A Computationally Efficient Link Criticality Ranking with Perception Error and Route Overlapping for Road Transport Networks

Abstract

In real-size congested road networks, ranking links with respect to their criticalities while capturing dependence of link travel costs on user behavior and travel demand is not an easy task. The existing measures either require multiple traffic assignments (i.e., full-scan) or make strong assumptions on users' rationality while using a single traffic assignment. In this study, we propose an improved link criticality index that not only alleviates the computational burden associated with the full-scan network efficiency measures, but also accounts for travelers' perception error and route overlap. The proposed link criticality index is based on the resulting stochastic user equilibrium (SUE) model with a specific discrete choice model. Particularly, we adopt the path-size logit (PSL) route choice model in the SUE model, in which a path-size factor resolves the route overlapping issue by adjusting the choice probabilities for routes with strong correlations. To solve the traffic assignment, we use a faster gradient projection algorithm based on Barzilai-Borwein step size determination scheme (GP-BB). Numerical experiments are conducted to verify and demonstrate the properties of the proposed link criticality measure with the loophole network.