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Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems

Hongzhao Guan et al.

Transportation Science2026https://doi.org/10.1287/trsc.2025.0194preprint
AJG 3ABDC A*
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0.50

Abstract

Capturing latent demand has a pivotal role in designing transit services, as omitting these riders can lead to poor quality of service and/or additional costs. This paper explores this topic in the design of transit networks by considering the perspectives of both the transit agencies and riders. The paper presents a generic bilevel optimization model—namely, the Transit Networks Design with Adoptions (TN-DA)—that considers the network design decisions in the leader problem and routing of the riders in the follower problem under the given network design, while allowing a black-box choice function for representing the adoption behavior of latent demand. The paper then identifies structural properties of the optimal solution of the TN-DA problem, which are desirable for transit agencies for capturing adoption behavior of the riders. The paper further provides guideline metrics for the transit agencies based on these desired adoption properties. Because of the computational complexity of this bilevel problem, the paper proposes five efficient heuristic algorithms to solve large-scale instances, which leverage an iterative procedure by solving a simpler version of the TN-DA problem and integrating the evaluation of rider choices. These algorithms either satisfy the desired properties of the optimal solution or provide fast approximations. The paper presents extensive large-scale case studies on two different transit systems by utilizing real data sets: (i) On-demand Multimodal Transit Systems (ODMTS) and (ii) Scooters-Connected Transit Systems (SCTS). The results demonstrate that under time limits, the heuristic algorithms can find high-quality solutions satisfying key adoption properties of the optimal solutions much faster than the exact approaches over various large-scale instances of ODMTS and SCTS. Funding: Financial support from the National Science Foundation [Leap-HI Grant 1854684, NSF Grant 2112533, and Division of Civil, Mechanical and Manufacturing Innovation Grant 2434302] and the Department of Transportation [T-SCORE Grant 69A3552047141] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0194 .

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https://doi.org/https://doi.org/10.1287/trsc.2025.0194

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@article{hongzhao2026,
  title        = {{Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems}},
  author       = {Hongzhao Guan et al.},
  journal      = {Transportation Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/trsc.2025.0194},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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