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

TL;DR

This paper introduces P-Path, a novel path-based optimization model that significantly improves the computational efficiency of designing on-demand multimodal transit systems with latent demand, enabling solutions for large-scale problems.

Contribution

The paper proposes P-Path, a new path enumeration approach that reformulates ODMTS-DA as a single-level mixed-integer program, overcoming scalability issues of previous methods.

Findings

P-Path solves Michigan transit problems in minutes, outperforming existing methods by over two orders of magnitude.

P-Path can handle large-scale Atlanta transit instances with about 17 million variables and 37 million constraints.

Experimental results demonstrate P-Path's scalability and efficiency in designing on-demand multimodal transit systems.

Abstract

This paper reconsiders the 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 (2022) 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-Path, to address these computational difficulties. The key idea underlying P-Path 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-Path is evaluated on two comprehensive case studies: the mid-size transit system of the Ann Arbor Ypsilanti region in Michigan (which was studied by Basciftci and Van Hentenryck (2022)) and the large-scale transit system for the city of Atlanta. The experimental results show that P-Path solves the Michigan ODMTS-DA instances in a few minutes, bringing more than two orders of magnitude improvements compared to the existing approach. For Atlanta, the results show that P-Path can solve large-scale ODMTS-DA instances (about 17 millions of variables and 37 millions of constraints) optimally in a few hours or in a few days. These results show the tremendous computational benefits of P-Path which provides a scalable approach to the design of on-demand multimodal transit systems with latent demand.