# Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning

arXiv: https://arxiv.org/abs/2504.05018 · HTML: https://mlsanity.com/paper/2504.05018/

## Key facts

- Cited by 2 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.
- First posted on arXiv 7 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Built on by (most cited first, 2 of 2)

- [Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control](https://mlsanity.com/paper/2512.11247/) (2025)
- [DeCoR: Design and Control Co-Optimization for Urban Streets Using Reinforcement Learning](https://mlsanity.com/paper/2605.21311/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) holds significant promise for adaptive traffic signal control. While existing RL-based methods demonstrate effectiveness in reducing vehicular congestion, their predominant focus on vehicle-centric optimization leaves pedestrian mobility needs and safety challenges unaddressed. In this paper, we present a deep RL framework for adaptive control of eight traffic signals along a real-world urban corridor, jointly optimizing both pedestrian and vehicular efficiency. Our single-agent policy is trained using real-world pedestrian and vehicle demand data derived from Wi-Fi logs and video analysis. The results demonstrate significant performance improvements over traditional fixed-time signals, reducing average wait times per pedestrian and per vehicle by up to 67% and 52% respectively, while simultaneously decreasing total wait times for both groups by up to 67% and 53%. Additionally, our results demonstrate generalization capabilities across varying traffic demands, including conditions entirely unseen during training, validating RL's potential for developing transportation systems that serve all road users.