Multimode Traffic Network Equilibrium and System Optimization With Tradable Credit Scheme

In this paper, the equilibrium problem of multimode traffic networks (auto and transit networks) with the tradable credit scheme (TCS) is studied. The mathematical programming model with the Logit function as the mode‐split function is proposed to study the users’ travel mode and route choice behavior in the multimode network under the TCS. Based on the proposed network equilibrium model, Pareto‐improving is further investigated. Research shows that the Pareto‐improving under the TCS is a sufficient condition for reducing the system’s total cost. A Pareto‐improving scheme for specific user categories is developed. For the Pareto‐improving scheme, all users do not become worse and some or all users become better. In addition, a bilevel programming model is also proposed. The upper level minimizes the social total and transit operating costs by optimizing the number of free credits issued and the frequency of transit departure. The lower level is the traffic equilibrium assignment model of the multimode networks to determine traffic flows. The model is solved by combining the genetic algorithm (GA) and the heuristic algorithm based on the Method of Successive Averages (MSAs). The correctness and validity of the models, propositions, and algorithms are verified using two numerical examples.