Adaptive Traffic Signal Control Strategies Research Articles

Managing mixed traffic at signalized intersections: An adaptive signal control and CAV coordination system based on deep reinforcement learning

Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs) at a signalized intersection has become a concern of researchers. However, the performances of most existing control methods are limited, especially when CAV penetration rate is low, since they fail to make a better trade-off between safety and operational efficiency for both CAVs and HVs. To this end, this study proposes a deep reinforcement learning (DRL) powered control system for the mixed traffic at signalized intersections, which aims to optimize operational efficiency of both CAVs and HVs while assuring safety and reducing interference on HVs’ driving habits. The system adopts an adaptive traffic signal control strategy and an efficient CAV control policy with a passing rule proposed as a link in between. The traffic signal control strategy allows traffic light to adaptively adjust its phase and duration based on real-time traffic information, while the CAV control policy permits the CAVs meeting certain safety constraints to form platoons and pass through the intersection in a coordinated manner regardless of traffic signals. As an efficient DRL algorithm, Deep Q-Network (DQN) is adopted to adaptively control the signals and implement CAV coordination. The proposed system is examined on Simulation of Urban Mobility (SUMO), given different CAV penetration rates and traffic conditions. It is found that the proposed system not only outperforms the state-of-the-art control methods on reducing travel time and fuel consumption under low CAV penetration rate, but also enlarges its advantages with the increase of CAV penetration rate. In certain traffic scenarios, the proposed system can even achieve a maximum reduction of travel time by 37.33% and fuel consumption by 15.95%, in comparison to the existing method with the best performance. Besides, to some extent, the comparisons between the performances of CAVs and HVs demonstrate certain benefits of introducing CAVs into the mixed traffic.

Parallel Reinforcement Learning for Traffic Signal Control

Developing Adaptive Traffic Signal Control strategies for efficient urban traffic management is a challenging problem, which is not easily solved. Reinforcement Learning (RL) has been shown to be a promising approach when applied to traffic signal control (TSC) problems. When using RL agents for TSC, difficulties may arise with respect to convergence times and performance. This is especially pronounced on complex intersections with many different phases, due to the increased size of the state action space. Parallel Learning is an emerging technique in RL literature, which allows several learning agents to pool their experiences while learning concurrently on the same problem. Here we present an extension to a leading published work on RL for TSC, which leverages the benefits of Parallel Learning to increase exploration and reduce delay times and queue lengths.