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

摘要

An approach for predicting incident dura-tions that are susceptible to severe congestion, the oc-currence 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 contri-bution 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 de-termining the extent of duration. Finally, the joint con-sideration of severe congestion and secondary incident occurrence may improve the generalization power of the prediction models.