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

Yu Wang, Haoxiang Liu, Yinchao Fan, Jianxun Ding and Jiancheng Long

Transportation Research Part E: Logistics and Transportation Review, 2022, vol. 167, issue C

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.

Keywords: Network capacity; Network design problem; Large-scale multimodal network; Tri-level model; Kriging-surrogate-based optimization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.tre.2022.102918

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