# A sequential transit network design algorithm with optimal learning under correlated beliefs

arXiv: https://arxiv.org/abs/2305.09452 · HTML: https://mlsanity.com/paper/2305.09452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 82 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 4 of 4)

- [From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions](https://mlsanity.com/paper/1912.03513/) (2019)
- [Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits](https://mlsanity.com/paper/1511.06014/) (2015)
- [A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers](https://mlsanity.com/paper/1901.00760/) (2018)
- [Learning to Route Efficiently with End-to-End Feedback: The Value of Networked Structure](https://mlsanity.com/paper/1810.10637/) (2018)

## Built on by (most cited first, 1 of 1)

- [Learning to Design City-scale Transit Routes](https://mlsanity.com/paper/2512.19767/) (2025)

## Abstract (arXiv)

Mobility service route design requires demand information to operate in a service region. Transit planners and operators can access various data sources including household travel survey data and mobile device location logs. However, when implementing a mobility system with emerging technologies, estimating demand becomes harder because of limited data resulting in uncertainty. This study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning to address the operation under limited data. An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand. At the same time, observed information is archived to update the knowledge that the operator currently uses. Three learning policies are compared within the algorithm: multi-armed bandit, knowledge gradient, and knowledge gradient with correlated beliefs. For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City. Prior knowledge is reproduced from the regional household travel survey data. The results suggest that exploration considering correlations can achieve better performance compared to greedy choices in general. In future work, the problem may incorporate more complexities such as demand elasticity to travel time, no limitations to the number of transfers, and costs for expansion.