
Highway Safety Analytics and Modeling
Resources
Description
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
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
Affiliations and Expertise
Xiao Qin
Affiliations and Expertise
Srinivas Geedipally
Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
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.