A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclists
This addresses the need for safer cycling infrastructure by reducing delays for cyclists and other road users, though it is an incremental improvement using existing DRL methods on a specific traffic control problem.
The paper tackles the problem of increased waiting time for cyclists at traffic lights with bike-specific green phases by introducing a deep reinforcement learning (DRL) solution that adapts the green phase cycle to traffic, achieving better minimization of vehicle waiting time at almost all hours compared to an actuated traffic light control algorithm.
Cyclists prefer to use infrastructure that separates 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 almost all hours. Our DRL approach is also robust to moderate changes in bike traffic. The code of 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.