Book chapter
Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pp.173-190
Lecture Notes in Computer Science, v. 16595, Springer Nature Switzerland
2026
DOI: 10.1007/978-3-032-27242-3_11
Abstract
Transit Network Design is a well-studied problem in the field of transportation, typically addressed by solving optimization models under fixed demand assumptions. Considering the limitations of these assumptions, this paper proposes a new framework, namely the Two-Level Rider Choice Transit Network Design (2LRC-TND), that leverages machine learning and contextual stochastic optimization (CSO) through constraint programming (CP) to incorporate two layers of demand uncertainties into the network design process. The first level identifies travelers who rely on public transit (core demand), while the second level captures the conditional adoption behavior of those who do not (latent demand), based on the availability and quality of transit services. To capture these two types of uncertainties, 2LRC-TND relies on two travel mode choice models, that use multiple machine learning models. To design a network, 2LRC-TND integrates the resulting choice models into a CSO that is solved using a CP-SAT solver. 2LRC-TND is evaluated through a case study involving over 6,600 travel arcs and more than 38,000 trips in the Atlanta metropolitan area. The computational results demonstrate the effectiveness of the 2LRC-TND in designing transit networks that account for demand uncertainties and contextual information, offering a more realistic alternative to fixed-demand models.
Details
- Title: Subtitle
- Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
- Creators
- Hongzhao Guan - Georgia Institute of TechnologyBeste Basciftci - University of IowaPascal Van Hentenryck - Georgia Institute of Technology
- Contributors
- Tias Guns (Editor)
- Resource Type
- Book chapter
- Publication Details
- Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pp.173-190
- Series
- Lecture Notes in Computer Science; v. 16595
- DOI
- 10.1007/978-3-032-27242-3_11
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Publisher
- Springer Nature Switzerland; Cham
- Language
- English
- Date published
- 2026
- Academic Unit
- Business Analytics
- Record Identifier
- 9985167575102771
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