Path-Based Formulations for the Design of On-demand Multimodal Transit Systems with Adoption Awareness

Hongzhao Guan (), Beste Basciftci () and Pascal Van Hentenryck ()
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Hongzhao Guan: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30345
Beste Basciftci: Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, Iowa 52242
Pascal Van Hentenryck: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30345

INFORMS Journal on Computing, 2024, vol. 36, issue 6, 1459-1480

Abstract: This paper reconsiders the On-Demand Multimodal Transit Systems (ODMTS) Design with Adoptions problem (ODMTS-DA) to capture the latent demand in on-demand multimodal transit systems. The ODMTS-DA is a bilevel optimization problem, for which Basciftci and Van Hentenryck proposed an exact combinatorial Benders decomposition. Unfortunately, their proposed algorithm only finds high-quality solutions for medium-sized cities and is not practical for large metropolitan areas. The main contribution of this paper is to propose a new path-based optimization model, called P-P ath , to address these computational difficulties. The key idea underlying P-P ath is to enumerate two specific sets of paths which capture the essence of the choice model associated with the adoption behavior of riders. With the help of these path sets, the ODMTS-DA can be formulated as a single-level mixed-integer programming model. In addition, the paper presents preprocessing techniques that can reduce the size of the model significantly. P-P ath is evaluated on two comprehensive case studies: the midsize transit system of the Ann Arbor – Ypsilanti region in Michigan (which was studied by Basciftci and Van Hentenryck) and the large-scale transit system for the city of Atlanta. The experimental results show that P-P ath solves the Michigan ODMTS-DA instances in a few minutes, bringing more than two orders of magnitude improvements compared with the existing approach. For Atlanta, the results show that P-P ath can solve large-scale ODMTS-DA instances (about 17 millions variables and 37 millions constraints) optimally in a few hours or in a few days. These results show the tremendous computational benefits of P-P ath which provides a scalable approach to the design of on-demand multimodal transit systems with latent demand.

Keywords: transit network optimization; bilevel optimization; integer programming; on-demand services; travel mode adoption; latent demand (search for similar items in EconPapers)
Date: 2024
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