Multimodal Transportation Pricing Alliance Design: Large-Scale Optimization for Rapid Gains

Kayla Cummings (), Vikrant Vaze (), Özlem Ergun () and Cynthia Barnhart ()
Additional contact information
Kayla Cummings: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Vikrant Vaze: Thayer School of Engineering at Dartmouth, Hanover, New Hampshire 03755
Özlem Ergun: Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115
Cynthia Barnhart: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Transportation Science, 2025, vol. 59, issue 3, 451-472

Abstract: Transit agencies have the opportunity to outsource certain services to established mobility-on-demand (MOD) providers. Such alliances can improve service quality, coverage, and ridership; reduce public sector costs and vehicular emissions; and integrate the passenger experience. To amplify the effectiveness of such alliances, we develop a fare-setting model that jointly optimizes fares and discounts across a multimodal network. We capture commuters’ travel decisions with a discrete choice model, resulting in a large-scale, mixed-integer, nonconvex optimization problem. To solve this challenging problem, we develop a two-stage decomposition with the pricing decisions in the first stage and a mixed-integer linear optimization of fare discounts and passengers’ travel decisions in the second stage. To solve the decomposition, we develop a new solution approach that combines customized coordinate descent, parsimonious second-stage evaluations, and interpolations using special ordered sets. This approach, enhanced by acceleration techniques based on slanted traversal, randomization, and warm-start, significantly outperforms algorithmic benchmarks. Different alliance priorities result in qualitatively different fare designs: flat fares decrease the total vehicle-miles traveled, whereas geographically informed discounts improve passenger happiness. The model responds appropriately to equity-oriented and passenger-centric priorities, improving system utilization and lowering prices for low-income and long-distance commuters. Our profit allocation mechanism improves the outcomes for both types of operators, thus incentivizing profit-oriented MOD operators to adopt transit priorities.

Keywords: public transit; transportation pricing; alliance design; mixed-integer nonconvex optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/trsc.2023.0009 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:59:y:2025:i:3:p:451-472

Access Statistics for this article

More articles in Transportation Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().