# Deep Reinforcement Learning for Dynamic Urban Transportation Problems

arXiv: https://arxiv.org/abs/1806.05310 · HTML: https://mlsanity.com/paper/1806.05310/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes](https://mlsanity.com/paper/2302.10434/) (2023)
- [Lifting the Veil: Unlocking the Power of Depth in Q-learning](https://mlsanity.com/paper/2310.17915/) (2023)
- [Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing](https://mlsanity.com/paper/2509.08329/) (2025)
- [Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates](https://mlsanity.com/paper/2602.21408/) (2026)

## Abstract (arXiv)

We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an optimization problem - such as optimal control problems in intelligent transportation systems and long term urban planning. Often transportation models used to represent dynamics of a transportation system involve large data sets with complex input-output interactions and are difficult to use in the context of optimization. Use of deep learning metamodels can produce a lower dimensional representation of those relations and allow to implement optimization and reinforcement learning algorithms in an efficient manner. In particular, we develop deep learning models for calibrating transportation simulators and for reinforcement learning to solve the problem of optimal scheduling of travelers on the network.