Highway Safety Analytics and Modeling
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Highway Safety Analytics and Modeling

1st Edition - February 25, 2021

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  • Authors: Dominique Lord, Xiao Qin, Srinivas Geedipally
  • Paperback ISBN: 9780128168189
  • eBook ISBN: 9780128168196

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Description

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.

Key Features

  • Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials
  • Provides examples and case studies for most models and methods
  • Includes learning aids such as online data, examples and solutions to problems

Readership

Transportation safety researchers, graduate students, engineers, analysts, and designers

Table of Contents

  • 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

Product details

  • No. of pages: 500
  • Language: English
  • Copyright: © Elsevier 2021
  • Published: February 25, 2021
  • Imprint: Elsevier
  • Paperback ISBN: 9780128168189
  • eBook ISBN: 9780128168196

About the Authors

Dominique Lord

Dominique Lord is a Professor and A.P. and Florence Wiley Faculty Fellow in the Zachry Department of Civil and Environmental Engineering at Texas A&M University. His highway safety research has led to the development of new and innovative methodologies for analyzing crash data and has been used by researchers across the world in medicine, accounting, mathematics, statistics, biology, and engineering. He’s been published extensively in peer-reviewed journals and presents his work regularly at international conferences. He is the recipient of numerous university, national and international awards.

Affiliations and Expertise

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

Xiao Qin

Xiao Qin is a Professor of Civil and Environmental Engineering, the Director of the University of Wisconsin-Milwaukee's Institute for Physical Infrastructure and Transportation (IPIT), and a licensed professional engineer in Civil Engineering. He chairs the Transportation Research Board (TRB) ACS20(1) Subcommittee on Safety Analytical Methods. He is the Editor of Transportation Research Record and Journal of Transportation Safety & Security, and an Advisory Board Member of Accident Analysis and Prevention. He has authored numerous journal articles, conference papers, and technical reports in highway safety and traffic operations, and a recipient of many best paper awards.

Affiliations and Expertise

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

Srinivas Geedipally

Srinivas R. Geedipally is a Research Engineer in the Center for Transportation Safety at Texas A&M Transportation (TTI) and a registered Professional Engineer in the state of Texas. He received his doctorate from Texas A&M University and has been with TTI since 2005. He has participated in numerous traffic safety research projects with state and federal governments and international sponsors. Dr. Geedipally is an Advisory Board Member of Analytic Methods in Accident Research and has numerous papers published in high-standard international journals and conferences. He has been a key contributor in the development of the Highway Safety Manual, a two-time recipient of the Young Researcher Award, and a Fred Burggraf award winner from the Transportation Research Board.

Affiliations and Expertise

Texas A&M University, Texas A&M Transportation Institute, College Station, TX, USA

Ratings and Reviews

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  • Yonggang W. Fri Nov 26 2021

    Highway Safety Analytics and Modeling

    I saw the information about this book tweeted by researchers at Tongji University and thought it was very close to my own research area, so I checked its catalog. I also like to collect print books, and I like to buy a print journal as a souvenir after I publish research papers in journals published by Elsevier or Springer, etc., so I didn't hesitate to buy this work to study and treasure.

  • Sumon M. Tue May 18 2021

    A single material which covers all aspects for crash data collection and analysis

    This is an excellent book on highway safety (I think a must-read book who are interested to work on traffic crash data). The book has successfully enlisted all the required theory and methods to extract inference by analyzing crash data. For many years there have been a need for a single book/material which will accumulate all information relevant to traffic crash data modelling. This book fills that gap. References and all information mentioned in the book are up to date and authentic. For example, at the end para of the introduction (page 11, second para), critic’s on machine learning for acting as a ‘black box’ to analyze crash data are mentioned. Thus, the book suggested that application of machine learning as a screening tool and then apply conventional statistical model to analyze crash data. This statement is supported by Dr. Spyros Makridakis’ “M4 competition” and a paper published by Fred Mannering et. al in early 2020 ( https://www.sciencedirect.com/science/article/pii/S2213665720300038 ). Anyone having interest and well-understanding on crash data analysis can understand the significance of such statement. From my experience, I did my undergrad thesis on traffic safety and have been working on crash data from 2017. I studied a course on statistics and another course on traffic safety in my Master’s at the University of Kansas. It seems to me the book is the combination of that two courses and is only dedicated to traffic safety. So the book can help students and researchers on traffic safety by reducing the time to extract information after browsing tons of statistical modeling and tons of journal article. As an active student of traffic safety study, I keep this book in my table in front of eye like my ‘Bible’.

  • Soheil Sat May 15 2021

    A most-have book for traffic safety practitioners/researchers

    This is a condensed yet comprehensive highway safety reference work that covers all topics in traffic safety.

  • Maryam Mon Mar 08 2021

    Highly Recommended

    This is an amazing and comprehensive book on analyzing traffic safety. It is a must have book for any one who is working on safety.