Book chapter
Bilevel Optimization for On-Demand Multimodal Transit Systems
Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pp.52-68
Lecture Notes in Computer Science, Springer International Publishing
09/19/2020
DOI: 10.1007/978-3-030-58942-4_4
Abstract
This study explores the design of an On-Demand Multimodal Transit System (ODMTS) that includes segmented mode switching models that decide whether potential riders adopt the new ODMTS or stay with their personal vehicles. It is motivated by the desire of transit agencies to design their network by taking into account both existing and latent demand, as quality of service improves. The paper presents a bilevel optimization where the leader problem designs the network and each rider has a follower problem to decide her best route through the ODMTS. The bilevel model is solved by a decomposition algorithm that combines traditional Benders cuts with combinatorial cuts to ensure the consistency of mode choices by the leader and follower problems. The approach is evaluated on a case study using historical data from Ann Arbor, Michigan, and a user choice model based on the income levels of the potential transit riders.
Details
- Title: Subtitle
- Bilevel Optimization for On-Demand Multimodal Transit Systems
- Creators
- Beste BasciftciPascal Van Hentenryck
- Resource Type
- Book chapter
- Publication Details
- Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pp.52-68
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-030-58942-4_4
- ISSN
- 0302-9743
- Publisher
- Springer International Publishing; Cham
- Language
- English
- Date published
- 09/19/2020
- Academic Unit
- Business Analytics
- Record Identifier
- 9984120460402771
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