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Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model
Institution:1. REGIM-Lab.: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax 3038, Tunisia;2. Department of Computer Science and Artificial Intelligence, Research Center on Communication and Information Technology (CITIC-UGR), University of Granada, 18071 Granada, Spain;3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, WA 98071, USA;4. VSB – Technical University of Ostrava, 17, listopadu 15/2172, Ostrava-Poruba, Czech Republic;1. School of Pharmacy, University of Reading, Whiteknights, P.O. Box 224, Reading, RG6 6AD, UK;2. C4X Discovery, Manchester One, 53 Portland Street, Manchester, M1 3LD, UK;1. Laboratory of Mathematical Ecology, A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, 3 Pyzhevsky Lane, Moscow 119017, Russia;2. Faculty of Biology, M.V. Lomonosov Moscow State University, 1 Lenin Hills, Bldg.12, Moscow 119234, Russia;1. Department of Engineering Mechanics, Zhejiang University, Hangzhou 310027, China;2. Institute of Applied Physics and Materials Engineering, Faculty of Science and Technology, University of Macau, Macao SAR, China;1. Research Center of Nonlinear Science and College of Mathematics and Computer Science of Wuhan, Textile University, Wuhan 430200, PR China;2. Complexity Science Center, Institute of Particle Physics, Hua-Zhong (Central China) Normal University, Wuhan 430079, PR China;1. College of Mathematics and Information Science, Leshan Normal University, Leshan, Sichuan 614000, China;2. College of Mathematics and Software Science, Sichuan Normal University, Chengdu, Sichuan 610066, China;3. Audit Office, Leshan Normal University, Leshan, Sichuan 614000, China
Abstract:Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.
Keywords:Traffic management  Adaptive vehicle route guidance  Multiagent systems  Ant colony  Hierarchical fuzzy system  Traffic simulation
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