SEVERITY OF TRAFFIC ACCIDENTS ON HORIZONTAL CURVES AND THEIR DETERMINANTS: A BAYESIAN NETWORK AND INFORMATION THEORY MODEL

Statistical analysis reveals that the unique environment of horizontal curve roads significantly contributes to the severity and fatality rates of traffic accidents. This study leveraged accident data from the Florida Department of Transportation (FDOT) to explore the severity of traffic accidents on horizontal curves and its influencing factors. Bayesian network was combined with information theory for the analysis of the severity and determinants of accidents on horizontal curves from the perspectives of network topology, the strength of the relationship between influencing factors, and the pathways of influencing factors. Results show that, (1) Traffic accident causation is complex, with a hierarchical network structure of factors rather than direct impacts from individual variables. (2) The strength of the relationship and dynamic change correlation between each variable are obtained. Results demonstrate that accidents are rarely caused by a single factor, and the severity of traffic accidents can be prevented and reduced by controlling variables states.(3) The analysis of the influence pathways of uncontrollable variables, like weather, revealed specific state combinations (e.g., Fog+Slippery, Rain+Slippery, Fog+Wet) that significantly escalate accident severity. This study presents an advanced model for predicting and diagnosing traffic accidents on horizontal curves, offering insights into the causative factors and their quantitative relationships and influence pathways. Keywords:Traffic safety, Horizontal curve, Bayesian network, Information theory, Accident prediction and diagnosis