mlsanityData to 14 Oct 2026

Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning

arXiv 2404.05894 ↗First posted 8 Apr 2024cs.LG cs.AI cs.NEcited by 3

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Planning a network of public transit routes is a challenging optimization problem. Metaheuristic algorithms search through the space of possible transit networks by applying heuristics that randomly alter routes in a network. Existing algorithms almost exclusively use heuristics that modify the network in purely random ways. In this work, we explore whether we can obtain better transit networks using more intelligent heuristics, that modify networks according to a learned preference function instead of at random. We use reinforcement learning to train graph neural nets to act as heuristics. These neural heuristics yield improved results on benchmark synthetic cities with 70 nodes or more, and achieve new state-of-the-art results on the challenging Mumford benchmark. They also improve upon a simulation of the real transit network in the city of Laval, Canada, achieving cost savings of up to 19% over the city's existing transit network.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 29 of its 96 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

29 of 96 references.

References · 29

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Learning to Design City-scale Transit Routescited by 0
  2. 2026AlphaTransit: Learning to Design City-scale Transit Routescited by 0
  3. 2026AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?cited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Applications of deep reinforcement learning to urban transit network design
  2. 2026Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning
  3. 2024Public Transport Network Design for Equality of Accessibility via Message Passing Neural Networks and Reinforcement Learning
  4. 2026Learning to Dial-a-Ride: A Deep Graph Reinforcement Learning Approach to the Electric Dial-a-Ride Problem
  5. 2024Applying Reinforcement Learning to Optimize Traffic Light Cycles
  6. 2025Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework
  7. 2026Traffic-Aware Optimal Taxi Placement Using Graph Neural Network-Based Reinforcement Learning
  8. 2026Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks