http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
(Professor of civil and environmental engineering)
"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 published 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 solution to problems"--Back cover.
Fundamentals and data collection -- Crash -- frequency modeling -- Crash-severity modeling -- Exploratory analyses of safety data -- Cross-sectional and panel studies in safety -- Before -- after studies in highway safety -- Identification of hazardous sites -- Models for spatial data -- Capacity, mobility, and safety -- Surrogate safety measures -- Data mining and machine learning techniques -- Appendix A: Negative binomial regression models and estimation methods -- Appendix B: Summary of crash-frequency and crash-severity -- Appendix C: Computing codes -- Appendix D: List of exercise datasets.
Dominique Lord, Xiao Qin, Srinivas R. Geedipally.