Highway Safety Analytics and Modeling

1st Edition - February 25, 2021
Imprint: Elsevier
Authors: Dominique Lord, Xiao Qin, Srinivas R. Geedipally
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 1 6 8 1 8 - 9
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 6 8 1 9 - 6

Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety da… Read more

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Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes.

Cover image
Title page
Table of Contents
Copyright
Dedication
Preface
Chapter 1. Introduction
1.1. Motivation
1.2. Important features of this textbook
1.3. Organization of textbook
I. Theory and background
Chapter 2. Fundamentals and data collection
2.1. Introduction
2.2. Crash process: drivers, roadways, and vehicles
2.3. Crash process: analytical framework
2.4. Sources of data and data collection procedures
2.5. Assembling data
2.6. 4-stage modeling framework
2.7. Methods for evaluating model performance
2.8. Heuristic methods for model selection
Chapter 3. Crash–frequency modeling
3.1. Introduction
3.2. Basic nomenclature
3.3. Applications of crash-frequency models
3.4. Sources of dispersion
3.5. Basic count models
3.6. Generalized count models for underdispersion
3.7. Finite mixture and multivariate models
3.8. Multi-distribution models
3.9. Models for better capturing unobserved heterogeneity
3.10. Semi- and nonparametric models
3.11. Model selection
Chapter 4. Crash-severity modeling
4.1. Introduction
4.2. Characteristics of crash injury severity data and methodological challenges
4.3. Random utility model
4.4. Modeling crash severity as an unordered discrete outcome
4.5. Modeling crash severity as an ordered discrete outcome
4.6. Model interpretation
II. Highway safety analyses
Chapter 5. Exploratory analyses of safety data
5.1. Introduction
5.2. Quantitative techniques
5.3. Graphical techniques
Chapter 6. Cross-sectional and panel studies in safety
6.1. Introduction
6.2. Types of data
6.3. Data and modeling issues
6.4. Data aggregation
6.5. Application of crash-frequency and crash-severity models
6.6. Other study types
Chapter 7. Before–after studies in highway safety
7.1. Introduction
7.2. Critical issues with before–after studies
7.3. Basic methods
7.4. Bayesian methods
7.5. Adjusting for site selection bias
7.6. Propensity score matching method
7.7. Before–after study using survival analysis
7.8. Sample size calculations
Chapter 8. Identification of hazardous sites
8.1. Introduction
8.2. Observed crash methods
8.3. Predicted crash methods
8.4. Bayesian methods
8.5. Combined criteria
8.6. Geostatistical methods
8.7. Crash concentration location methods
8.8. Proactive methods
8.9. Evaluating site selection methods
Chapter 9. Models for spatial data
9.1. Introduction
9.2. Spatial data and data models
9.3. Measurement of spatial association
9.4. Spatial weights and distance decay models
9.5. Point data analysis
9.6. Spatial regression analysis
Chapter 10. Capacity, mobility, and safety
10.1. Introduction
10.2. Modeling space between vehicles
10.3. Safety as a function of traffic flow
10.4. Characterizing crashes by real-time traffic
10.5. Predicting imminent crash likelihood
10.6. Real-time predictive analysis of crashes
10.7. Using traffic simulation to predict crashes
III. Alternative safety analyses
Chapter 11. Surrogate safety measures
11.1. Introduction
11.2. An historical perspective
11.3. Traffic conflicts technique
11.4. Field survey of traffic conflicts
11.5. Proximal surrogate safety measures
11.6. Theoretical development of safety surrogate measures
11.7. Safety surrogate measures from traffic microsimulation models
11.8. Safety surrogate measures from video and emerging data sources
Chapter 12. Data mining and machine learning techniques
12.1. Introduction
12.2. Association rules
12.3. Clustering analysis
12.4. Decision tree model
12.5. Bayesian networks
12.6. Neural network
12.7. Support vector machines
12.8. Sensitivity analysis
IV. Appendices
Appendix A. Negative binomial regression models and estimation methods
Appendix B. Summary of crash-frequency and crash-severity models in highway safety
Appendix C. Computing codes
Appendix D. List of exercise datasets
Index

Dominique Lord

Dr. Dominique Lord is a professor and holder of the A.P. and Florence Wiley Faculty Fellowship in the Zachry Department of Civil and Environmental Engineering at Texas A&M University. Over the last 27 years, Dr. Lord has conducted numerous research studies in the United States, Canada, and across the world in highway design and safety. Dr. Lord's primary interests are conducting fundamental research on accident analysis methodology, new and innovative statistical methods for modeling motor vehicle collisions, and before-after evaluation techniques. He has extensive experience in data analysis techniques and developed new tools that have been used by engineers and scientists across the world. His other research interests include problems associated with the crash data collection process, safety audits, and traffic flow theory. He has had more than 150 papers published in peer-reviewed journals and more than 140 papers presented at international conferences with a peer-reviewed process.

Affiliations and expertise

Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TD, USA

Xiao Qin

Dr. Xiao Qin is the Lawrence E. Sivak ’71 Professor in the Department of Civil and Environmental Engineering and Director of the Institute for Physical Infrastructure and Transportation (IPIT) at the University of Wisconsin-Milwaukee, USA. Dr. Qin has authored over 150 refereed journal articles, conference proceedings, and technical reports, covering the areas of highway safety, traffic operations, and GIS applications in Transportation. His research has been instrumental in identifying critical safety issues in transportation systems and addressing them using effective methodologies. He has conducted extensive research to support decision making in safety project planning and development for state and local agencies and given safety lectures in several universities. He is an Associate Editor of the Journal of Transportation Safety & Security, Journal of Urban Lifeline, and serves on the editorial board of Accident Analysis and Prevention. He received his Ph.D. in Civil and Environmental Engineering from the University of Connecticut.

Affiliations and expertise

University of Wisconsin‐Milwaukee, Department of Civil and Environmental Engineering, Milwaukee, WI, USA

Srinivas R. Geedipally

Dr. Srinivas Geedipally received his doctorate from Texas A&M University and has been with Texas A&M Transportation Institute since 2005. He is currently the Program Manager for Crash Analytics Group and a Research Engineer in the Center for Transportation Safety. He has more than 75 papers published in high-standard international journals and conferences. Dr. Geedipally is an advisory board member of the Analytic Methods in Accident Research journal and a handling editor of the Transportation Research Record journal. He has participated in numerous traffic safety research projects with state and federal governments and international sponsors. He has been a key contributor in the development of the Highway Safety Manual. He is a registered professional engineer in the state of Texas. Dr. Geedipally is a two-time recipient of the young researcher award, winner of best paper award, and a Fred Burggraf award winner from the TRB.

Affiliations and expertise

Texas A&M Transportation Institute, Texas A&M University, Arlington, TX, USA

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