Application of support vector machine in a traffic lights control
Artur Całuch
arturcaluch@gmail.comLublin University of Technology (Poland)
Adam Cieślikowski
Lublin University of Technology (Poland)
Małgorzata Plechawska-Wójcik
Lublin University of Technology (Poland)
Abstract
This article presents the process of adapting support vector machine model’s parameters used for studying the effect of traffic light cycle length parameter’s value on traffic quality. The survey is carried out using data collected during running simulations in author’s traffic simulator. The article shows results of searching for optimum traffic light cycle length parameter’s value.
Keywords:
machine learning; traffic simulator; support vector machineReferences
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[2] El-Tantawy S., Abdulhai B., An Agent-Based Learning Towards Decentralized and Coordinated Traffic Signal Control, 13th International IEEE Conference on Intelligent Transportation Systems, s. 665 - 670, 2010
[3] Gao J., Shen Y., Liu J., Ito M., Shiratori S., Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network, arXiv:1705.02755, 2017
[4] Jin J., Ma X., A group-based traffic signal control with adaptive learning ability, Engineering applications of artificial intelligence, s. 282-293, 2017
[5] Kuyer L., Whiteson S., Bakker B., Vlassis N., Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs, Obrady ECML/PKDD, Antwerp, Belgia, s.656–671,2008
[6] Liu Y., Liu L., Chen W., Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning, IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Październik 2017
[7] Lu S., Liu X., Dai S., Q-Learning for Adaptive Traffic Signal Control Based on Delay Minimization Strategy, 2008 IEEE International Conference on Networking, Sensing and Control, s. 687-691, 2008
[8] Mousav S. S., Schukat M., Howley E., Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning, IET Intelligent Transport System, vol.11 No.7, s. 417-423, Wrzesień 2017
[9] Pierre-Luc G., Desjardins C., Laumonier J., Chaib-draa B., Urban Traffic Control Based on Learning Agents, 2007 IEEE Intelligent Transportation Systems Conference, s. 916-921, 2007
[10] van der Pol E. Oliehoek F. A., Coordinated Deep Reinforcement Learners for Traffic Light Control, Artykuły naukowe Uniwersytetu Amsterdamskiego, 2016
Całuch, A., Cieślikowski, A., & Plechawska-Wójcik, M. (2020). Application of support vector machine in a traffic lights control. Journal of Computer Sciences Institute, 14, 37–42. https://doi.org/10.35784/jcsi.1573
Authors
Adam CieślikowskiLublin University of Technology Poland
Authors
Małgorzata Plechawska-WójcikLublin University of Technology Poland
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