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

•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...

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Bibliographic Details
Published in:Transportation research. Part E, Logistics and transportation review Vol. 167; p. 102918
Main Authors: Wang, Yu, Liu, Haoxiang, Fan, Yinchao, Ding, Jianxun, Long, Jiancheng
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2022
Subjects:
Kriging-surrogate-based optimization
Large-scale multimodal network
Network capacity
Network design problem
Tri-level model
Network capacity
Tri-level model
Large-scale multimodal network
Kriging-surrogate-based optimization
Network design problem
ISSN:1366-5545, 1878-5794
Online Access:Get full text
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Summary:•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. 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.
ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2022.102918