Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning
Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/6505893 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832554586453311488 |
|---|---|
| author | Duowei Li Jianping Wu Ming Xu Ziheng Wang Kezhen Hu |
| author_facet | Duowei Li Jianping Wu Ming Xu Ziheng Wang Kezhen Hu |
| author_sort | Duowei Li |
| collection | DOAJ |
| description | Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex. |
| format | Article |
| id | doaj-art-a991db2f9ff44d29b924859bdb6bc894 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a991db2f9ff44d29b924859bdb6bc8942025-02-03T05:51:13ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/65058936505893Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement LearningDuowei Li0Jianping Wu1Ming Xu2Ziheng Wang3Kezhen Hu4Department of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Civil Engineering, Tsinghua University, Beijing 100084, ChinaInstitute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaChina Academy of Information and Communication Technology, Beijing 100804, ChinaControlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.http://dx.doi.org/10.1155/2020/6505893 |
| spellingShingle | Duowei Li Jianping Wu Ming Xu Ziheng Wang Kezhen Hu Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning Journal of Advanced Transportation |
| title | Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning |
| title_full | Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning |
| title_fullStr | Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning |
| title_full_unstemmed | Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning |
| title_short | Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning |
| title_sort | adaptive traffic signal control model on intersections based on deep reinforcement learning |
| url | http://dx.doi.org/10.1155/2020/6505893 |
| work_keys_str_mv | AT duoweili adaptivetrafficsignalcontrolmodelonintersectionsbasedondeepreinforcementlearning AT jianpingwu adaptivetrafficsignalcontrolmodelonintersectionsbasedondeepreinforcementlearning AT mingxu adaptivetrafficsignalcontrolmodelonintersectionsbasedondeepreinforcementlearning AT zihengwang adaptivetrafficsignalcontrolmodelonintersectionsbasedondeepreinforcementlearning AT kezhenhu adaptivetrafficsignalcontrolmodelonintersectionsbasedondeepreinforcementlearning |