A deep reinforcement solution to help reduce the cost in waiting time of securing a traffic light for cyclists. - Inria - Institut national de recherche en sciences et technologies du numérique
Article Dans Une Revue Journal of Cycling and Micromobility Research Année : 2024

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

Nicolas Chiabaut

Résumé

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.
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Dates et versions

hal-04300866 , version 1 (22-11-2023)
hal-04300866 , version 2 (05-04-2024)
hal-04300866 , version 3 (01-10-2024)

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Lucas Magnana, Hervé Rivano, Nicolas Chiabaut. A deep reinforcement solution to help reduce the cost in waiting time of securing a traffic light for cyclists.. Journal of Cycling and Micromobility Research, 2024, ⟨10.1016/j.jcmr.2024.100046⟩. ⟨hal-04300866v3⟩
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