A Deep Adaptive Traffic Signal Controller With Long-Term Planning Horizon and Spatial-Temporal State Definition Under Dynamic Traffic Fluctuations

IEEE Access

Deqi Chen¹  Shurong Li¹  Xuedong Yan¹  Chong Wei¹  Ying Wang¹  Lu Ma¹ 

[1] MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China;

关键词: Adaptive traffic signal control;    reinforcement learning;    spatial-temporal traffic state representation;    actor-critic;    LSTM;    24-hour dynamic traffic fluctuations;   

DOI  :  10.1109/ACCESS.2020.2974885

来源: DOAJ

【 摘 要 】

This study proposes a new adaptive traffic signal control scheme to effectively manage dynamically fluctuating traffic flows through intersections. A spatial-temporal representation of the traffic state at an intersection has been designed to efficiently identify traffic patterns from complex intersection environments, and a deep neural network (long short-term memory network, LSTM) is used to determine look-ahead signal control decisions based on the estimated long-term feedback from a given traffic state. The actor-critic algorithm, one of the reinforcement learning-based algorithms, is adopted to obtain the essential parameters of the LSTM deep neural network through multiple interactions between a simulated environment and the corresponding adaptive traffic signal controller. A realistic model environment comprising a 24-hour time-varying traffic demand including rush hour and non-rush hour situations served as the basis for traffic generation in the numerical experiments to confirm the effectiveness of the proposed scheme. The results of these experiments show that, compared to an optimized fixed time plan (Synchro), the proposed scheme can reduce waiting times at intersections by an astounding 50% with consequential benefits of reducing fuel consumptions, emissions, queue lengths, and vehicle delays whilst increasing mean speeds.

【 授权许可】

Unknown