Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach

TL;DR

This study uses deep reinforcement learning to evaluate how connected and automated vehicles combined with adaptive traffic signals can significantly improve intersection safety by reducing conflicts.

Contribution

It introduces a deep reinforcement learning framework to assess the combined impact of CAVs and adaptive signals on intersection safety, highlighting their potential benefits.

Findings

Significant reduction in rear-end conflicts

Decrease in crossing conflicts with combined CAVs and DQN signals

Long-term safety improvements similar to short-term effects

Abstract

In transportation networks, intersections pose significant risks of collisions due to conflicting movements of vehicles approaching from different directions. To address this issue, various tools can exert influence on traffic safety both directly and indirectly. This study focuses on investigating the impact of adaptive signal control and connected and automated vehicles (CAVs) on intersection safety using a deep reinforcement learning approach. The objective is to assess the individual and combined effects of CAVs and adaptive traffic signal control on traffic safety, considering rear-end and crossing conflicts. The study employs a Deep Q Network (DQN) to regulate traffic signals and driving behaviors of both CAVs and Human Drive Vehicles (HDVs), and uses Time To Collision (TTC) metric to evaluate safety. The findings demonstrate a significant reduction in rear-end and crossing conflicts through the combined implementation of CAVs and DQNs-based traffic signal control. Additionally, the long-term positive effects of CAVs on safety are similar to the short-term effects of combined CAVs and DQNs-based traffic signal control. Overall, the study emphasizes the potential benefits of integrating CAVs and adaptive traffic signal control approaches in order to enhance traffic safety. The findings of this study could provide valuable insights for city officials and transportation authorities in developing effective strategies to improve safety at signalized intersections.