A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclists
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Abstract (arXiv)
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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References · 7
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
- 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
- 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 52
- 2017Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learningcited by 33
- 2016Using a Deep Reinforcement Learning Agent for Traffic Signal Controlcited by 26
- 2019An Open-Source Framework for Adaptive Traffic Signal Controlcited by 6
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