  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.
