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Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes

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  • Du, Muqing
  • Zhou, Jiankun
  • Chen, Anthony
  • Tan, Heqing

Abstract

Given the increasing prevalence of new urban transport modes such as ridesharing, e-hailing, and combined transport, it is essential to evaluate their effects on the capacity of transportation networks. Hence, this paper develops a novel transportation network capacity model to capture the travel behaviors of inter-multimodal mobility in an urban transportation system that incorporates emerging travel modes. The novel model is formulated as a bi-level programming problem, in which the lower-level model is a combined modal split and traffic assignment (CMSTA) problem based on mathematical programming. The CMSTA problem adopts the cross-nested logit model to account for intermodal travel behavior in the modal split phase and the path-sized logit model to account for route overlap in the traffic assignment phase. Moreover, the logit-based trip distribution model is used to capture the dispatch of the e-hailing traffic flow and the matching of ridesharing drivers with passengers. Besides, we consider flow interactions (e.g., cars and buses sharing the same link) in the road network. We customize a solution framework for solving this novel model that adopts the recently developed fast path-based algorithm with the Barzilai–Borwein stepsize strategy to efficiently solve the CMSTA problem, and derive a sensitivity analysis-based (SAB) algorithm to solve the entire bi-level programming problem. The effectiveness of the novel model is verified in numerical experiments that demonstrate the effects of intermodal transportation, e-hailing, and ridesharing on the capacity of a multimodal transportation network.

Suggested Citation

  • Du, Muqing & Zhou, Jiankun & Chen, Anthony & Tan, Heqing, 2022. "Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:transe:v:168:y:2022:i:c:s1366554522003143
    DOI: 10.1016/j.tre.2022.102937
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