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Highway Safety Analytics and Modeling
Helps traffic safety researchers make better engineering and policy decisions using the latest tools for collecting and analyzing highway crash data
Dominique Lord (Author), Xiao Qin (Author), Srinivas R. Geedipally (Author)
9780128168189, Elsevier Science
Paperback, published 25 February 2021
500 pages
22.9 x 15.1 x 3.1 cm, 0.88 kg
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.
1. Introduction 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
Subject Areas: Transport law [LNKT]
