Fuzzy-Entropy Neural Network Freeway Incident Duration Modeling with Single and Competing Uncertainties

An approach for predicting incident durations that are susceptible to severe congestion, the occurrence of secondary incidents, and their joint effect is proposed. First, a fuzzy entropy feature selection methodology is applied to determine redundant factors and rank factor importance with respect to their contribution on the predictability of incident duration. Second, neural network models for incident duration prediction with single and competing uncertainties are developed. The results indicate that alignment, collision type, and downstream geometry may be considered as redundant when modeling incident duration. Rainfall intensity is a highly contributing feature, while lane volume, number of blocked lanes, as well as number of vehicles involved in the incident are among the top ranking factors for determining the extent of duration. Finally, the joint consideration of severe congestion and secondary incident occurrence may improve the generalization power of the