Surrogate Methods of Road Safety

This book provides a comprehensive understanding of accident data collection, analysis, and the use of surrogate safety measures (SSMs) from both vehicular and pedestrian perspectives. It discusses the application of simulation tools for surrogate safety analysis, with an emphasis on risk estimation and the integration of machine learning techniques. This book also explores the use of augmented and virtual reality for road user training and assessment, as well as safety concerns related to automated and connected vehicles. Field case studies offer a realistic view of on-site assessments, safety implications, and measures for safety enhancement. Explores surrogate safety methods in detail, including the identification of surrogate measures and their applications. Examines various SSMs, such as Post-Encroachment Time and Time-to-Collision, and identifies suitable SSMs for mixed traffic conditions, highlighting their strengths and weaknesses. Discusses international codes and standards at appropriate points in each chapter. Covers statistical methods, including Support Vector Machines, binary logit models, and ordered logit models, for estimating severity levels. Includes worked examples and numerical problems for practical understanding. This book is intended for senior undergraduate and graduate students in road safety engineering, transportation, and civil engineering.