Traffic Safety Analytics: Predictive Modeling and Data Science introduces foundational traffic safety concepts, human behavior factors, and crash dynamics before advancing to sophisticated data analytics and predictive modeling techniques. The text demonstrates how machine learning, statistical modeling, and big data approaches transform traffic safety research and intervention design. Through systematic coverage, the material shows how data science enables crash prediction, risk factor identification, and safety countermeasure evaluation. Each chapter integrates traffic safety theory with computational methods including regression analysis, classification algorithms, spatial analysis, and temporal forecasting. Topics encompass crash data analysis, driver behavior modeling, roadway safety assessment, traffic flow prediction, and intervention effectiveness evaluation. The book addresses contemporary challenges including autonomous vehicle safety, connected vehicle data, and real-time safety monitoring. Written for students, researchers, transportation professionals, and policymakers, the text balances technical rigor with practical applications. This resource serves transportation engineering students, traffic safety analysts, data scientists, transportation planners, and public safety officials. Through interdisciplinary approach combining transportation engineering with data science, the text develops expertise essential for evidence-based traffic safety management and advancing Vision Zero initiatives.
