Search for: All records

On-demand transit is attracting the attention of transportation researchers and transit agencies for its potential to solve the first-mile/last-mile problem. Although on-demand transit has been proved to increase transit accessibility significantly, its impact on transit equity and equality has not been addressed. In this study we examined the potential impact of the On-Demand Multimodal Transit System (ODMTS) in Atlanta (GA), on both transit equity and equality compared with the existing transit system. The results showed that ODMTS could have a positive impact on transit equality by reducing the disparity in transit service between neighborhoods close to and far from the existing transit network; however, it may not improve transit equity. 

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-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 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-Path 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-Path 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-Path which provides a scalable approach to the design of on-demand multimodal transit systems with latent demand. History: Accepted by Andrea Lodi, Design & Analysis of Algorithms—Discrete. Funding: This work was partially supported by National Science Foundation Leap-HI [Grant 1854684] and the Tier 1 University Transportation Center (UTC): Transit - Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) from the U.S. Department of Transportation [69A3552047141]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0014 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0014 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ . 

Emerging on-demand service platforms (OSPs) have recently embraced teamwork as a strategy for stimulating workers’ productivity and mediating temporal supply and demand imbalances. This research investigates the team contest scheme design problem considering work schedules. Introducing teams on OSPs creates a hierarchical single-leader multi-follower game. The leader (platform) establishes rewards and intrateam revenue-sharing rules for distributing workers’ payoffs. Each follower (team) competes with others by coordinating the schedules of its team members to maximize the total expected utility. The concurrence of interteam competition and intrateam coordination causes dual effects, which are captured by an equilibrium analysis of the followers’ game. To align the platform’s interest with workers’ heterogeneous working-time preferences, we propose a profit-maximizing contest scheme consisting of a winner’s reward and time-varying payments. A novel algorithm that combines Bayesian optimization, duality, and a penalty method solves the optimal scheme in the nonconvex equilibrium-constrained problem. Our results indicate that teamwork is a useful strategy with limitations. Under the proposed scheme, team contest always benefits workers. Intrateam coordination helps teams strategically mitigate the negative externalities caused by overcompetition among workers. For the platform, the optimal scheme can direct teams’ schedules toward more profitable market equilibria when workers have inaccurate perceptions of the market. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: This work was supported by the National Science Foundation [Grant FW-HTF-P 2222806]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/serv.2023.0320 . 

This paper studies how to integrate rider mode preferences into the design of on-demand multimodal transit systems (ODMTSs). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, that is, new riders adopting the system, is not captured. This paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and no-good cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper and lower bounds on trip durations for the follower problems, valid inequalities that strengthen the no-good cuts, and approaches to reduce the problem size with problem-specific preprocessing techniques. The proposed method is validated using an extensive computational study on a real data set from the Ann Arbor Area Transportation Authority, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and access to transit systems criteria. The designed ODMTSs feature high adoption rates and significantly shorter trip durations compared with the existing transit system and highlight the benefits of ensuring access for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude. Funding: This research was partly supported by National Science Foundation [Leap HI Proposal NSF-1854684] and the Department of Energy [Research Award 7F-30154].