A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination
Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/759097 |
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| author | Xiaoke Zhou Fei Zhu Quan Liu Yuchen Fu Wei Huang |
| author_facet | Xiaoke Zhou Fei Zhu Quan Liu Yuchen Fu Wei Huang |
| author_sort | Xiaoke Zhou |
| collection | DOAJ |
| description | Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. |
| format | Article |
| id | doaj-art-2feafff6c93a42d78f2dccff2f2fafdf |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2feafff6c93a42d78f2dccff2f2fafdf2025-02-03T01:01:01ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/759097759097A Sarsa(λ)-Based Control Model for Real-Time Traffic Light CoordinationXiaoke Zhou0Fei Zhu1Quan Liu2Yuchen Fu3Wei Huang4School of Computer Science and Technology, Soochow University, Shizi Street No. 1, Suzhou, Jiangsu 215006, ChinaSchool of Computer Science and Technology, Soochow University, Shizi Street No. 1, Suzhou, Jiangsu 215006, ChinaSchool of Computer Science and Technology, Soochow University, Shizi Street No. 1, Suzhou, Jiangsu 215006, ChinaSchool of Computer Science and Technology, Soochow University, Shizi Street No. 1, Suzhou, Jiangsu 215006, ChinaSchool of Computer Science and Technology, Soochow University, Shizi Street No. 1, Suzhou, Jiangsu 215006, ChinaTraffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.http://dx.doi.org/10.1155/2014/759097 |
| spellingShingle | Xiaoke Zhou Fei Zhu Quan Liu Yuchen Fu Wei Huang A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination The Scientific World Journal |
| title | A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination |
| title_full | A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination |
| title_fullStr | A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination |
| title_full_unstemmed | A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination |
| title_short | A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination |
| title_sort | sarsa λ based control model for real time traffic light coordination |
| url | http://dx.doi.org/10.1155/2014/759097 |
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