Highway Safety Analytics and Modeling

di Dominique Lord (Autore)

Xiao Qin (Autore)

Srinivas R. Geedipally (Autore)

Elsevier - Health Sciences Division, 2026

Highway 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.

• Autore: Dominique Lord, Xiao Qin, Srinivas R. Geedipally
• Editore: Elsevier - Health Sciences Division
• Anno: 2026
• Rilegatura: Paperback / softback
• Pagine: 566 p.
• Dimensioni: 229 x 152 mm
• Peso: 450 gr.
• EAN: 9780443300264

Testo in English