A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists

Abstract

Cyclists prefer to use infrastructures that separate them from motorized traffic. Using a traffic light to segregate car and bike flows, with the addition of bike-specific green phases, is a lightweight and cheap solution that can be deployed dynamically to assess the opportunity of a heavier infrastructure such as a separate bike lane. To compensate for the increased waiting time induced by these new phases, we introduce in this paper a deep reinforcement learning solution that adapts the green phase cycle of a traffic light to the traffic. Vehicle counter data are used to compare the DRL approach with the actuated traffic light control algorithm over whole days. Results show that DRL achieves better minimization of vehicle waiting time at every hours. Our DRL approach is also robust to moderate changes in bike traffic. The code used for this paper is available at : https://github.com/LucasMagnana/A-DRL-solution-to-help-reduce-the-cost-in-waiting-time-of-securing-a-traffic-light-for-cyclists

Keywords

• Deep reinforcement learning
• Traffic light
• Cyclists
• Waiting time
• 3DQN
• Vehicle counts