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

• URL: http://arxiv.org/abs/2504.05018v1
• Date: Mon, 07 Apr 2025 12:41:58 GMT
• Title: Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
• Authors: Bibek Poudel, Xuan Wang, Weizi Li, Lei Zhu, Kevin Heaslip,
• Abstract summary: Reinforcement learning holds significant promise for adaptive traffic signal control.<n>We present a deep RL framework for adaptive control of eight traffic signals along a real-world urban corridor.<n>Results demonstrate significant performance improvements over traditional fixed-time signals.
• Score: 11.107470982920262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 accumulated 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.

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