Reinforcement Learning-Based Traffic Signal Control for Signalized Intersections

Download or read book Reinforcement Learning-Based Traffic Signal Control for Signalized Intersections written by Dunhao Zhong and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicles have become an indispensable means of transportation to ensure people's travel and living materials. However, with the increasing number of vehicles, traffic congestion has become severe and caused a lot of social wealth loss. Therefore, improving the efficiency of transport management is one of the focuses of current academic circles. Among the research in transport management, traffic signal control (TSC) is an effective way to alleviate traffic congestion at signalized intersections. Existing works have successfully applied reinforcement learning (RL) techniques to achieve a higher TSC efficiency. However, previous work remains several challenges in RL-based TSC methods. First, existing studies used a single scaled reward to frame multiple objectives. Nevertheless, the single scaled reward has lower scalability to assess the controller's performance on different objectives, resulting in higher volatility on different traffic criteria. Second, adaptive traffic signal control provides dynamic traffic timing plans according to unforeseeable traffic conditions. Such characteristic prohibits applying the existing eco-driving strategies whose strategies are generated based on foreseeable and prefixed traffic timing plans. To address the challenges, in this thesis, we propose to design a new RL-TSC framework along with an eco-driving strategy to improve the TSC's efficiency on multiple objectives and further smooth the traffic flows. Moreover, to achieve effective management of the system-wide traffic flows, current researches tend to focus on the design of collaborative traffic signal control methods. However, the existing collaboration-based methods often ignore the impact of transmission delay for exchanging traffic flow information on the system. Inspired by the state-of-the-art max-pressure control in the traffic signal control area, we propose a new efficient RL-based cooperative TSC scheme by improving the reward and state representation based on the max-pressure control method and developing an agent that can address the data transmission delay issue by decreasing the discrepancy between the real-time and delayed traffic conditions. To evaluate the performance of our proposed work more accurately, in addition to the synthetic scenario, we also conducted an experiment based on the real-world traffic data recorded in the City of Toronto. We demonstrate that our method surpassed the performance of the previous traffic signal control methods through comprehensive experiments.