Highlights This paper proposes a novel bi-level multimodal network capacity problem. A tri-level multimodal network design model is then formulated. Kriging-surrogate-based optimization algorithms are developed to solve the models. Numerical studies are conducted on the real-scale Nanjing network with 12,000 ODs. Results show the efficiency of the proposed models and approaches.

    Abstract Transportation network capacity enhancement is essential in urban transportation planning. In this paper, a general multimodal network capacity problem (MNCP) is proposed, which can depict 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.


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    Title :

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


    Contributors:
    Wang, Yu (author) / Liu, Haoxiang (author) / Fan, Yinchao (author) / Ding, Jianxun (author) / Long, Jiancheng (author)


    Publication date :

    2022-09-26




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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