Journal article
Plan Your System and Price for Free: Fast Algorithms for Multimodal Transit Operations
Transportation science, Vol.59(1), pp.13-27
01/2025
DOI: 10.1287/trsc.2022.0452
Abstract
We study the problem of jointly pricing and designing a smart transit system, where a transit agency (the platform) controls a fleet of demand-responsive vehicles (cars) and a fixed line service (buses). The platform offers commuters a menu of options (modes) to travel between origin and destination (e.g., direct car trip, a bus ride, or a combination of the two), and commuters make a utility-maximizing choice within this menu, given the price of each mode. The goal of the platform is to determine an optimal set of modes to display to commuters, prices for these modes, and the design of the transit network in order to maximize the social welfare of the system. In this work, we tackle the commuter choice aspect of this problem, traditionally approached via computationally intensive bilevel programming techniques. In particular, we develop a framework that efficiently decouples the pricing and network design problem: Given an efficient (approximation) algorithm for centralized network design without prices, there exists an efficient (approximation) algorithm for decentralized network design with prices and commuter choice. We demonstrate the practicality of our framework via extensive numerical experiments on a real-world data set. We moreover explore the dependence of metrics such as welfare, revenue, and mode usage on (i) transfer costs and (ii) cost of contracting with on-demand service providers and exhibit the welfare gains of a fully integrated mobility system. Funding: This work was supported by the National Science Foundation [Awards CMMI-2308750, CNS-1952011, and CMMI-2144127]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0452 .
Details
- Title: Subtitle
- Plan Your System and Price for Free: Fast Algorithms for Multimodal Transit Operations
- Creators
- Siddhartha Banerjee - Cornell UniversityChamsi Hssaine - University of Southern CaliforniaQi Luo - University of IowaSamitha Samaranayake - Cornell University
- Resource Type
- Journal article
- Publication Details
- Transportation science, Vol.59(1), pp.13-27
- DOI
- 10.1287/trsc.2022.0452
- ISSN
- 0041-1655
- eISSN
- 1526-5447
- Publisher
- INFORMS
- Language
- English
- Electronic publication date
- 10/25/2024
- Date published
- 01/2025
- Academic Unit
- Business Analytics
- Record Identifier
- 9984740859802771
Metrics
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