Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
This addresses the need for more inclusive traffic management that considers pedestrian safety and efficiency, representing an incremental improvement over vehicle-centric methods.
The paper tackled the problem of optimizing traffic signals for both pedestrians and vehicles in urban environments using reinforcement learning, achieving reductions in average wait times of up to 67% for pedestrians and 52% for vehicles compared to traditional fixed-time signals.
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.