Multi-Objective Adaptive Traffic Signal Control Using Fuzzy Control and Q-Learning
A multi-objective adaptive traffic signal control algorithm using fuzzy control and Q-learning was proposed to improve the efficiency, traffic safety, and operational stability of signalized intersections. In this algorithm, the signal cycle length was derived by fuzzy control, then, to minimize del...
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| Vydáno v: | 2024 12th International Conference on Traffic and Logistic Engineering (ICTLE) s. 57 - 62 |
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IEEE
23.08.2024
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| Abstract | A multi-objective adaptive traffic signal control algorithm using fuzzy control and Q-learning was proposed to improve the efficiency, traffic safety, and operational stability of signalized intersections. In this algorithm, the signal cycle length was derived by fuzzy control, then, to minimize delay and conflicts, the green split of each phase was dynamically adjusted through Q-learning. A joint simulation of Python and VISSIM was adopted for traffic operational simulation and evaluation. The simulation results show that the proposed algorithm jointing fuzzy control and Q-learning, and compared with traffic actuated control and fixed timing, the delay, queue length and traffic conflict of the intersection are significantly and comprehensively optimized. In addition, the algorithm reduced the platoon crash risk at the intersection, improving the overall operational stability. |
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| AbstractList | A multi-objective adaptive traffic signal control algorithm using fuzzy control and Q-learning was proposed to improve the efficiency, traffic safety, and operational stability of signalized intersections. In this algorithm, the signal cycle length was derived by fuzzy control, then, to minimize delay and conflicts, the green split of each phase was dynamically adjusted through Q-learning. A joint simulation of Python and VISSIM was adopted for traffic operational simulation and evaluation. The simulation results show that the proposed algorithm jointing fuzzy control and Q-learning, and compared with traffic actuated control and fixed timing, the delay, queue length and traffic conflict of the intersection are significantly and comprehensively optimized. In addition, the algorithm reduced the platoon crash risk at the intersection, improving the overall operational stability. |
| Author | Wan, Chengpeng Lu, Zhaoyou Ding, Naikan Ma, Zufan |
| Author_xml | – sequence: 1 givenname: Naikan surname: Ding fullname: Ding, Naikan email: nkding@whut.edu.cn organization: Wuhan University of Technology,Intelligent Transportation Systems Research Center,Wuhan,China – sequence: 2 givenname: Zufan surname: Ma fullname: Ma, Zufan email: mazufan@163.com organization: Wuhan University of Technology,Intelligent Transportation Systems Research Center,Wuhan,China – sequence: 3 givenname: Zhaoyou surname: Lu fullname: Lu, Zhaoyou email: 18346741020@163.com organization: Harbin Institute of Technology,School of Transportation Science and Engineering,Harbin,China – sequence: 4 givenname: Chengpeng surname: Wan fullname: Wan, Chengpeng email: cpwan@whut.edu.cn organization: Wuhan University of Technology,Intelligent Transportation Systems Research Center,Wuhan,China |
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| PublicationTitle | 2024 12th International Conference on Traffic and Logistic Engineering (ICTLE) |
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| Snippet | A multi-objective adaptive traffic signal control algorithm using fuzzy control and Q-learning was proposed to improve the efficiency, traffic safety, and... |
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| StartPage | 57 |
| SubjectTerms | adaptive traffic signal control Computer crashes Delays Fuzzy control Heuristic algorithms Information entropy Logistics multi-objective optimization Q-learning Safety Simulation Stability analysis traffic simulation |
| Title | Multi-Objective Adaptive Traffic Signal Control Using Fuzzy Control and Q-Learning |
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