Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915405358956544 |
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| author | Poudel, Bibek Wang, Xuan Li, Weizi Zhu, Lei Heaslip, Kevin |
| author_facet | Poudel, Bibek Wang, Xuan Li, Weizi Zhu, Lei Heaslip, Kevin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning Poudel, Bibek Wang, Xuan Li, Weizi Zhu, Lei Heaslip, Kevin Machine Learning Multiagent Systems 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. |
| title | Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning |
| topic | Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2504.05018 |