Retrieval  Case retrieval is a basic operation in any C B R design, but it plays a significant role in the establishment of a C B R system. Retrieval of cases can be done informally or formally. Informal case retrieval refers to the selecting of relevant cases from the case base by the user based on his/her experience and/or judgement. On the other hand, formal case retrieval refers to a C B R design system capable of accepting a new set of definition and/or features from the user and conducting searches on its case base for cases that have the same or similar problem specifications. ISECR utilizes the latter of the two retrieving techniques.  Retrieval of cases can be based on a perfect match, where the specifications of the design case are found to match exactly. However, it is more likely to find the features in the design case that do not match completely with the ones in the retrieved cases. Thus, most of the retrieved cases from a C B R system are partial matches.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  14  The degree of partial match depends on the relative similarity and importance of the features that make up a case. The degree of similarity of each feature can be calculated by comparing the specific feature in the design case to the corresponding feature of retrieved cases. Case distance can be computed and used as a measure to determine the degree of similarity between the design and retrieved cases. One of the most frequently used case distances is the Euclidean distance calculated as (Yeh, 1997):  2  Z  where  (Equation 2.1) i=\w,•  D  Case distance of the design case to the k" case  n  Number of input features  1  k  Importance factor for feature i ABS R  fl  Absolute value function Maximum value for feature i Minimum value for feature i  fl R  f  Ji,k  Value of feature i in the design case Value for feature i in the k' case  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  15  In Equation 2.1, the importance factor, w can be represented by a numerical value ranging from i  0. 0 to 1.0. A higher importance value (closer to 1.0) indicates that the feature is more important compared to the features having lower importance values. The value of case distance, D , k  as  determined by Equation 2.1, can also be any real number varying between 0.0 to 1.0. The more similar the design case to the retrieved case, the smaller the D value or shorter the case distance. k  Case distance can be used to rank cases, i.e., lower scoring cases are used before higher scoring ones. Inherently, cases are ranked based on a weighted sum of features in the design case that match the ones in the retrieved cases.  2.2.4.2 Case Adaptation  The main purpose of case adaptation is to modify the solution of the case retrieved from the case base to account for the differences between the design and retrieved cases. There are a number of adaptation methods available, i.e., reinstantiation, parameter adjustment, local search, case-based substitution, commonsense transformation, model-guided repair, special-purpose adaptation and repair, derivational reply, etc. In general, adaptation methods in C B R are classified into the following two categories:  1.  Structural adaptation: The retrieved case solution is substituted directly with adaptation rules and/or formulas to generate a new solution for the design case.  2. Derivational reply: The same methods at which the original case solution (from the retrieved case) is derived are reused to derive a new solution for the design case. Specifically, it is assumed that the same rules and/or formulas used to generate the retrieved case solution can be reused to produce the new solution to the design case. With this method, the sequence at  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  16  which the retrieved case is solved must be stored in the case base as an additional attribute to the case.  Although adaptation of cases is useful in many applications, many successful C B R systems do not perform adaptation at all or leave this option to the user. Watson (1997) suggested that adaptation should be avoided unless it can be carried out easily using simple adaptation methods such as reinstantiation or parameter adjustment.  Complex adaptation of cases is knowledge  intensive and can only be applied to domains that are well understood. Since C B R is generally employed to solve problems that are not well understood, a complex adaptation method that is knowledge intensive may not be feasible.  2.2.4.3 Case Combination  Case combination aims to derive the solution for the design case by combining several of the original solutions stored in the retrieved cases. Frequently, the solution stored in the most similar case may not be the best solution for the design case. Thus, it becomes necessary to retrieve several similar cases and combine their solutions in order to generate an improved solution to the current problem (Yeh, 1997). Case combination can be carried out in several ways, such as the weighted average approach, constructive approach, or frame approach (Pu, 1993, and Yeh, 1997). However, for the purpose of this research, the weighted average approach is adequate to the application and it is shown below:  weighted =  T  ,  7~  Pre-Implementation Evaluation of Safety Improvement Programs  (Equation  2.2)  Chapter 2: Literature Review  where  17  TWeighted = Weighted solution for the design case u  Number of retrieved cases used to generate the weighted solution Case distance of the design case to the k" case, as determined by 1  Equation 2.1 T  = Solution for the k" case 1  k  The term (1 — D )  in Equation 2.2 suggests that solutions obtained from cases with lower case  k  distances, i.e., more similar to the design case, are weighted more significantly compared to the ones with higher case distances. The accuracy of the above weighted solution can be represented by the standard deviation that has the following expression:  (Equation 2.3)  where  Stdev(T  )  Weighted  = Standard deviation of the weighted solution for the design case  2.2.5 CBR Methodologies  There are several C B R methodologies used in practice, each employing different combination of the three C B R processes described in Sections 2.2.4.1 to 2.2.4.3. In this research, two of the C B R methodologies are utilized in the design of ISECR and thus, discussed further below.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  18  2.2.5.1 Nearest Neighbour Approach  The first C B R methodology is called the nearest neighbour approach and is illustrated in Figure 2.2. This is the simplest of all C B R methodologies where the only C B R process used is case retrieval. With this approach, only the most similar case from the case base is retrieved and the result stored in this retrieved case is then utilized as the solution to the design problem.  2.2.5.2 Collaborative Approach  Moving to a more sophisticated level, the second C B R methodology is called the collaborative approach as shown in Figure 2.3. Collaborative C B R approach retrieves several cases from the case base and performs case combination to the retrieved cases. With this technique, A>nearest neighbour retrieval is used, where k refers to a predetermined number of cases to be retrieved. The retrieved case solutions are then combined (using Equation 2.2) to yield the solution for the design case. The accuracy of the solution generated for the design case can be estimated by calculating its standard deviation by using Equation 2.3. Essentially, collaborative C B R approach utilizes both case retrieval and case combination to derive a better-fit solution to the design case.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  19  F i g u r e 2.2. Nearest N e i g h b o u r A p p r o a c h  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  20  Combination  F i g u r e 2.3. C o l l a b o r a t i v e A p p r o a c h  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  21  2.3 Collision Analysis  The need for using the generalized linear regression modeling (GLIM) and the Empirical Bayes (EB) approaches arises as they address and overcome the problems associated with the conventional methods of predicting site-specific collision estimates.  The following sections  review some of the published research to construct a fundamental understanding on the above two techniques.  2.3.1 The Generalized Linear Modeling (GLIM) Approach  Most of the earlier work in collision analysis utilized the conventional linear regression approach to develop prediction models relating collisions to traffic volumes with the assumption of a Gaussian (normal) distributed error structure. However, several researchers (Jovanis and Chang, 1986, Hauer et al., 1988, Saccomanno and Buyco 1988, Miaou and Lum 1993) have all shown that conventional linear regression prediction models lack the distributional property to adequately describe the random, discrete, non-negative, and typically sporadic characteristics of traffic collisions.  To overcome these problems, Jovanis and Chang (1986) concluded, with  supporting results from their modeling of collisions at highway sections in Indiana, that a Poisson distribution should be used to describe the model error structure. Furthermore, Miaou and Lum (1993) also supported the use of a Poisson distributed error structure with their results. A l l in all, these researchers confirmed the shortcomings associated with the conventional linear regression models and their capabilities in predicting collision estimates.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  22  The G L I M modeling approach, on the other hand, can overcome the shortcomings associated with the conventional methods.  Specifically, utilizing the G L I M approach, the flexibility of  assuming different error structures is provided and the conversion of non-linear models into linear ones is also feasible.  As an example, the followings describe how Hauer et al. (1988) and  Kulmala (1995) utilized the G L I M approach to develop collision prediction models for intersections.  Assuming that Y is a random variable that describes the number of collisions in a specific time period, y is the observation of Y during a period of time, and the random variable A is regarded as the mean of Y. Thus, for A = X, Y is Poisson distributed with parameter X . If each site has its own regional characteristics with a unique mean collision frequency A , Hauer et al. (1988) have shown that for an imaginary group of sites having similar characteristics, A follows a gamma distribution. The gamma distribution, having parameters K and K//U , has a mean and variance that can be described with the following two equations:  E(A) = /u  (Equation 2.4)  Var{h) = —  (Equation 2.5)  K  Based on Equations 2.4 and 2.5, the point probability function of Y can be given by the negative binomial distribution with an expected value and variance described by Equations 2.6 and 2.7 respectively as shown below (Hauer et al. 1988, and Kulmala, 1995):  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 2: Literature Review  E(Y)=  23  (Equation 2.6)  M  Var(Y)=Li +  (Equation 2.7)  K  As shown above, unless K —> o o , the variance of the observed number of collisions is greater than its expected value. However, when K —> c o , the variance equals the expected value, which is identical to the Poisson distribution (Kulmala, 1995) and suitable to describe the nature of collisions.  2.3.2 The Empirical Bayes (EB) Approach  The E B approach is used to refine the G L I M estimate of the expected number of collisions at a location to yield a more accurate, location-specific safety estimate. Two types of clues of the location are used in the E B approach: its traffic and road characteristics, and its historical collision data (Hauer, 1992, Brude and Larsson, 1988). Utilizing the EB approach, Hauer (1992) calculated the expected number of collisions for any intersection using the following equations:  (Equation 2.8)  where  a  1  Var(E(A))  1+  E(A)  count = observed number of collisions  Pre-Implementation Evaluation of Safety Improvement Programs  (Equation 2.9)  Chapter 2: Literature Review  24  it (A) = predicted number of collisions estimated from the GLIM model = variance of the GLIM estimate  Since Var(E(h)) =  f  , Equation 2.8 can be rearranged as:  ( A \ E(A) T 7  EB Safety. Estimate  \  (K + count)  rc + E(A);  (Equation  2.10)  K  Finally, the variance of the EB estimate can be determined using Equation 2.11 as:  f  Var^EB f Sa  )—  etyEstimate  E(A) K +  ^  • (K + count)  (Equation  2.11)  E(A)  Hence, the safety estimate, i.e., the expected number of collisions and the associated uncertainties, can be evaluated by using Equations 2.10 and 2.11 respectively.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  25  3.0 C B R A P P L I C A T I O N I N I S E C R  3.1 Introduction  The computerized approach of the Information System for Estimating Crash Reductions (ISECR) is designed to minimize the amount of manual work required in evaluating the effectiveness of different safety improvements on a project-level basis. ISECR consists of historical information extracted from past evaluation studies that reported the performance of different safety improvements.  Utilizing a CBR approach, ISECR is capable of retrieving and analyzing  appropriate past records in order to assess the range and reliability of the predicted countermeasure effectiveness (CRFs) for a given safety improvement under certain condition. This chapter provides an overview of the implementation of CBR in ISECR by focusing on the followings:  1.  Case representation: Determine the contents/features used to represent a case, and their organization and structure in the ISECR case base.  2. Case retrieval: Establish the query parameters used to retrieve relevant cases. 3.  Case distance: Determine the criteria used to calculate the case distance for each of the retrieved cases.  4.  Solution construction: Establish the adaptation and/or combination strategies employed to modify and/or combine the retrieved solutions to create an improved solution for the design case.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  26  3.2 Case Representation in ISECR  Frequently, more than one CRF can be found in an evaluation study, i.e., some studies reported the effectiveness for more than one countermeasure while others evaluated the same countermeasure under different situations, etc. Hence, it is possible to represent an evaluation study with more than one case in the ISECR case base. The information contained within each case is organized using part-subpart relationships, as described in Section 2.2.3. The contents within each case are broken down into the following six subcases:  1. General information, 2. Case quality, 3. Countermeasure type, 4. Location type, 5. Location characteristics, and 6. Case solutions.  Within each subcase, attribute-value pairs are used to represent the features and associated values. The representation of each of the above six subcases is organized as follows and shown in Figure 3.1:  Subcase 1- General Information:  •  Case Id: reference point for a given case in the case base  •  General information on the evaluation study of which the case belongs to:  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  27  -  Study Id: an identifier used to reference the evaluation study  -  Author(s) of the study  -  Study title  Source of the study: journal, volume, pages, publication date, and country  Subcase 2- Case Quality: •  This subcase stores information regarding the treatment of the following three confounding factors: -  Changes in traffic volume  -  Inclusion of unrelated effects Regression to the mean (RTM) artifact  Subcase 3- Countermeasure Type:  •  A total of 116 countermeasure types are considered in this subcase (see Appendix A for details). They are organized and grouped into the following sixteen countermeasure categories: area-wide schemes, bridge improvements, cyclist/pedestrian facilities, delineation,  geometric  improvements,  intersection  improvements,  lane/shoulder  treatment, lighting improvements, object removal/relocation, parking improvements, pavement treatment, railway improvements, regulation change, safety barriers, traffic controls/signs, and traffic signals.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  28  Subcase 4- Location Type:  •  This subcase gathers the information on the type of location where the countermeasure effectiveness is investigated.  A total of eight location types are considered in this  research, i.e., general intersections, signalized intersections, unsignalized intersections, road sections, freeways, bridges, rails, and construction zones.  Subcase 5- Location Characteristics:  •  The location characteristics associated with a given case are extracted from the evaluation study and stored in this subcase.  Different location characteristics are considered for  each of the eight location types (see Appendix B for details). For example, the following characteristics are considered for signalized intersections (see Table 3.1): 1. Area type: urban, suburban, rural, or other 2.  Intersection type: four-legged, t-intersection, or y-intersection  3.  Implementation level: isolated location or wide area  4.  Total traffic volume: Total entering traffic volume (AADT) recorded for the intersection. The intervals are as follows: 0-4999, 5000-9999, 10000-14999, 1500019999, 20000-29999, 30000-39999, 40000-49999, 50000-59999,  60000-69999,  70000-79999, and 80000 and more. 5. Average lane width: Average width of all traffic lanes. i  between less than 12 feet and greater or equal to 12 feet. 6. Provision of left-turn channelization: yes or no  Pre-Implementation Evaluation of Safety Improvement Programs  The distinction is made  Chapter 3: CBR Application in ISECR  7.  Provision of right-turn channelization: yes or no  8.  Left-turn movement: not allowed, permissive, or protected  9.  Right-turn movement: not allowed, permissive, or protected  29  10. Street parking: allowed or not allowed 11. Type of traffic control: fixed-timed, semi-actuated, or fully-actuated 12. Average number of lanes per approach: less than or equal to two lanes or more than two lanes •  The above location characteristics are represented in the case base as different sets of attribute-value pairs. For example, Table 3.1 presents a set of attribute-value pairs to represent location characteristics for signalized intersections (see Appendix B for the representation of the location characteristics used for other location types). Representing these features in such a manner does not only minimize the storage requirement, but also facilitates the calculation of case distance, as will be shown later.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  30  Table 3.1. Representation of Location Characteristics for Signalized Intersections Characteristic Area type  Intersection type  Implementation level Total entering traffic volume (AADT)  Average lane width Provision of left-turn channelization Provision of right-turn channelization Left-turn movement  Right-turn movement  Street parking Type of traffic control  Average number of lanes per approach  Feature Value l=urban 2=suburban 3=rural 4=other l=four-legged 2=t-intersection 3=y-intersection l=isolated location 2=wide area 1=0-4999 2=5000-9999 3=10000-14999 4=15000-19999 5=20000-29999 6=30000-39999 7=40000-49999 8=50000-59999 9=60000-69999 10=70000-79999 11=80000 and more l=less than 12 ft 2=greater or equal to 12 ft l=yes 2=no l=yes 2=no l=not allowed 2=permissive 3=protected l=not allowed 2=permissive 3=protected l=allowed 2=not allowed l=fixed-timed 2=semi-actuated 3=fully-actuated l=less than or equal to 2 lanes 2=more than 2 lanes  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  31  Subcase 6- Case Solutions:  •  The reported collision reduction factors (CRFs) are entered into this subcase as the case solutions. CRFs corresponding to the following collision severity and types are entered into the case base (if available): -  Total collisions  -  Collision severity: fatal, injury, casualty, and property-damage-only. Collision types: angle, bike/pedestrian, fixed-object, head-on, left-turn, off-road, overtaking, parked vehicle, rear-end, right-turn, sideswipe, and other.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  32  Case A •General Information •Case Quality •Countermeasure Type •Location Type •Location Characteristics •Case Solutions  Figure 3.1. Representation of a Case in ISECR  With the above information entered into the case base, the ISECR user can then query for cases that report the effectiveness of a specific countermeasure at a specific location. Case retrieval strategies used in ISECR are further explained in the next section.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  33  3.3 Case Retrieval in ISECR  Case retrieval in any C B R system requires the user to input a set of definitions and/or features, which can be used to uniquely describe the current problem (design case). These specifications become the query parameters for filtering cases from the case base. When using ISECR, the user is first asked to specify both the type and location of the proposed countermeasure by selecting from the same lists used in Subcases 3 and 4 respectively (see Section 3.2). These two features are then used as the query parameters and are matched against the associated attribute-value pairs contained in each case stored in the case base. A case is only retrieved from the case base i f a perfect match of the two query parameters is found.  It is likely that more than one case with varying CRFs will match the above query criteria and will be retrieved from the case base. In this instance, it is essential to determine which of the retrieved cases and the associated results are more valid and/or relevant to the design case. As mentioned previously, a credibility factor and an application factor, or referred to in this thesis as the quality and relevance scores respectively, can be evaluated for each case to assess its validity and relevance.  The following sections discuss the quality and relevance scores in greater depth, the methods in which they are derived, followed by an explanation on how solution construction is implemented in ISECR.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  3.4  Quality  34  Score  Quality score is a quantitative measure used to determine the validity of a case and its results. In this thesis, the quality of a case is influenced by the treatment of the three common confounding factors, as considered in Subcase 2 (see Section 3.2) of each case.  Confounding factors are  factors that may affect the accuracy of the evaluation on the effectiveness of a safety improvement i f they are not accounted for in the retrieved case. The lack of treatment of these factors, in turn, can threaten the validity of cases and their results. Ultimately, by treating these confounding factors, one can decide i f the observed changes in road safety are caused by the implemented countermeasure, the existence of the confounding factors, or a combination of both.  Some researchers have attempted to account for the presence of confounding factors in evaluation studies. One approach is to assign arbitrary weights to evaluation studies based on their treatment of these factors. For example, Elvik has assessed the quality of evaluation studies based on several confounding variables (Elvik, 1995, 1996, and 1998). These variables include research design, decade of study, change in traffic volume, regression to the mean, collision migration, etc. In this thesis, the following three confounding factors are considered:  1. Changes in traffic volume, 2.  Inclusion of unrelated effects, and  3. Regression to the mean (RTM) artifact.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  35  3.4.1 Changes in Traffic Volume  Changes in traffic volume are usually controlled by calculating collision rates (Elvik, 1996). Collision rates can be expressed as collisions per million-entering-vehicles (col/mev) for intersections and collisions per million-vehicle-kilometers (col/mvk) for road sections. Changes in collision rate are often used as a measure of the effectiveness of a countermeasure. B y using collision rates, the number of collision is assumed to relate linearly with traffic volumes. However, this assumption is not always valid. In fact, a non-linear relationship between collision frequency and traffic volumes has been shown to be a more suitable assumption (Hauer et al., 1988). This indicates that the use of collision rates does not necessarily, under all circumstances, minimize the effect of traffic volumes on collision frequency. Nevertheless, in this thesis, similar to Elvik's approach (1996), cases that employ changes in collision rate as the measure of countermeasure effect are classified as having accounted for the confounding factor on the changes in traffic volume.  3.4.2 Inclusion of Unrelated Effects  Frequently, factors other than the treatment may affect the observed difference in collision frequency.  The inclusion of unrelated effects in a case may lead one to believe that the  implemented countermeasure is more effective than it really is. The presence of unrelated effects in a case can be controlled by using a comparison group when determining the CRF. Inherently, a comparison group is a group of sites that are somewhat similar to the treatment site. This group often consists of the total number of collisions in the area where the treatment area is located. The change in collision frequency of the comparison group can be compared to the one observed  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  36  for the treatment site. This permits the calculation of the actual treatment effects. Therefore, in this research, i f a case includes a comparison group when estimating the countermeasure effectiveness, it is classified as having treated for the confounding factor of including the unrelated effects.  3.4.3 Regression to the Mean (RIM) Artifact  Sites are usually selected for treatment due to their high collision occurrence. However, this high occurrence may be entirely caused by a random up-fluctuation of collision around the location's true mean (collision occurrence) value. If a location is selected for treatment solely because it undergoes an up-fluctuation in collision frequency, it will show a reduction in collision occurrence in the after period regardless of the implementation of the countermeasure. Hence, i f the R T M bias is not accounted for in a case, an overestimation of the effectiveness of a countermeasure can take place.  R T M artifact can be controlled with the use of a reference group and/or an appropriate analysis technique. A reference group should be selected to represent the treatment site, i.e., from the same potential treatment population. As for the analysis methods used to address the R T M bias, the Empirical Bayes technique is often used in practice. Thus, i f a case uses one of these two methods to remove the R T M effects, it is considered to have accounted for this confounding factor.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  37  3.4.4 Calculation of Quality Scores  In ISECR, quality scores are calculated automatically using the information contained in Subcase 2, i.e., case quality, and hence, not requiring any input from the user. Quality score, employing the Euclidean distance, can be computed using Equation 3.1 as shown below:  I 2  Y"  w ABS r  fi f  R  fi,k - f  R  .  D k{Quality)  where  (Equation 3-1)  D (Q k  )  Uality  = Quality score of the  k"  1  retrieved case  n  = Number of input features  Wj  = Importance factor for feature i  ABS  = Absolute value function  fit\ax  =  fUmm  =  fl  Maximum value for feature  i  Minimum value for feature  i  fl  = Value of feature i in the design case = Value for feature / in the k" retrieved case 1  k  In Equation 3.1, values of 3.0 and 0.0 are assigned to f  R  and f  R  mm  min  as the maximum and  minimum numbers of confounding factors that can be accounted for in a case respectively. A  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  38  feature value, f , is assigned to each case based on its treatment of the three confounding R  t k  factors.  For example, i f two of the three confounding factors are accounted for in a case, a  feature value of 2.0 is assigned. Similarly, i f a case has accounted for none, one, or all of the confounding factors, a feature value of 0.0, 1.0, or 3.0 is given respectively. In Equation 3.1, a value of 1.0 is assigned to w as the only feature considered at hand is the treatment of the three t  confounding factors.  Lastly, a value of 3.0 is assigned to ff  as the optimum number of  confounding factors that can be accounted for in any case.  Below is a sample calculation of the quality score (using Equation 3.1) for a case having accounted for two of the three confounding factors:  Y" D k(Quality)  w, • ABS  fi f  _  R  J i,max  1=1  fi,k f  R  J /,min  1  \.0-ABS\  3-2 V 3^0  1.0  \  2  = 0.33  In the above calculation, a quality score of 0.33 is calculated for the k"' retrieved case as it has accounted for two of the three confounding factors. Alternatively, i f none, one, or all of the three confounding factors are treated, a quality score of 1.0, 0.66, or 0.0 can be computed respectively. Clearly, the smaller the quality score, the more valid the case and its corresponding solutions.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  39  3.5 Relevance Score  Not all cases retrieved from the ISECR case base, based on querying for the same countermeasure and location types, have the same location characteristics as the design case. For example, i f the design case deals with an urban signalized intersection and a total entering traffic volume of 10000 A A D T , not all of the retrieved cases have the same site features as the design case. Hence, it would be reasonable to assign more weight to a case and its results i f it has more similar site features compared to the design case.  Relevance score determined for each case is influenced by the degree of similarity in the location characteristics between the design and retrieved cases, as provided by the ISECR user and those already stored in the case base respectively.  The location characteristics considered, as  mentioned previously, are different for each of the eight location types (see Appendix B for details).  3.5.1 Calculation of Relevance Scores  In this thesis, relevance score for each retrieved case is determined by utilizing the Equation 3.1. To calculate the relevance score using Equation 3.1, the values of f  and fi  R  R  nax  min  assigned to  each feature of a case are based on the specific feature encountered. For example, suppose that the encountered feature is traffic volume, values of 11.0 and 1.0 are assigned to f  R mM  and  f  R min  to represent the maximum traffic volume of 80000 and more A A D T and the minimum traffic volume of 0-4999 A A D T respectively. As for the values of f/  Pre-Implementation Evaluation of Safety Improvement Programs  and f , R  k  they are based on the  Chapter 3: CBR Application in ISECR  40  specific features of the design and retrieved cases respectively (see Appendix B for details). Each feature is weighted equally in Equation 3.1, i.e., the value of w: is dependent on the number of features considered.  For instance, a value of 0.5 is assigned i f two features are considered.  Similarly, a value of 0.33, 0.25, or 0.20 is assigned to w. i f three, four, or five, features are considered respectively.  To demonstrate how Equation 3.1 can be used to determine the relevance score for a retrieved case, consider the example below where the design case deals with an urban signalized intersection with a total entering traffic volume of 10000 A A D T , while the retrieved case concerns with a suburban signalized intersection with a total entering traffic volume of 5000 A A D T . These features are represented in ISECR as follows:  Table 3.2. Example: Representation of the Location Characteristics Feature Values Maximum  Minimum  Design Case  Retrieved Case  Area type  4  1  1 (urban)  2 (suburban)  Total Entering traffic volume (AADT)  11  1  3 (10000-  2 (5000-9999)  Pre-Implementation Evaluation of Safety Improvement Programs  14999)  Chapter 3: CBR Application in ISECR  41  The relevance score for the retrieved case can be determined by using Equation 3.1 as:  Y" D  w. • ABS  fi fi,k - f!R  f  R  J /,max  A: (Re levance)  XV  J i, min  0.5 • ABS,r J  i-2V 4-1  . . • ABS ._J3-2^ + 0.5  2  11-1  0.5 + 0.5  W;  Dk(Relevance) ~ 0.246  A lower relevance score signifies that the location characteristics are more similar between the design and retrieved cases, and vice versa.  3.6 Calculating Case Distance in ISECR  Case distance is evaluated to determine the relative importance of a case compared to others. When computing case distance, D , for each of the retrieved cases, the ISECR user can decide to k  include either the quality score alone or a combination of the quality and relevance scores. If the user decides to exclude the relevance score and only uses the quality score, D  k  is equivalent to  the quality score computed for the retrieved case. However, i f the user decides to encompass both the quality and relevance scores in determining D , both scores are weighted equally as in k  the expression below:  — _ '{Pk(Quality)  +  At(Re/erance))  Pre-Implementation Evaluation of Safety Improvement Programs  (Equation 3-2)  Chapter 3: CBR Application in ISECR  42  where D = Case distance of the k" retrieved case 1  k  For example, with the quality and relevance scores (0.33 and 0.246) computed for the two examples in Sections 3.4.4 and 3.5.1 respectively, case distance for the retrieved case can be determined in the following two ways:  Dk ~ ^k(Quality) ~  D  k  =\\D  k { Q u a l i t y )  +D  m  e  l  e  v  a  n  c  e  )  )=±.  (0.33 + 0.246) = 0.288  The first result indicates that the ISECR user has decided to exclude the relevance score in determining the case distance for the retrieved case. If this is the case, case distance is exactly the same as the quality score determined, i.e., in this example, 0.33. Conversely, the second result indicates that both the quality and relevance scores are utilized in determining D . In this k  instance, the two scores are weighted equally and determined to be 0.288.  Hence, a lower case distance is assigned to a case if it has treated more confounding factors and/or if its location characteristics are more similar to the design case.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  43  3.7 Solution Construction in ISECR  In ISECR, solutions to the design problem can be constructed utilizing either the nearest neighbour or collaborative approaches (see Section 2.2.5 for details).  3.7.1 Nearest Neighbour Approach  The nearest neighbour approach utilizes the most relevant case (lowest case distance) and its result is used as the solution to the design case. The ISECR user is presented with a list of all matched cases ranked in an ascending order according to the calculated case distances. The user can then select the most appropriate case and adapt the case solutions (CRFs), i f necessary, to account for the differences between the design and retrieved cases.  3.7.2 Collaborative Approach  The collaborative C B R approach, on the other hand, utilizes more than one retrieved case, combines their results, and then employs the combined result as the solution to the current problem. With this approach, the A:-nearest neighbour retrieval is used, where k is the number of cases to be retrieved and utilized in solution combination. There is little evidence in the literature indicating what the optimum k value should be to produce the best results. Not only that, the k values found in the literature vary greatly from one application to the next.  For example,  Gonzalez and Laureano-Ortiz (1992) combined outcomes from the three most relevant cases (if available) while Yeh (1997) utilized the results stored in all of the retrieved cases.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  44  The approach employed in ISECR uses all of the retrieved cases to derive the new solution (the effectiveness of the proposed countermeasure and its uncertainty). Although this approach may include results from cases that have lower case distances, it should be realized that utilizing solutions stored in the most similar cases may not necessarily provide the best solution to the new problem (Yeh, 1997). Furthermore, this report intends to provide both the non-weighted average and weighted average solutions (CRFs) based on the results obtained from all of the retrieved cases. The non-weighted average is simply the arithmetic mean of the retrieved CRFs, while the weighted result is determined by weighting the retrieved CRFs with their case distances. Equations 3.3 and 3.4 are used in ISECR to determine the non-weighted and weighted CRFs respectively:  CRF,  Non-Weighted  (Equation 3.3)  N  (Equation 3.4)  CRF, Weighted  where  CRF,Non-Weighted  = Non-weighted solution (CRF)  CRF  = Weighted solution (CRF)  V  Weighted  = Number of retrieved cases = Case distance of the k" retrieved case 1  CRF  K  = C R F of the if* retrieved case  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  45  The accuracy of the above results can be estimated with the standard deviations as:  Y^JCRF.-CRF) = ]j —~  2  °'CRF-Non-weighted  (Equation 3.5)  Z ^-D )(CRF -CRFf k  k  °CRF'-Weighted  where  z;  o~  _  CRF  0  = 1  _  Non  CRF-weighted  k  Waighted  =  (Equation 3.6)  d-^)-(/v-i)  = Standard deviation of the non-weighted solution Standard deviation of the weighted solution  The provision of the weighed results attempts to account for either the quality or both the quality and relevance of a case, i.e., solutions obtained from cases with lower case distances are weighted more and vice versa. This is demonstrated in the following example as shown in Table 3.3:  Table 3.3. Example: Determining Non-Weighted and Weighted CRFs Ranking of the  ( aso Distance,  (1-D ) A  ri-trk-w-d case  Case Result.  Weighted Case  C/?f*("..)  Ui-siill ("..)  1  0.25  0.75  23  r.25  2  0.5  0.5  25  12.5  3  0.75  0.25  33  8.25  4  1.0  0.0  40  0  Sum  2.5  1.5  121.0  38.0  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 3: CBR Application in ISECR  46  The above table demonstrates that cases with lower case distances are weighted considerably higher than those with higher case distances. The non-weighted and weighted CRFs can be determined by using Equations 3.3 and 3.4 respectively, as shown below:  CRF f Non-Weighted L R  CRF  WEIGHTED  =  121-0 _ ~  ~  ~  J^lfi-D^-CRF, ,  ~  =  ,  38^0  = —  30.25%  = 25.33%  Hence, the non-weighted and weighted CRFs are 30.25% and 25.33% respectively. In this example, the weighted CRF is less than the non-weighted CRF. This is expected as the weighted CRFs reflect results that have been accounted for some or all of the three confounding factors, as mentioned previously. In some instances, ISECR provides weighted CRFs that are slightly larger than the non-weighted results. This arises mainly due to the insufficient data available and/or the small number of evaluation studies available for the specific safety improvement implemented at the specific location.  Continuing with the example shown in Table 3.3, the standard deviations for the non-weighted and weighted CRFs can be estimated with Equations 3.5 and 3.6 respectively as:  ZljCRF -CRF)  IT8Z75  2  K  CRF-Non-Weighted ~ y  A ^ —1  _  Pre-Implementation Evaluation of Safety Improvement Programs  ~  Chapter 3: CBR Application in ISECR  47  (1 - D ) • (CRF - CRF)  2  k  <7, CRF-Weighted  k  i  (1^83 4.5  = 2.05%  Thus, the standard deviations for the non-weighted and weighted CRFs are 7.81% and 2.05% respectively.  Clearly, the non-weighted standard deviation is significantly greater when  compared to the weighted standard deviation.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  48  4.0 DESCRIPTION OF ISECR  4.1 Main Menu  ISECR is designed as an user-friendly intelligent database that can facilitate the daily use of practitioners in the highway safety engineering industry. A prototype of ISECR is currently implemented in a personal computer using Microsoft Access 97 in a Microsoft Windows 95/98 environment. Currently, the database consists of 450 documents.  In addition to predicting the effectiveness of safety improvements and their reliability, ISECR is designed to accept queries, filter and display information based on the users' specifications, and permit the entry of new documents. This chapter provides a description of the features available in ISECR.  Specifically, the first four of the following five ISECR Main Menu options are  discussed (see Figure 4.1):  1. View all documents, 2.  Search documents,  3. Predict C R factors, 4.  Input new documents, and  5.  Exit (ISECR).  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  49  Information System for Estimating Crash Reductions  Figure 4.1. ISECR Window: Main Menu  4.2 View All Documents  With the View All Documents option, the user can list all evaluation studies stored in the ISECR database. As illustrated in Figure 4.2, ISECR allows the user to list and sort the available studies based on:  1. Author name (including all authors and co-authors), 2. Study source,  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  3.  Study title, or  4.  Publication date.  50  View All Documents  List / Sort Documents by:  Figure 4.2. ISECR Window: View All Documents  Once the sorting option is selected, ISECR then presents a summary of the documents including the following information (see Figure 4.3):  1. Authors'names, 2. Study title, 3.  Study source,  4.  Publication date,  5.  Availability of the one page summary for the document, and  6. Availability of the document.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  E H Return  u  Print  51  <J  Help  ADAMS. P.  R&TR (4/4, pp. 88-100)  1995  .  | J 2 | A G E N T . K.  TRAFFIC CONTROL AND ACCIDENTS AT RURAL HIGH-SPEED INTERSECTIONS  TRANSPORTATION RESEARCH RECORD (1180, pp. 14-21)  1888  |[  3l| AGENT, K.  DEVELOPMENT OF WARRENTS FOR LEFTTURN LANES  KENTUCKY DEPARTMENT OF TRANSPORTATION (RESEARCH REPORT 526, pp.  1979-07  ||  4 AGENT, K. and DEEN.R.  RELATIONSHIPS BETWEEN ROADWAY GEOMETRICS AND ACCIDENTS  TRANSPORTATION RESEARCH RECORD (541, pp. 1-11)  1975  ||  5l||AGENT. K.  WARRANTS FOR LEFT-TURN LANES  TRANSPORTATION QUARTERLY (37/1, pp. 99-114)  |[  6| AGENT, K.  j!  7;|  AGENT, K. and CREASEY. T.  TRANSVERSE PAVEMENT MARKINGS FOR SPEED CONTROL AND ACCIDENT REDUCTION  TRANSPORTATION RESEARCH RECORD (773, pp. 11-14)  1980  DELINEATION OF HORIZONTAL CURVES  KENRUCKY TRANSPORTATION CABINET (UKTRP.86-4.pp. 1-42)  138603  I'rint  I Record;  l< I  VJT  1383-01 |  View Summary  Exit  1'. »*r»f|P»| oF 450(Filtered) Figure 4.3. ISECR Window: Summary of Documents  If the one page summary of the document is available, the user can click on the View Summary button to view the summary of the document. Figure 4.4 shows an example of the document summary. The available summaries in the ISECR database are provided by G.D. Hamilton & Associates Consulting LTD.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  Author  52  Title  Adams P.  File: adams.dot  Traffic signals and roundabouts: Are they really safer?  Reference  Year  County  R&TR Vol. 4 No. 4  1995  Aus  Location  Sydney, Australia.  Level  System wide. 11 traffic signal and 13 roundabouts sites studied .  Methodology  Two years before and after accident data. A control was introduced using the entire Local Government Area.  Shortcomings  No information on roundabouts and intersections characteristics  Road and vehicle characteristics Accident cause and pattern  NA  Safety countermeasure or Strategy Effectiveness  Traffic signals and roundabouts  Three classes of accident severity: • PDO • Non-admitted injury • admitted injury and fatalities  Mean of % change Traffic signal at intersection Roundabouts  -31.2  Percentage change  (stdev 63.04)  -61.7 (stdev 39.8)  -41.7  Adjusted percentage changes (control section) -35.1  -77.7  -71.1  See Table 3 for detail with accident categories Miscellaneous  NA  NA = Non Applicable or Non Available. Ns = not significant.  Figure 4.4. ISECR Window: An Example of One Page Summary  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  53  4.3 Search Documents  This feature of the software provides the user a search capability on documents by entering a set of query parameters. The following query parameters are included (see Figure 4.5):  1. Author name, 2. Publication year, 3. Country, 4. Countermeasure, and 5. Location.  Search Documents  Figure 4.5. ISECR Window: Selecting Query Parameters  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  54  Once the query parameters are selected, the database is searched and a summary of the matched documents is presented similarly as shown in Figure 4.3.  4.4 Predict CR Factors (CRFs)  This option allows the user to input the specifications of the new problem and to predict the effectiveness of a specific safety improvement.  Utilizing a C B R approach, as explained in  Chapters 2 and 3, past records reporting CRFs can be retrieved and analyzed to generate results for the current problem. Once the Predict CR Factors option is chosen, the user is first presented a list of sixteen countermeasure categories as illustrated in Figure 4.6.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  55  Piedict CR Factors  Select a Countermeasure Category: #  Area-wide schemes  #  Bridge imp rove merits  @ Cyclist / Pedestrian facilities i t Delineation #  Geometric improvements  @ Intersection improvements >@ ' Lane / Shoulder treatment. @ Lighting improvements #  0 b j e ct re tn oval / re I o cati o n  © Parking improvements #  Pavement treatment  @ Rai I way i rn p rove rn e nts @ Regulation change #  Safety barriers  @ Traffic controls / signs % Traffic signals  I  i  Figure 4.6. ISECR Window: Countermeasure Categories  By clicking on any one of the sixteen categories, another window consists of different countermeasures (belonging to the selected countermeasure category) is opened. For example, Figure 4.7 is a result of selecting Delineation as the countermeasure category. provides the details when other countermeasure categories are selected.  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix C  Chapter 4: Description of ISECR  56  Predict CR Factors (cont.)  Select a Countermeasure:  Figure 4.7. ISECR Window: Countermeasure Types for Delineation  Once the countermeasure type is selected, the user is required to click on the Continue button before the location type window can be opened. This window allows the user to specify where the proposed countermeasure is to be implemented. The location type window, as shown in Figure 4.8, consists of eight different location types.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  57  Predict CR Factors (cont.)  Select a Location Type: Intersections #  General Intersections  #  Signalised Intersections  #  Unsignalized Intersections  ;1  •-. -  Road Sections and Freeways & IRoad Sections i #  Freeways  Others #  Bridges  •S Rail Crossings #  Construction Zones  Figure 4.8. I S E C R Window: Location Types  After the location type is specified, ISECR searches its case base for cases dealing with the same countermeasure and location types. A case is only retrieved if a perfect match of the two query parameters is realized. If no matched case is found with the specified query parameters, the user will be notified, as illustrated in Figure 4.9:  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  58  No match found.  Click YES to continue search Click MO to quit.  Yei  No  Figure 4.9. ISECR Window: No Match Found  On the other hand, i f cases are found and retrieved, the user will then be prompted with a dialog box to enter the location characteristics which would be used later in the calculation of case distances. This is shown in Figure 4.10. Once the Yes button is clicked, the user is presented with an input form, as shown in Figure 4.11 for signalized intersections, to enter location characteristics (see Appendix D for the input forms used for other location types). In ISECR, the implementation level for the design case is assumed to be at an isolated location.  This  assumption is made i f the user decides to use location characteristics in the analysis. Once the user finishes entering location characteristics and clicks on the Continue button, he/she is warned by ISECR to use quality criterion only i f few location characteristics were entered. As shown in Figure 4.12, the user can then decide i f case distances will be calculated based on the quality scores alone or based on both the quality and relevance scores.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  59  ISECR  f)  e  Do you want (o input site characteristics and use then for analy : i ; ?  Yes  No  Cancel  Figure 4.10. ISECR Window: Selecting to Enter Location Characteristics  Predict CR Factors (cont.)  Select Location Characteristic^): Are a T y p e  urban  Intersection T y p e T y p e of Traffic Control Total Traffi c V o I u rn e (AADT) A v e . Number of Lanes per Approach  Jra  mm  Average Lane Width Left-turn Channelisation Right-turn Channelisation Left-turn Movement Right-turn Movement Street Parking  Figure 4.11. ISECR Window: Input Location Characteristics for Signalized Intersections  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  60  3 It is advised to use quality criterion only if few location characteristics were entered in the location type window.  Click YES to weight results ba;ed on both quality and relevance criteria Click NO to weight results based on qualify criterion  Yes  No  Figure 4.12. ISECR Window: Selecting the Criteria to Calculate Case Distances  However, i f No is clicked in the ISECR window (Figure 4.10) to indicate that no location characteristics will be entered, case distances will only be calculated based on considering the quality scores alone. Whether the user decides to use only the quality scores or both the quality and relevance scores in determining the case distances, ISECR then presents a summary of ranked evaluation studies, which are essentially the retrieved cases that match the query parameters. This summary is displayed in Figure 4.13.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  61  Information System for Estimating Crash Reductions - [Search Summary]  Search Summa  Figure 4.13. ISECR Window: Summary of Ranked Documents  Since some evaluation studies may consist of more than one case that matched the query parameters, the ranking of the studies is achieved by assigning each study a case distance only when the calculated case distance for that retrieved case is the lowest among all of its retrieved cases. The studies are then ranked according to the case distance assigned, as shown in the following example:  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  62  Table 4.1. Example: Ranking of Evaluation Studies Study (Study Id)  Retrieved Case (Case Id)  Case Distance  1 2  1 3 2 6 7 4 5 8 9  0.7 0.i25 0.25 0.25 0.3 0.3 0.3 0.3 0.3  Case Distance assigned to the study? l.s Fes No Fes Fes No No No No  10  0.35  Yes  1  3 4 2 2 4 4 5  Hence, with the above example, the following five studies are listed in the ISECR summary window (see Figure 4.13):  Table 4.2. Example: Final Output to the Summary Window Ranking  Study (Study  1 2 3 4 5  1 2 3 4 5  Id)  Retrieved Case (Case Id) 1 3 6 7 10  Case Distance 0.1 0.125 0.25 0.3 0.35  Finally, to determine the countermeasure effectiveness, the user can click on the Calculate Results button, as shown in Figure 4.13. As mentioned in Chapter 3, ISECR utilizes all of the retrieved cases to derive the solutions (CRFs and the associated standard deviations) required for the current problem. Thus, with the same example as the one shown in Table 4.1, a total of ten cases from five evaluation studies would be used.  The predicted CRFs and their standard  deviations for total, and various collision severity and types are presented in an ISECR window as  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  63  shown in Figure 4.14. The non-weighted and weighted CRFs are determined by using Equations 3.3 and 3.4 respectively. As for the non-weighted and weighted standard deviations, Equations 3.5 and 3.6 are used respectively.  Query Results: Collision Reduction Factors a n d their Standard Deviations  Query Results:  (66 Documents Found)  Countermeasure: Delineation General| Location: Road Sections Collision Non-Weighted Non-Weighted Tjpe/Severity CRF(W) Standard Dev. mBham  [Kofi  Injury:  47.3  34.4  Casualty:  24.4  Total: Fatal:  _  Weighted  Weighted Standard Dev..  '  PDO:  Figure 4.14. ISECR Window: Predicted CRFs and their Standard Deviations  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 4: Description of ISECR  64  4.5 Input New Documents  The Input New Documents option allows new documents to be added to the ISECR database. However, in order to assure the quality and consistency of data entry, input of new documents in ISECR is limited to the agency responsible for the database maintenance. As far as the types of information entered for each document and how they are represented in the database, Section 3.2 should be referred.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 5: Validation of ISECR  65  5.0 VALIDATION OF ISECR  5.1 Introduction  Validation is an essential step to confirm the success of any computer software.  In order to  validate the results produced by ISECR, this chapter is devoted to compare the CRFs predicted by ISECR and those extracted from the literature for 15 safety improvements (see Table 5.1 for details). These improvements were selected randomly from the 116 countermeasures considered in ISECR (see Appendix A for details).  5.2 ISECR Results in the Context of Published Studies  Pertinent portions of four previously published sources (Tamburri and Smith, 1971, Creasey and Agent, 1985, McFarland et al., 1978, Terry and Watson, 1982) that have compiled and tabulated a summary of CRFs for various countermeasures are summarized in Table 5.1. Table 5.1 also provides both the non-weighted and weighted CRFs produced by ISECR for the chosen 15 countermeasures.  Upon inspection of Table 5.1, it is evident that the scope of each study varies greatly from one to another. The CRFs presented in Table 5.1, while representative of the four published studies, should not be considered to be complete as there are additional collision data presented in these reports which are not relevant for the purpose of this thesis and thus, excluded from Table 5.1.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 5: Validation of ISECR  66  Table 5.1. Summary of CRFs: ISECR Results vs. Published Results C'oiink'rmeasure Type  Tamburri  C'rcascv &  Smith l«'71 Bridge improvements Widen bridges Geometric improvements Horizontal alignment Vertical alignment Hor & vert alignment Sight distance Intersection improvements Left-turn chan. Lighting improvements Install at intersections Pavement treatment Pavement grooving Resurfacing Skid reduction Railway improvements Flashing beacons Traffic controls/signals 4-way stops Traffic signals Flashing beacons red-yellow New signals 'Wet pavement collisions only  '  ;  Agent 1985  CRF (lotal Collisions) .McFarlami Terry ISKCK et al. Non1078 W alson W eiglited  30-65 20-40 15-54 50-52 20-31  40-88  65  63.5  41  25.6 30.3 40.4 33.5  23.8 29.1 32.7  38.2  31.9  20-21 31  15 75  b  75  a  ISKCK Weighted  75  50°  43.8,43.8  36.6, 40.3  10-48 12- 42 13- 50  12-44 21  58.3 22.4 31.4  19.0 14.7  64.5  60.3  73  62.8  44.1  26 20  54.0 34.0 25.6  21.0  b  C  70-94 70  68-70  50 15  34 15-80  37 6-29  C  "'Night time collisions only Rural area type  c  By examining Table 5.1, it is evident that there is a general agreement between the ISECR results and the published results. In some instances, the ISECR CRFs are considerably higher than those reported by the published literature. For example, while McFarland et al. (1978) reported a C R F of 37% for implementing flashing beacons, ISECR predicted a CRF of 54%. The variation may be due to the difference in the sample size of studies and the approach undertaken by each study in determining the CRFs. For instance, by examining the ISECR outputs, it is noted that a small sample size of studies is used in the determination of the CRF, i.e., only four studies are retrieved  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 5: Validation of ISECR  from the ISECR database.  67  Not only that, of the four studies retrieved, three (Mayer, 1971,  Cribbins and Walton, 1970, Wilson, 1967) of them are outdated and analyzed their collision data by using the simple before and after approach. Although the studies are outdated, the ISECR result is an indication of how effective the countermeasure is, without correcting for the confounding factors. Further examination of the ISECR outputs for other countermeasures, it is also noted that the ISECR predictions are more valid and comparable to the literature results when there is a larger sample size available for the specific countermeasure.  Based on the  countermeasures examined in Table 5.1, it is apparent that more work is required to enter new evaluation studies into the ISECR database to further increase the reliability of the ISECR results for some countermeasures.  Nevertheless, for the majority of the countermeasures examined  above, ISECR does provide valid results.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 6: Expected Collision Reduction  68  6.0 EXPECTED COLLISION REDUCTION  6.1 Introduction  The safety benefits of implementing a safety improvement can be represented by the expected reduction in collision frequency, which is normally calculated by the product of C R F and the expected number of collisions (denoted by N).  This thesis has proposed the use of a C B R approach, one of the recent developments in problemsolving paradigms in artificial intelligence, to assist in developing an intelligent database (ISECR) that is capable of assessing the range and reliability of the predicted countermeasure effectiveness. With ISECR, CRFs are determined on a project-level basis where the variability of geometric configuration among different locations is considered. Moreover, ISECR addresses the uncertainty issues related to the effectiveness of the proposed safety improvement. Specifically, non-weighted and weighted CRFs and their standard deviations can be determined with ISECR as shown previously.  The expected number of collisions, N , at a given location can be evaluated by procedures such as the multivariate approach, i.e., the G L I M (generalized linear modeling) approach, and the E B (Empirical Bayes) approach. As discussed in Chapter Two, these techniques can be used to readily provide more accurate site-specific safety estimates compared to the conventional approaches. The outcomes from these techniques are drawn upon in the next section, i.e., the expected value and standard deviation of N .  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 6: Expected Collision Reduction  69  The next section outlines the procedures involved in evaluating the collision reduction and its uncertainty expected from implementing a safety improvement.  Specifically, the moment  approach (Benjamin and Cornell, 1970, and Ang and Tang, 1984) is utilized to combine CRFs and expected number of collisions and their uncertainties.  6.2 Collision Reduction and Its Uncertainty  With the availability of the expected values and standard deviations of both C R F and N , the expected reduction in collision frequency, Z, and its variance can now be calculated. Assuming that C R F and N are independent of each other, i.e., no correlation between the two variables, the expected value of Z can be determined by the following equation:  E(Z) = NxCRF  where  (Equation 6.1)  E(Z)  = Expected reduction in collision frequency in the after period  N  = Expected number of collisions  CRF = Collision reduction factor  The accuracy of Z is represented by the variance which can be calculated as:  Var(Z) = N (cr ) 2  2  CRF  where  + CRF (CJ ) 2  2  n  + (a,CRF  Var(Z) = Variance of Z  Pre-Implementation Evaluation of Safety Improvement Programs  (Equation 6.2)  Chapter 6: Expected Collision Reduction  o~  = Standard deviation of CRF  o~  = Standard deviation of N  CRF  N  The application of the above procedures is illustrated with an example in Chapter Eight.  Pre-Implementation Evaluation of Safety Improvement Programs  70  Chapter 7: Economic Analysis  71  7.0 ECONOMIC ANALYSIS  7.1 Introduction  There are a number of methods that can be used to evaluate the economic feasibility of implementing a safety improvement. One of the most frequently used evaluation measures is the benefit-cost ratio (BCR). The B C R is a measure of the amount of dollar return expected with every dollar spent on a safety improvement.  The expected benefits from implementing a  countermeasure can be evaluated by the savings anticipated from the reduction in collisions. With the expected value and variance of Z determined in Chapter Six, the uncertainty issues associated with B C R can now be addressed.  Specifically, the probability of a proposed  countermeasure achieving a preset B C R can now be determined.  7.2 Benefit-Cost Ratio (BCR) and Its Uncertainty  The expected value of B C R for implementing a countermeasure can be determined by the following equation:  CoSti pi m  where  ( ti  emen  a  on  E(BCR)  = Expected value of B C R  E(Z)  = Expected reduction in collision frequency in the after period  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 7: Economic Analysis  72  Col.Cost  = Average collision cost  t  = Payback period (year) = Discount rate  ( P / A, i, t)  = Present worth factor, given the payback period and discount rate  (Col.Cost) x (PI A,i,t)  k  (Equation 7.2)  implementation  Assuming that the only random variable in Equation 7.1 is Z , i.e., k is a constant variable, the variance of B C R can then be expressed by Equation 7.3:  Var(BCR) = k  2  where  xVar(Z)  Var(BCR)  = Variance of B C R  Var(Z)  = Variance of Z , as defined by Equation 6.2.  (Equation 7.3)  7.3 Probability Density Function of BCR  With the expected value and variance of BCR, the probability density function of B C R can now be established. This thesis utilizes a Gamma distribution to model B C R , as this distribution is suitable for modeling continuous random variable and providing wide variety of shapes. Furthermore, this distribution is also limited to positive values and skewed to the right, which is appropriate to model B C R . The Gamma distribution parameters, a and 6, and the probability density function of B C R can be calculated by the following equations:  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 7: Economic Analysis  _ p =  73  E(BCR) (Equation 7.4)  a = E(BCR) x B  (Equation 7.5)  f(BCR;a,B)  (Equation 7.6)  where  = J—fBCRy-'e™*™ T(a)  a and B  = Gamma distribution parameters  / (BCR,a, 6) = Probability density function of B C R  Once the above probability density function is defined, the cumulative distribution function for B C R can be formulated as follows:  F(BCR;a,B)  = f(BCR;a,B)d(BCR) }  (Equation 7.7)  o  where  F(BCR; a, (3) = Cumulative distribution function of B C R  By plotting the cumulative distribution function of B C R , the probability of a countermeasure achieving a specific B C R upon its implementation can be determined. This is illustrated with an example in the next chapter.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 8: Applications  74  8.0 APPLICATIONS  8.1 Problem Definition  The procedures outlined in the previous chapters can be used to evaluate the cost-effectiveness of road safety improvements. For example, assume that a traffic safety engineer is interested in determining the probability of achieving a 2:1 return, i.e., a benefit-cost ratio (BCR) of 2.0, by improving public lighting along an arterial street (or known as road sections in the location type feature used in this research) that has the following characteristics:  •  Area type: suburban  •  Implementation level: isolated location (this is automatically assumed by ISECR when the user decides to include the relevance criterion in the calculation of case distances)  •  Total traffic volume: 25000 A A D T  With the above problem specifications, the remaining sections in this chapter intend to:  1. Determine the effectiveness of the proposed countermeasure, i.e., CRFs and the standard deviations. 2.  Calculate the reduction in collision anticipated once the countermeasure is implemented by utilizing the moment approach, as discussed in Chapter Six.  3.  Compute the B C R and its variance for the proposed countermeasure and plot its cumulative distribution function to assess the probability for the proposed countermeasure achieving a B C R of 2.0.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 8: Applications  75  8.2 Countermeasure Effectiveness  The effectiveness of the proposed countermeasure is evaluated by utilizing cases currently available in the ISECR case base. Relevant cases are retrieved only i f the same countermeasure and location types match between the design case and cases stored in the ISECR case base. Querying the ISECR case base with general lighting improvements and road sections as the countermeasure and location types respectively, a total of 53 cases from 38 documents are retrieved. Table 8.1 provides a list of the retrieved cases.  To determine the case distance for each of the retrieved cases, the quality and relevance scores are considered. Tables 8.1 and 8.2 summarize the quality (treatment of the confounding factors) and relevance (location characteristics) information for each case, as extracted from Subcases 2 and 5 from the ISECR case base respectively. With the information available, both the quality and relevance scores can be determined by using Equation 3.1, as shown below for Case Id 296:  Y" D k (Quality)  w ABS r  fi  f  R  J i,max  fi,k  -  f  R  J i,n\]  Pre-Implementation Evaluation of Safety Improvement Programs  1.0 -ABS  3-1 V V3 - 0 ,  1.0  0.667  Chapter 8: Applications  y  76  w, • ABS  D k(Re levance)  f i R  i=l  D^Relevance) ~  fi,k  f - f. R J i,max J i mm J  Q.5-ABS\  '  4-1  + 0.5-^55  1 - 2^ 2- 1  0.5 + 0.5  0-745  As mentioned previously, when both the quality and relevance scores are considered when determining a case distance, they are weighted equally. For Case Id 296, case distance can be evaluated using Equation 3.2 as below:  D =\k  +D  k(Ke  l e v m c e )  ) = i (0.667 + 0.745) = 0.706  Using the same approach, case distances are computed for all of the retrieved cases and the results are as listed in Table 8.3. Furthermore, Table 8.3 also presents the results (CRFs) extracted from the retrieved cases, however, in this example, only CRFs for total collisions are considered.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 8: Applications  77  Table 8.1. Example: Retrieved Cases and their Quality Scores Retrieved Study Case ((use Id) (Study Id) Changes in Traffic Volume No 169 54 Yes 192 59 Yes 238 68 No 257 73 No 267 74 No 296 77 No 297 77 Yes 314 423 Yes 319 424 No 328 434 No 359 448 No 406 101 No 407 101 No 408 101 No 463 116 No 480 119 No 498 124 Yes 689 78 Yes 690 78 No 713 196 No 766 1 No 796 229 No 835 376 No 854 403 No 855 403 No 856 403 No 861 254 Yes 869 256 No 870 257 No 885 262 No 893 265 No 894 265 No 895 265 No 896 265 No 897 265 No 898 265 No 899 265  Treatment of the Confounding Factors Inclusion of 1 II related Kffccts No No No No No Yes Yes Yes Yes No No No No No No Yes No No No No No No No Yes Yes Yes No Yes Yes Yes No No No No No No No  Pre-Implementation Evaluation of Safety Improvement Programs  RIM Artifact .v. No No No No No No Yes Yes No No No No No No No No No No No No No No No No No No Yes No No No No No No No No No  Total U of Factors Treated 0 1 1 0 0 1 1 3 3 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 1 0 3 1 1 0 0 0 0 0 0 0  Qualit> Score. f^kllJmiliiM  1.000 0.667 0.667 1.000 1.000 0.667 0.667 0.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 0.667 1.000 0.667 0.667 1.000 1.000 1.000 1.000 0.667 0.667 0.667 1.000 0.000 0.667 0.667 1.000 1.000 1.000 1.000 1.000 1.000 1.000  Chapter 8: Applications  78  Table 8.1. Example: Retrieved Cases and their Quality Scores (cont.) Retrieved Study Case (Case Id) (Study Id) Changes in Traffic Volume 267 No •J01 902 267 No 903 267 No 27 923 No 925 89 No 930 282 No 942 295 No 944 297 No 311 958 No 292 1253 No 1 No 2800 2805 285 No 2820 409 Yes 2828 413 No 2867 493 No 2872 496 No  Treatment of the Confounding Factors Inclusion of Unrelated KfTccts No No No No No No No No No No No No Yes No No No  Pre-Implementation Evaluation of Safety Improvement Programs  RT.M Artifact No No No No No No No No No No No No Yes No No No  Total # of Factors 'Created 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0  Quality Score. 1.000  1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 1.000 1.000 1.000  Chapter 8: Applications  79  Table 8.2. Example: Retrieved Cases and their Relevance Scores Retrieved Study Case (Case Id) (Study Id) 169 192 238 257 267 296 297 314 319 328 359 406 407 408 463 480 498 689 690 713 766 796 835 854 855 856 861 869 870 885 893 894 895 896 897 898 899 901  54 59 68 73 74 77 77 423 424 434 448 101 101 101 116 119 124 78 78 196 1 229 376 403 403 403 254 256 257 262 265 265 265 265 265 265 265 267  Location ( liaracteristics Area Type Other Other Other Other Other Urban Urban Rural Other Urban Other Rural Rural Urban Urban Urban Urban Urban Urban Urban Rural Rural Other Urban Rural Other Urban Rural Rural Urban Other Other Other Other Other Urban Rural Other  Implementation Traffic Volume I ,evel (AADT) Wide Area Wide Area Wide Area Wide Area Wide Area Wide Area Wide Area Wide Area Wide Wide Wide Wide Wide  Area Area Area Area Area  Wide Area Wide Area Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide  Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area  Pre-Implementation Evaluation of Safety Improvement Programs  Relevance Score, DkiKtlt-iumil  0 S50  0.850 0.850 0.850 0.850 0.745 0.745 0.745 0.667 0.745 0.850 0.745 0.745 0.745 0.333 0.333 0.333 0.745 0.745 0.333 0.745 0.745 0.850 0.745 0.745 0.850 0.745 0.745 0.745 0.745 0.850 0.850 0.850 0.850 0.850 0.745 0.745 0.850  Chapter 8: Applications  80  Table 8.2. Example: Retrieved Cases and their Relevance Scores (cont.) Retrieved Study Case (Case Id) (Study Id) 902 903 923 925 930 942 944 958 1253 2800 2805 2820 2828 2867 2872  267 267 27 89 282 295 297 311 292 1 285 409 413 493 496  1 .ocalion Characteristics Area Type Other Other Other Urban Other Other Other Other Other Rural Other Other Other Other Other  Implementation Traffic Volume Level (AADT) 5000-00')') Wide Area Wide Area Isolated Location 20000-29999 Isolated Location Wide Area Wide Wide Wide Wide Wide Wide Wide Wide  Area Area Area Area Area Area Area Area  Pre-Implementation Evaluation of Safety Improvement Programs  Relevance Score. ^klReltvamvi  0.715 0.850 0.385 0.236 0.850 0.667 0.850 0.850 0.850 0.745 0.850 0.850 0.850 0.850 0.667  Chapter 8: Applications  81  Table 8.3. Example: Retrieved Cases (Ranked) and their Case Distances and Results ( use Id Study Id  W) 192 238 257 267 296 297 314 319 328 359 406 407 408 463 480 498 689 690 713 766 796 835 854 855 856 861 869 870 885 893 894 895 896 897 898 899 901 902 903  54 59 68 73 74 77 77 423 424 434 448 101 101 101 116 119 124 78 78 196 1 229 376 403 403 403 254 256 257 262 265 265 265 265 265 265 265 267 267 267  Quality Score,  Relevance Case Score. Distance,  DklQualifyi  Dk(Rrh-iance)  D  1.000 0.667 0.667 1.000 1.000 0.667 0.667 0.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 0.667 1.000 0.667 0.667 1.000 1.000 1.000 1.000 0.667 0.667 0.667 1.000 0.000 0.667 0.667 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000  0.850 0.850 0.850 0.850 0.850 0.745 0.745 0.745 0.667 0.745 0.850 0.745 0.745 0.745 0.333 0.333 0.333 0.745 0.745 0.333 0.745 0.745 0.850 0.745 0.745 0.850 0.745 0.745 0.745 0.745 0.850 0.850 0.850 0.850 0.850 0.745 0.745 0.850 0.715 0.850  0.925 0.758 0.758 0.925 0.925 0.706 0.706 0.373 0.333 0.873 0.925 0.873 0.873 0.873 0.667 0.500 0.667 0.706 0.706 0.667 0.873 0.873 0.925 0.706 0.706 0.758 0.873 0.373 0.706 0.706 0.925 0.925 0.925 0.925 0.925 0.873 0.873 0.925 0.858 0.925  Case Result,  CRF <%) k  <%)  k  Pre-Implementation Evaluation of Safety Improvement Programs  Weighted Case Result  ••.'•75 0.242 0.242 0.075 0.075 0.294 0.294 0.627 0.667 0.127 0.075  25.00 79.00 40.00 60.00 75.13 45.28 22.00 5.00 57.00 30.00  1.13 6.04 19.10 3.00 4.50 22.09 13.31 13.80 3.33 7.26 2.25  0.127  30.00  3.82  0.500 0.333 0.294  9.00 30.00 58.00  4.50 10.00 17.05  0.333 0.127 0.127 0.075 0.294 0.294 0.242  30.00 58.00 30.00 21.00 63.57 63.33 73.09  10.00 7.38 3.82 1.58 18.69 18.62 17.67  0.627 0.294 0.294  10.00 20.00 50.00  6.27 5.88 14.70  0.075 0.075  30.00 21.00  2.25 1.58  0.127 0.127 0.075 0.142 0.075  38.00 36.90 59.00 14.00 26.00  4.84 4.70 4.43 1.99 1.95  15.00  •  Chapter 8: Applications  82  Table 8.3. Example: Retrieved Cases (Ranked) and their Case Distances and Results (cont.) Case Id Study Id Qualil> Score, DklQualitv)  923 925 930 942 944 958 1253 2800 2805 2820 2828 2867 2872  27 89 282 295 297 311 292 1 285 409 413 493 496  1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 1.000 1.000 1.000  Relevance C ase 0-0,.) Score, Distance, DkfRelcvancei  0.385 0.236 0.850 0.667 0.850 0.850 0.850 0.745 0.850 0.850 0.850 0.850 0.667  Case Result. Weighted CRt\ (%) Case Result (%)  D  k  0.692 0.618 0.925 0.833 0.925 0.925 0.925 0.873 0.925 0.425 0.925 0.925 0.833  Sum  Pre-Implementation Evaluation of Safety Improvement Programs  0.382 0.075  -19.00 24.69  -7.26 1.85  0.075 0.127 0.075  -18.00 17.00 69.00  -1.35 2.16 5.18  0.075  30.00  2.25  8.263  1327.99  260.38  Chapter 8: Applications  83  Table 8.3 illustrates again that cases with lower case distances are weighted more significantly than those with higher case distances. The non-weighted and weighted CRFs for total collisions can be determined by using Equations 3.3 and 3.4 respectively, as shown below:  CRF  =  > , CRF. - =  13?7 99  = 34 94% Jt.^H/o  ^-^Non-Weighted  C  R  T J-D )-CRF  F  k  k  260.38  k  Therefore, for the proposed countermeasure, i.e., general lighting improvement, the non-weighted and weighted CRFs are 34.94% and 31.51% respectively. As expected, the weighted CRF is less than the non-weighted C R F as with the weighted result, the confounding factors have been accounted for. Finally, the accuracy of these estimates can be represented by their standard deviations.  The standard deviations for both the non-weighted and weighted CRFs can be  determined with Equations 3.5 and 3.6 respectively, as shown below:  £ &CRF-Non-Weighted  °CRF-Weighted  M  (CRF - CRFf k  N-l  Y^-D^iCRF.-CRFf ZLd-^)-(^-l)  _ /212221J  ^  %  V 38-1  _ (5287^5 V 305.73  Pre-Implementation Evaluation of Safety Improvement Programs  =  4  1  6  %  Chapter 8: Applications  84  The standard deviations for the non-weighted and weighted CRFs are 23.94% and 4.16% respectively.  Clearly, the non-weighted standard deviation is significantly greater than the  weighted standard deviation.  8.3 Expected Collision Reduction  From the analysis conducted in Section 8.2, the expected values for the non-weighted and weighted CRFs are 34.94% and 31.51%, while the standard deviations are 23.94% and 4.16% respectively. Assuming that from the collision analysis conducted by the safety engineer, the expected number of collisions, N , for this location is 20.0 collisions per year (col/yr) with a standard deviation of 2.5 col/yr. Equations 6.1 and 6.2 can now be used to calculate the expected value and variance of Z respectively. For example, with the non-weighted results, the expected value of Z and its variance can be computed as follows:  E ( Z ) = Nx CRF = 20.0 x 0.3494 = 6.99col I yr  Var(Z) = N (o- ) +CRF (o- ) 2  2  2  CRF  + (a  2  N  = 20 (0.2394) +0.3494 (2.5) 2  2  2  2  x cr )  2  CRF  N  + (0.2394 x 2.5) = 24.06(co//.yr) 2  As for the weighted results, E(Z) = 6.30col I yr determined in a similar fashion as shown above.  and Var(Z) = \32{col I yr)  2  can be  The above results represent the expected  reductions in total collisions and the uncertainties at the location.  Pre-Implementation Evaluation of Safety Improvement Programs  2  Chapter 8: Applications  85  8.4 Economic Analysis  With the results determined in Section 8.3, the expected value of B C R and its accuracy can now be evaluated. For the purpose of this example, the variable k in Equation 7.1 is assumed to be 0.25. Hence, the non-weighted B C R and its variance can be determined with Equations 7:1 and 7.3 respectively, as shown below:  E(BCR) = kx E(Z) = 0.25 x 6.99 = 1.747  Var(BCR) = k  2  x Var(Z) = 0.25 x 24.06 = 1.504 2  Similarly, the weighted B C R and its variance can be calculated and are determined to be 1.576 and 0.083 respectively.  Therefore, given the expected value and variance of B C R , the two  gamma distribution parameters, a and f3 corresponding to the non-weighted B C R can be determined using Equations 7.4 and 7.5 respectively as follows:  EjBCR) P  =  _ 1.747  Var(BCR) ~ 1.504  = 1.161  a = E(BCR) x fi = 1.747 x 1.161 = 2.03  Similarly, the a and /? parameters for the weighted B C R are 30.017 and 19.051 respectively. It follows that with the known gamma parameters, a cumulative distribution can be plotted as shown in Figure 8.1 (see Appendix E for the probability density plot).  Pre-Implementation Evaluation of Safety Improvement Programs  Subsequently, the  Chapter 8: Applications  86  probability of achieving a specific economic goal can be evaluated prior to the implementation of a countermeasure. For example, i f it is desired to determine the probability of achieving a B C R of 2.0 with the proposed countermeasure (general lighting improvement), Figure 8.1 indicates that the probabilities of achieving this goal (or having a B C R of less or equal to 2.0) are 61.5% and 80.6%o based on the non-weighted and weighted B C R curves respectively. Based on Figure 8.1, it is clear that the expected range of B C R represented by the weighted B C R curve is considerably smaller than the one indicated by the non-weighted curve, i.e., a B C R range of 1.0 to 2.5 compared to a range of 0.0 to 6.0 respectively.  Cummulative Distribution of BCR  0  1  2  3  4  5  Benefit-Cost Ratio  —  -Weighted  NonWeighted  Figure 8.1. Example: Cumulative Distribution Plot of BCR  Pre-Implementation Evaluation of Safety Improvement Programs  6  Chapter 9: Conclusion  87  9.0 CONCLUSION  This thesis first describes the development of ISECR, which is now a functional intelligent database on the M S Access platform. ISECR maintains a case base that consists of published literature which quantifies crash reduction benefits for various safety improvements. Utilizing a C B R approach, ISECR permits users to query the system for cases similar to the current situation, retrieves and then summarizes the retrieved solutions to estimate the range and reliability of the countermeasure effectiveness on a project level.  With the ISECR outputs, this thesis then illustrates the use of the moment approach to determine the expected collision reduction and its uncertainties for specific countermeasures.  Lastly, the  technique involving the probability assessment of achieving a specific benefit-cost ratio for a specific countermeasure is also presented in the thesis. The evaluation procedure illustrated in this report attempts to increase the confidence of predictions when evaluating the expected benefits from safety improvement programs.  Currently, 450 evaluation studies are entered into the ISECR case base. To further increase the usefulness and applicability of ISECR, new evaluation studies should be evaluated and entered into the database on a regular basis. Based on the available cases in the database, ISECR has shown to provide results that are comparable to the results obtained from real cases, given that there is an adequate sample size of retrieved studies that matches the user's query. Nevertheless, further data testing and validation of the system from experts and end-users are still necessary to improve the prototype ISECR.  Pre-Implementation Evaluation of Safety Improvement Programs  Chapter 9: Conclusion  88  The confounding factors considered in ISECR can be further expanded to include other factors, such as collision migration. A different weighting scheme for the confounding factors can also be introduced to highlight the relative importance of each confounding factor. The same can be applied to the location characteristics considered in this thesis, i.e., other location characteristics, in addition to the ones included in ISECR, can also be introduced and a different weighting scheme can also be employed to emphasize the importance of some characteristics.  In addition, the prototype ISECR can be improved by providing its user the capability of predicting CRFs for different combinations of countermeasures, instead of one countermeasure as currently allowed in ISECR.  Frequently, more than one countermeasure is considered at a  location, i.e., intersection improvements may include a new left-turn lane, new delineation, lane widening.  Thus, it would definitely be beneficial to be able to query for the effect of a  combination of countermeasures at a location.  Additional risk analysis should also be performed for the economic procedure illustrated in this thesis.  This is essential as the discount rate, i, and project life, t, used in Equation 7.1 were  assumed to be constant over time. Nevertheless, these two variables can fluctuate during the life of the project and are subject to changes in interest rate, risk premium, market condition, government policy, etc. Hence, further risk analysis incorporating the random nature of these two variables should be conducted.  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Post-implementation evaluation system methodology and output, Traffic and Safety Division, New York State Department of Transportation.  Watson, I. (1997). Applying case-based reasoning: techniques for enterprise systems, Morgan Kaufmann Publishers, Inc.  Wilson, J. (1967). Simple types of intersection improvements, Highway Research Board, Special Report 93, pp.144-159.  Yeh, L C . (1997).  Case-based approaches for preliminary design of steel building frames,  Microcomputers in Civil Engineering, Vol.12, pp.327-337.  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix A: Countermeasure Types  APPENDIX A: C O U N T E R M E A S U R E TYPES  Pre-Implementation Evaluation of Safety Improvement Programs  94  Appendix A: Countermeasure Types  95  Table A . l . Countermeasure Types Countermeasure Category 1. Area-wide schemes  2.  Bridge improvements  3.  Cyclist/Pedestrian facilities  4.  Delineation  5.  Geometric improvements  Countermeasure Type General Blackspot treatment Enforcement New roads Traffic calming: general Traffic calming: speed bumps/humps Traffic planning/Management General Widen bridges General bicycle safety improvements General pedestrian safety improvements Pedestrian crossings Pedestrian overpasses/underpasses Pedestrian signals General Pavement markings: general Pavement markings: edge lines Pavement markings: median edge line Pavement markings: right edge line Raised pavement markers Reflected guide posts Strips: general Strips: rumble Strips: transverse General Alignment: general Alignment: horizontal Alignment: vertical Median: general Median: close median openings Median: concrete/attenuator barriers Median: install new median Median: upgrade existing median Median: widen existing median Sight distance Staggered intersection Superelevation  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix A: Countermeasure Types  96  Table A . l . Countermeasure Types (cont.) ( ouiitcrmeasiire Category 6. Intersection improvements  7. Lane/Shoulder treatment  8. Lighting improvements  9. Object removal/relocation  10. Parking improvements  11. Pavement treatment  Countermeasure T\pe General Channelization: general Channelization: left-turn lane: general Channelization: left-turn lane with left-turn phase Channelization: left-turn lane without left-turn phase Channelization: right-turn lane Turning bay/traffic island Roundabouts: general Roundabouts: installation/upgrade General 2-way left-turn lane Acc./Decel./Passing lane Bicycle lane Bus lane Climbing lane Flattening side-slopes H O V lane Lane/shoulder: general Lane/shoulder: narrowing Lane/shoulder: widening Lane addition General Install new lighting Upgrade existing lighting General Relocation: general Relocation: fixed objects Relocation: utility poles Removal: general Removal: fixed objects Removal: trees Removal: utility poles General Change from angle- to parallel parking Eliminate parking: general Eliminate parking: parallel parking General Pavement grooving Resurfacing Skid reduction  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix A: Countermeasure Types  97  Table A . l . Countermeasure Types (cont.) Countermeasure Category 12. Railway improvements  13. Regulation change  14. Safety barriers  15. Traffic controls/signs  16. Traffic signals  Countermeasure Type General Automatic gates Flashing beacons General Modify speed limit: general Modify speed limit: decrease Modify speed limit: increase Prohibit turns: general Prohibit turns: left-turns General Crash cushions Guardrails: general Guardrails: double-sided Safety poles/posts General Guidance signs Install new signs/upgrade existing signs Regulatory signs: general Regulatory signs: speed Regulatory signs: stop signs: general Regulatory signs: stop signs: 2-way to 4-way stops Regulatory signs: stop signs: 4-way stops Regulatory signs: minor-leg stops Regulatory signs: yield signs Warning signs: general Warning signs: flashing beacons/signals General Actuated signals Advance warning signs Coordinated signals Flashing beacons/signals: general Flashing beacons/signals: all-way red Flashing beacons/signals: red-yellow Install new signals/upgrade existing signals Phasing: general Phasing: left-turn phase Phasing: pedestrian phase Phasing: timing Removal signals  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix B: Location Characteristics Considered for Each of the Eight Location Types  98  APPENDIX B: LOCATION CHARACTERISTICS CONSIDERED FOR EACH OF THE EIGHT LOCATION TYPES  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix B: Location Characteristics Considered for Each of the Eight Location Types  Table B.l. Representation of Location Characteristics for General Intersections Characteristic Area type  Intersection type  Implementation level Total entering traffic volume (AADT)  Average lane width Provision of left-turn channelization Provision of right-turn channelization Left-turn movement  Right-turn movement  Street parking Average number of lanes per approach  Feature \ alue l=urban 2=suburban 3=rural 4=other l=four-legged 2=T-intersection 3=Y-intersection l=isolated location 2=wide area 1=0-4999 2=5000-9999 3=10000-14999 4=15000-19999 5=20000-29999 6=30000-39999 7=40000-49999 8=50000-59999 9=60000-69999 10=70000-79999 11=80000 and more l=less than 12 ft 2=greater or equal to 12 ft l=yes 2=no l=yes 2=no l=not allowed 2=permissive 3=protected l=not allowed 2=permissive 3=protected l=allowed 2=not allowed l=less than or equal to 2 lanes 2=more than 2 lanes  Pre-Implementation Evaluation of Safety Improvement Programs  99  Appendix B: Location Characteristics Considered for Each of the Eight Location Types  Table B.2. Representation of Location Characteristics for Signalized Intersections Characteristic Area type  Intersection type  Implementation level Total entering traffic volume (AADT)  Average lane width Provision of left-turn channelization Provision of right-turn channelization Left-turn movement  Right-turn movement  Street parking Type of traffic control  Average number of lanes per approach  Feature Value l=urban 2=suburban 3=rural 4=other l=four-legged 2=T-intersection 3=Y-intersection l=isolated location 2=wide area 1=0-4999 2=5000-9999 3=10000-14999 4=15000-19999 5=20000-29999 6=30000-39999 7=40000-49999 8=50000-59999 9=60000-69999 10=70000-79999 11=80000 and more l=less than 12 ft 2=greater or equal to 12 ft l=yes 2=no l=yes 2=no l=not allowed 2=permissive 3=protected l=not allowed 2=permissive 3=protected l=allowed 2=not allowed l=fixed-timed 2=semi-actuated 3=fully-actuated l=less than or equal to 2 lanes 2=more than 2 lanes  Pre-Implementation Evaluation of Safety Improvement Programs  100  Appendix B: Location Characteristics Considered for Each of the Eight Location Types  Table B.3. Representation of Location Characteristics for Unsignalized Intersections ('haractcristic  Feature Value  Area type  l=urban 2=suburban 3=rural 4=other l=four-legged 2=T-intersection 3=Y-intersection l=isolated location 2=wide area 1=0-4999 2=5000-9999 3=10000-14999 4=15000-19999 5=20000-29999 6=30000-39999 7=40000-49999 8=50000-59999 9=60000-69999 10=70000-79999 11=80000 and more l=less than 12 ft 2=greater or equal to 12 ft l=yes 2=no l=yes 2=no l=not allowed 2=permissive 3=protected l=not allowed 2=permissive 3=protected l=allowed 2=not allowed 1 uncontrolled 2=2-way stops 3=4-way stops l=less than or equal to 2 lanes 2=more than 2 lanes  Intersection type  Implementation level Total entering traffic volume (AADT)  Average lane width Provision of left-turn channelization Provision of right-turn channelization Left-turn movement  Right-turn movement  Street parking Type of traffic control  Average number of lanes per approach  Pre-Implementation Evaluation of Safety Improvement Programs  101  Appendix B: Location Characteristics Considered for Each of the Eight Location Types  Table B.4. Representation of Location Characteristics for Road Sections Characteristic Area type  Implementation level Total traffic volume (AADT)  Average lane width Provision of left-turn channelization Provision of right-turn channelization Street parking Average number of lanes Passing/Acceleration/ Deceleration Lanes  Feature \ alue l=urban 2=suburban 3=rural 4=other l=isolated location 2=wide area 1=0-4999 2=5000-9999 3=10000-14999 4=15000-19999 5=20000-29999 6=30000-39999 7=40000-49999 8=50000-59999 9=60000-69999 10=70000-79999 11=80000 and more l=less than 12 ft 2=greater or equal to 12 ft l=yes 2=no l=yes 2=no l=allowed 2=not allowed 1-less than or equal to 2 lanes 2=more than 2 lanes l=yes 2=no  Pre-Implementation Evaluation of Safety Improvement Programs  102  Appendix B: Location Characteristics Considered for Each of the Eight Location Types  Table B.5. Representation of Location Characteristics for Freeways Characteristic Area type  Implementation Level Total traffic volume (AADT)  Average lane width Average number of lanes Passing/Acceleration/ Deceleration Lanes  geature \ aluc l=urban 2=suburban 3=rural 4=other l=isolated location 2=wide area 1=0-4999 2=5000-9999 3=10000-14999 4=15000-19999 5=20000-29999 6=30000-39999 7=40000-49999 8=50000-59999 9=60000-69999 10=70000-79999 11=80000 and more l=less than 12 ft 2=greater or equal to 12 ft l=less than or equal to 2 lanes 2=more than 2 lanes l=yes 2=no  Pre-Implementation Evaluation of Safety Improvement Programs  103  Appendix B: Location Characteristics Considered for Each of the Eight Location Types  Table B.6. Representation of Location Characteristics for Bridges, Rails, and Construction Zones Characteristic Area type  Implementation Level Total traffic volume (AADT)  Average lane width Average number of lanes  Feature Value l=urban 2=suburban 3=rural 4=other l=isolated location 2=wide area 1=0-4999 2=5000-9999 3=10000-14999 4=15000-19999 5=20000-29999 6=30000-39999 7=40000-49999 8=50000-59999 9=60000-69999 10=70000-79999 11=80000 and more l=less than 12 ft 2=greater or equal to 12 ft l=less than or equal to 2 lanes 2=more than 2 lanes  Pre-Implementation Evaluation of Safety Improvement Programs  104  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  105  APPENDIX C: ISECR WINDOWS: COUNTERMEASURE TYPES CONSIDERED FOR DIFFERENT COUNTERMEASURE CATEGORIES  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  106  Predict CR Factors (cont.)  Select a Countermeasure: <& General $  B l a c k s p o t treatment  $  Enforcement  $  N e w roads  $  Traffic calming: general  $  Traffic calming: s p e e d bumps/humps  #  Traffic planning/Management  : .~ •  Figure C.l. ISECR Window: Countermeasure Types for Area-Wide Schemes  Predict CR Factors (cont.)  Select a Countermeasure:  Figure C.2. ISECR Window: Countermeasure Types for Bridge Improvements  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  107  Predict CR Factors (cont.)  Select a Countermeasure: @ General bicycle safety improvements $  G e n e ral p e d e stri an s afety i m p rove m e nts  #  Pedestrian crossings  #  Pedestrian overpasses/underpasses  $  Pedestrian signals  Figure C.3. ISECR Window: Countermeasure Types for Cyclist/Pedestrian Facilities  Predict CR Factors (cont.)  Select a Countermeasure: @ General $  P a v e m e nt m arki n g s: g e n e ral  #  P a v e rn e nt m arki n g s: e d g e I i n e s  #  Pavement markings: median edge line  •S P a v e rn e nt m arki n g s: ri g ht e d g e I i n e $  Rai s e d p ave m e nt m arke rs  $  Reflected guide posts  $  Strips: general  #  Strips: rumble  #  Strips: transverse  Continue:  Figure C.4. ISECR Window: Countermeasure Types for Delineation  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  Predict CR Factors (cont.)  Select a Countermeasure: 9 General #  Alignment general  #  Alignment: horizontal  #  Alignment: vertical  #  Median: general  #  Median: close median openings  #  M e d i an: co n crete/atte n u ato r b arri e rs  #  M e d i an: i n stal I n ew median  #  Median: upgrade existing median  W  Median: widening existing median  #  Sight distance  #  Staggered intersection  #  Superelevation  Figure C.5. ISECR Window: Countermeasure Types for Geometric Improvements  Pre-Implementation Evaluation of Safety Improvement Programs  108  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  109  Predict CR Factors (cont.]  @ General #  Channelization: general  $  Channelization: left-turn lane: general  #  Channelization: left-turn lane with left-turn phase  #  Channelization: left-turn lane without left-turn phase  #  Channelization: right-turn lane  #  Tu rn i n g b ay/traff i c i s I an d  $  Roundabouts: general  ® Roundabouts: installation/upgrade  ! Continue  Exit  Figure C.6. ISECR Window: Countermeasure Types for Intersection Improvements  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  110  Predict CR Factors (cont.)  Select a Countermeasure: <3 General $  2-way left-turn lane  #  A c c . / Decel./ Passing lane  W Bicycle lane % Bus lane #  Climbing lane  % Flattening sideslopes % HOVIarte #  Lane/shoulder: general  #  Lane/shoulder: narrowing  % Lane/shoulder: widening >§t Lane addition  Figure C.7. ISECR Window: Countermeasure Types for Lane/Shoulder Treatment  Predict CR Factors (cont.)  Select a Countermeasure: ©General #  Install new lighting  W  Upgrade existing lighting  Figure C.8. ISECR Window: Countermeasure Types for Lighting Improvements  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  Predict CR Factors (cont.)  Select a Countermeasure: ® General $  Relocation: general  #  R e I o cati o n: f ixe d o b j e cts  $  R e I o cati o n: uti I ity poles  $  R e m o v a l : general  % Removal: fixed objects #  Fie rn oval: trees  #  R e rn oval: utility poles  Figure C.9. I S E C R Window: Countermeasure Types for Object Removal/Relocation  Predict CR Factors (cont.)  Select a Countermeasure: f  & General  #  Ch an g e fro m an g I e- to p aral I e I p arki n g  #  Eliminate parking: general  #  Eliminate parking: parallel parking  Figure C.10. I S E C R Window: Countermeasure Types for Parking Improvements  Pre-Implementation Evaluation of Safety Improvement Programs  111  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  Predict CR Factors (cont.)  Select a Countermeasure: <9 General #  P a v e rn e nt grooving  ®  Resurfacing  #  Skid reduction  Figure C . l l . I S E C R Window: Countermeasure Types for Pavement Treatment  | Predict CR Factors (cont.)  Select a Countermeasure: @  General  #  Automatic gates  W Flashing b e a c o n s  Figure C.12. I S E C R Window: Countermeasure Types for Railway Improvements  Pre-Implementation Evaluation of Safety Improvement Programs  112  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  Predict GR Factors (cont.)  Select a Countermeasure:  Figure C.13. ISECR Window: Countermeasure Types for Regulation Change  Predict CR Factors (cont.)  Select a Countermeasure:  Figure C.14. ISECR Window: Countermeasure Types for Safety Barriers  Pre-Implementation Evaluation of Safety Improvement Programs  113  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  Predict CR Factors (cont.)  Select a Countermeasure: f  & General  #  Guidance signs  #  Install new signs/ jpgre.de existing signs  $  Regulatory signs general  ® Regulatory signs s p e e d #  Regulatory signs stop signs: general  $  Regulatory signs stop signs: 2-way to 4 ^ / a y stops  $  Regulatory signs sto p s i g n s: 4-way sto p s  W  Regulatory signs stop signs: minor-leg stops  #  Regulatory signs yield signs  <W Warning signs: general $  'Warn i n g s i g n s: f 1 as h i n g b e aco n s/s i g n al s  Figure C.15. ISECR Window: Countermeasure Types for Traffic Controls/Signs  Pre-Implementation Evaluation of Safety Improvement Programs  114  Appendix C: ISECR Windows: Countermeasure Types Considered for Different Countermeasure Categories  Predict CR Factors (cont.)  Select a Countermeasure: @ General $  Actuated signals  #  A d v a n c e d warning signals  #  Coordinated signals  $  Flashing beacons/signals: general  $  Flashing beacons/signals: all-way red  #  Flashing beacons/signals: red-yellow  $  I n stal I n ew s i g n al s/u p g rad e exi sti n g s i g n al s  #  Phasing: general  $  Phasing: left-turn phase  #  Phasing: pedestrian phase  #  Phasing: timing  #  R e m o v a l signals  -Continue:  Figure C.16. ISECR Window: Countermeasure Types for Traffic Signals  Pre-Implementation Evaluation of Safety Improvement Programs  115  Appendix D: ISECR Windows: Input Forms Used to Enter Location Characteristics for Different Location Types  APPENDIX D: ISECR WINDOWS: INPUT FORMS USED TO ENTER LOCATION CHARACTERISTICS FOR DIFFERENT LOCATION TYPES  Pre-Implementation Evaluation of Safety Improvement Programs  116  Appendix D: ISECR Windows: Input Forms Used to Enter Location Characteristics for Different Location Types  117  Predict CR Factors ( c o n t )  Figure D.l. ISECR Window: Input Location Characteristics for General Intersections  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix D: ISECR Windows: Input Forms Used to Enter Location Characteristics for Different Location Types Predict CR Factors (cont.)  Select Location Characteristic^  Continue  Exit  Figure D.2. ISECR Window: Input Location Characteristics for Signalized Intersections  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix D: ISECR Windows: Input Forms Used to Enter Location Characteristics for Different Location Types  119  Predict CR Factors (cont.)  Select Location Characteristic^  Continue  Figure D.3. ISECR Window: Input Location Characteristics for Unsignalized Intersections  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix D: ISECR Windows: Input Forms Used to Enter Location Characteristics for Different Location Types  Figure D.4. ISECR Window: Input Location Characteristics for Road Sections  Pre-Implementation Evaluation of Safety Improvement Programs  120  Appendix D: ISECR Windows: Input Forms Used to Enter Location Characteristics for Different Location Types  121  Predict CR Factors (cont.)  Select Location Characteristic(s):  Continue  Figure D.5. ISECR Window: Input Location Characteristics for Freeways  Figure D.6. ISECR Window: Input Location Characteristics for Bridges, Rails, and Construction Zones  Pre-Implementation Evaluation of Safety Improvement Programs  Appendix E: Probability Density Plot of BCR for the Public Lighting Example  APPENDIX E: PROBABILITY DENSITY PLOT OF BCR FOR THE PUBLIC LIGHTING EXAMPLE  Pre-Implementation Evaluation of Safety Improvement Programs  122  Appendix E: Probability Density Plot of BCR for the Public Lighting Example  Figure E . l . Example: Probability Density Plot of B C R  Pre-Implementation Evaluation of Safety Improvement Programs  123  

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