Large-scale multimodal transportation network models and algorithms-Part II: Network capacity and network design problem

Transportation network capacity enhancement is essential in urban transportation planning. In this paper, a general multimodal network capacity problem (MNCP) is proposed, which can de-pict the transfers, mode overlap, and common line problem and congestion effect of transit. The problem is established as a bi-level model with combined mode choice and traffic assignment as the lower-level programming. Based on the MNCP, a tri-level multimodal network design problem (MNDP-MNCP) is developed to maximize the network capacity. The models are solved with an efficient Kriging-surrogate-based optimization algorithm in real-scale urban networks. Numerical results demonstrate the performances of the proposed framework.

Automated summary

This paper studies the enhancement of transportation network capacity in urban transportation planning. A general multimodal network capacity problem is proposed to depict transfers, mode overlap, common line problem, and congestion effect of transit. A tri-level multimodal network design problem is developed based on this problem to maximize the network capacity. The proposed framework is evaluated in real-scale urban networks using an efficient optimization algorithm.