A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists
Cyclists prefer to use infrastructures that separate 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 infras...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Cycling and Micromobility Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950105924000378 |
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| author | Lucas Magnana Hervé Rivano Nicolas Chiabaut |
| author_facet | Lucas Magnana Hervé Rivano Nicolas Chiabaut |
| author_sort | Lucas Magnana |
| collection | DOAJ |
| description | Cyclists prefer to use infrastructures that separate 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 every hours. Our DRL approach is also robust to moderate changes in bike traffic. The code used for 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 |
| format | Article |
| id | doaj-art-e341833f5f354305b8d183d31d4b8d0e |
| institution | DOAJ |
| issn | 2950-1059 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cycling and Micromobility Research |
| spelling | doaj-art-e341833f5f354305b8d183d31d4b8d0e2025-08-20T02:49:01ZengElsevierJournal of Cycling and Micromobility Research2950-10592024-12-01210004610.1016/j.jcmr.2024.100046A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclistsLucas Magnana0Hervé Rivano1Nicolas Chiabaut2CITI, INSA Lyon-Inria, Université de Lyon, Villeurbanne, France; Corresponding author at: 56 bd Niels Bohr, Villeurbanne, France.CITI, INSA Lyon-Inria, Université de Lyon, Villeurbanne, FranceDépartement de la Haute-Savoie, Annecy, FranceCyclists prefer to use infrastructures that separate 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 every hours. Our DRL approach is also robust to moderate changes in bike traffic. The code used for 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-cyclistshttp://www.sciencedirect.com/science/article/pii/S2950105924000378Deep reinforcement learningTraffic lightCyclistsWaiting time3DQNVehicle counts |
| spellingShingle | Lucas Magnana Hervé Rivano Nicolas Chiabaut A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists Journal of Cycling and Micromobility Research Deep reinforcement learning Traffic light Cyclists Waiting time 3DQN Vehicle counts |
| title | A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists |
| title_full | A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists |
| title_fullStr | A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists |
| title_full_unstemmed | A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists |
| title_short | A deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists |
| title_sort | deep reinforcement learning solution to help reduce the cost in waiting time of securing a traffic light for cyclists |
| topic | Deep reinforcement learning Traffic light Cyclists Waiting time 3DQN Vehicle counts |
| url | http://www.sciencedirect.com/science/article/pii/S2950105924000378 |
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