Simulation-based analysis of second-best multimodal network capacity

Modeling the capacity of a transportation system ought to be an essential task for assessing the level of service of urban transportation networks. Compared to the maximum traffic throughput under idealized conditions, the total network origin-destination (OD) demand considering practical and political requirements has recently been brought into focus. This problem can be referred to as the second-best network capacity (SNC) analysis and is fully investigated in this paper. To quantify the SNC of multimodal networks, an accurate traffic assignment model incorporating combined modes such as Park-and-Ride is necessary. This paper develops a simulation-based approach that exploits the advantages of the off-the-shelf simulator to reduce model complexity. A bilevel formulation of SNC analysis in a simulation-based manner is proposed to obtain optimal OD demand patterns. Owing to the black-box nature of simulation models, there is a need for computationally efficient algorithms dispensing with gradient approximations. Inspired by machine learning parameter tuning techniques, a Bayesian-type algorithm is employed to solve the model, which can considerably save the number of simulation evaluations. Case studies are conducted on two networks: the Braess and the Sioux Falls networks. The results suggest that the proposed method can accurately and robustly solve the complex SNC problem.