mlsanityData to 14 Oct 2026

Online Estimation of Dynamic Origin-Destination Matrices Using Reinforcement Learning with Link-Flow Propagation Guidance

arXiv 2608.30317 ↗First posted 31 Aug 2026cs.LG cs.AIcited by 0

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

Abstract (arXiv)

Dynamic origin-destination (OD) matrix estimation calibrates time-dependent input demand for simulations to reproduce observed link flows. Reinforcement learning is well suited to online estimation because a trained policy estimates demand in a single evaluation, but varying target flows complicate learning from aggregate rewards. We propose reinforcement learning with link-flow propagation guidance (LFPG-RL), combining simulated vehicle propagation records with downstream errors to guide individual OD components. Using 250 weekday link-flow trajectories from a Melbourne arterial network, LFPG-RL achieves a mean test root mean squared error (RMSE) of 7.41 vehicles per 15-minute interval, mean absolute percentage error of 31.24%, and Pearson correlation of 0.987. Comparisons with optimisation, filtering and reinforcement learning without guidance show a 40.1% RMSE reduction over the strongest baseline, gradient descent with propagation guidance. LFPG-RL enables rapid demand updates that keep traffic simulations consistent with observed conditions, supporting the evaluation of traffic management strategies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 32 references that are in the map. First posted on arXiv 31 Aug 2026.

Builds on

5 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment9 Nov

Other references · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2019Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphscited by 10
  4. 2021Improving the Accuracy and Efficiency of Online Calibration for Simulation-based Dynamic Traffic Assignmentcited by 1

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  2. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
  3. 2025Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study
  4. 2025RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning
  5. 2024DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models
  6. 2026Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control
  7. 2022Offline Reinforcement Learning for Road Traffic Control
  8. 2025Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control ↗