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.
- 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
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.
Part 1: THEORY AND BACKBROUND 2. Fundamentals and Data Collection 3. Crash-Frequency Modeling 4. Crash-Severity Modeling
Part 2: HIGHWAY SAFETY ANALYSES 5. Exploratory Analysis of Safety Data 6. Cross-sectional and Panel Studies in Safety 7. Before-After Studies in Highway Safety 8. Identification of Hazardous Sites 9. Models for Spatial Data 10. Capacity, Mobility, and Safety
Part 3: ALTERNATIVE SAFETY ANALYSES 11. Surrogate Safety Measures 12. Data Mining and Machine Learning Techniques
Appendix A. Negative Binomial Regression Models and Estimation Methods B. Summary of Crash-Frequency and Crash-Severity Models in Highway Safety C. Computing Codes D. List of Exercise Data
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