Risk Factors Re-evaluation with Bayesian Network Using SHRP 2 Data

Traffic safety is a complex system influenced by numerous factors, including human behavior, road design, vehicle technology, and environmental conditions. Each of these factors can impact the safety of the transportation system in unique ways, and all factors could interact with each other in complex ways. The goal of this study was to evaluate the joint contribution of multiple risk factors to traffic safety by examining the interactions among different factors. This study considered 24 potential risk factors that reflect different perspectives in the analysis, including driver demographics, driving behavior, environmental conditions, road characteristics, traffic context, vehicle kinematics within a 5-second window of each event, and cell phone ban policies. There were two aspects to this study: first, it explored the relationships between traffic safety risk factors using unsupervised learning models with data from the Second Strategic Highway Research Program Naturalistic Driving Study. Second, with supervised learning models, the study developed a robust data-driven Bayesian network model, evaluated impacting risk factors, quantified their corresponding importance on driving risk, and consequently identified high-risk scenarios.