Traffic network micro-simulation model and control algorithm based on approximate dynamic programming
This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete tim...
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| Published in: | IET intelligent transport systems Vol. 10; no. 3; pp. 186 - 196 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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The Institution of Engineering and Technology
01.04.2016
Institution of Engineering and Technology |
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| ISSN: | 1751-956X, 1751-9578 |
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| Abstract | This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system. |
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| AbstractList | This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system. |
| Author | Yin, Biao Dridi, Mahjoub El Moudni, Abdellah |
| Author_xml | – sequence: 1 givenname: Biao surname: Yin fullname: Yin, Biao email: yihu580124@gmail.com organization: Laboratoire Systèmes et Transports, Université de Technologie de Belfort-Montbéliard, 90000 Belfort, France – sequence: 2 givenname: Mahjoub surname: Dridi fullname: Dridi, Mahjoub organization: Laboratoire Systèmes et Transports, Université de Technologie de Belfort-Montbéliard, 90000 Belfort, France – sequence: 3 givenname: Abdellah surname: El Moudni fullname: El Moudni, Abdellah organization: Laboratoire Systèmes et Transports, Université de Technologie de Belfort-Montbéliard, 90000 Belfort, France |
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| Keywords | approximate dynamic programming vehicle-following model microscopic traffic dynamic model dynamic programming intelligent transportation systems adaptive control ADP method distributed traffic network system cellular automata cellular automata theory learning (artificial intelligence) reinforcement learning method traffic network loading environment traffic network microsimulation model adaptive traffic signal control algorithm |
| Language | English |
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| SubjectTerms | adaptive control adaptive traffic signal control algorithm ADP method Algorithms approximate dynamic programming cellular automata cellular automata theory Computer Science Control theory distributed traffic network system Dynamic programming intelligent transportation systems Intersections learning (artificial intelligence) Mathematics microscopic traffic dynamic model Modeling and Simulation Networks reinforcement learning method Traffic engineering Traffic flow traffic network loading environment traffic network microsimulation model Traffic signals vehicle‐following model |
| Title | Traffic network micro-simulation model and control algorithm based on approximate dynamic programming |
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