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Intelligent traffic light control of isolated intersections using machine learning methods
conference contribution
posted on 2013-01-01, 00:00 authored by Sahar Araghi, Abbas KhosraviAbbas Khosravi, Michael JohnstoneMichael Johnstone, Douglas CreightonDouglas CreightonTraffic congestion is one of the major problems in modern cities. This study applies machine learning methods to determine green times in order to minimize in an isolated intersection. Q-learning and neural networks are applied here to set signal light times and minimize total delays. It is assumed that an intersection behaves in a similar fashion to an intelligent agent learning how to set green times in each cycle based on traffic information. Here, a comparison between Q-learning and neural network is presented. In Q-learning, considering continuous green time requires a large state space, making the learning process practically impossible. In contrast to Q-learning methods, the neural network model can easily set the appropriate green time to fit the traffic demand. The performance of the proposed neural network is compared with two traditional alternatives for controlling traffic lights. Simulation results indicate that the application of the proposed method greatly reduces the total delay in the network compared to the alternative methods.
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Event
IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)Pagination
3621 - 3626Publisher
IEEELocation
Manchester, EnglandPlace of publication
Piscataway, N.J.Start date
2013-10-13End date
2013-10-16Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and CyberneticsUsage metrics
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