Highway Safety Analytics and Modeling
- 2nd Edition - November 1, 2025
- Authors: Dominique Lord, Xiao Qin, Srinivas R. Geedipally
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 2 6 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 2 7 - 1
Highway Safety Analytics and Modeling, Second Edition comprehensively covers the key elements for effective transportation engineering and policy decisions based on highway safety… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteHighway Safety Analytics and Modeling, Second Edition comprehensively covers the key elements for effective transportation engineering and policy decisions based on highway safety data analysis in a single reference. It includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating results. It discusses the challenges of working with crash and naturalistic data, identifies problems, and proposes well-researched methods to solve them. It 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.
This thoroughly updated second edition updates the material contained in the book based on the latest advancements in highway safety research as well as feedback from readers. It includes entirely new sections on topics such as digital twins as a source of data, model validation, extreme value models, temporal instability, joint crash frequency and severity modeling, sample size, quasi-induced exposure method, autonomous vehicle safety estimate, and more.
This book serves as a valuable reference for students, researchers, and practitioners alike. It provides more examples and exercises to help in using the book for courses, and it continues to complement the Highway Safety Manual (HSM) published by the American Association of State Highway and Transportation Officials (AAHSTO), thus helping in the training of engineers and practitioners to better understand the concepts and methods outlined in the forthcoming HSM.
This thoroughly updated second edition updates the material contained in the book based on the latest advancements in highway safety research as well as feedback from readers. It includes entirely new sections on topics such as digital twins as a source of data, model validation, extreme value models, temporal instability, joint crash frequency and severity modeling, sample size, quasi-induced exposure method, autonomous vehicle safety estimate, and more.
This book serves as a valuable reference for students, researchers, and practitioners alike. It provides more examples and exercises to help in using the book for courses, and it continues to complement the Highway Safety Manual (HSM) published by the American Association of State Highway and Transportation Officials (AAHSTO), thus helping in the training of engineers and practitioners to better understand the concepts and methods outlined in the forthcoming HSM.
- Offers a better understanding of the nuances associated with safety data (such as low sample mean, small sample size, and repeated measurement)
- Provides examples and exercises not available in research papers as well as learning aids such as online datasets and slides
- Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials
Transportation safety researchers, engineers, analysts, and designers, upper undergraduate and graduate students studying highway safety
Section 1: Introduction Section
1. Theory and background
2. Fundamentals and data collection
3. Crash–frequency modeling
4. Crash-severity modeling
Section 2: Highway safety analyses
5. Exploratory analyses of safety data
6. Application of Models for Safety Analyses
7. Before–afterstudies in highway safety
8. Identification of hazardous sites
9. Models for spatial data
10.Capacity, mobility, and safety
Section 3: Alternative safety analyses
11. Surrogate safetymeasures
12. Data mining and machine learning techniques
Appendices:
A. Negative binomial regression models and estimation methods
B. Computing codes
C. List of exercise datasets
1. Theory and background
2. Fundamentals and data collection
3. Crash–frequency modeling
4. Crash-severity modeling
Section 2: Highway safety analyses
5. Exploratory analyses of safety data
6. Application of Models for Safety Analyses
7. Before–afterstudies in highway safety
8. Identification of hazardous sites
9. Models for spatial data
10.Capacity, mobility, and safety
Section 3: Alternative safety analyses
11. Surrogate safetymeasures
12. Data mining and machine learning techniques
Appendices:
A. Negative binomial regression models and estimation methods
B. Computing codes
C. List of exercise datasets
- No. of pages: 440
- Language: English
- Edition: 2
- Published: November 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443300264
- eBook ISBN: 9780443300271
DL
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, USAXQ
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, USASG
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