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Cyclists prefer to use infrastructure that separates them from motorizedtraffic. Using a traffic light to segregate car and bike flows, with theaddition of bike-specific green phases, is a lightweight and cheap solutionthat can be deployed dynamically to assess the opportunity of a heavierinfrastructure such as a separate bike lane. To compensate for the increasedwaiting time induced by these new phases, we introduce in this paper a deepreinforcement learning solution that adapts the green phase cycle of a trafficlight to the traffic. Vehicle counter data are used to compare the DRL approachwith the actuated traffic light control algorithm over whole days. Results showthat DRL achieves better minimization of vehicle waiting time at almost allhours. Our DRL approach is also robust to moderate changes in bike traffic. Thecode of this paper is available athttps://github.com/LucasMagnana/A-DRL-solution-to-help-reduce-the-cost-in-waiting-time-of-securing-a-traffic-light-for-cyclists.

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