Modeling the Effects of Weather and Traffic on the Risk of Secondary Incidents

Abstract

To extract useful information on variables that are associated with secondary accident likelihood, this article develops neural network models with enhanced explanatory power. Traffic and weather conditions at the occurrence of a primary incident are explicitly considered. Two measures to extract variable significance are introduced: mutual information and partial derivatives. The proposed approach is also compared to other classical statistical approaches of the Logit family. Results suggest that traffic speed, duration of the primary accident, hourly volume, rainfall intensity, and number of vehicles involved in the primary accident are the top five factors associated with secondary accident likelihood. However, changes in traffic speed and volume, number of vehicles involved, blocked lanes, and percentage of trucks and upstream geometry also significantly influence the probability of having a secondary incident. Finally, the incident management implications of the proposed modeling approach are discussed.