  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.
