# Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework

arXiv: https://arxiv.org/abs/2603.00010 · HTML: https://mlsanity.com/paper/2603.00010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [A Survey of Contextual Optimization Methods for Decision Making under Uncertainty](https://mlsanity.com/paper/2306.10374/) (2023)
- [Contextual Stochastic Optimization for Omnichannel Multi-Courier Order Fulfillment Under Delivery Time Uncertainty](https://mlsanity.com/paper/2409.06918/) (2024)
- [Enhanced Route Planning with Calibrated Uncertainty Set](https://mlsanity.com/paper/2503.10088/) (2025)
- [Resiliency of On-Demand Multimodal Transit Systems During a Pandemic](https://mlsanity.com/paper/2101.05395/) (2021)
- [Benders Decomposition for the Design of a Hub and Shuttle Public Transit System](https://mlsanity.com/paper/1601.00367/) (2015)
- [Bilevel Optimization and Heuristic Algorithms for Integrating Latent Demand into the Design of Large-Scale Transit Systems](https://mlsanity.com/paper/2212.03460/) (2022)
- [Path-Based Formulations for the Design of On-demand Multimodal Transit Systems with Adoption Awareness](https://mlsanity.com/paper/2301.07292/) (2023)
- [Contextual Stochastic Optimization for School Desegregation Policymaking](https://mlsanity.com/paper/2408.12572/) (2024)
- [Bilevel Optimization for On-Demand Multimodal Transit Systems](https://mlsanity.com/paper/1912.02557/) (2019)
- [Capturing Travel Mode Adoption in Designing On-demand Multimodal Transit Systems](https://mlsanity.com/paper/2101.01056/) (2021)

## Abstract (arXiv)

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.