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Please use this identifier to cite or link to this item: http://hdl.handle.net/10791/322

Title: Vehicle routing in a congested city using a centralized real-time traffic information system and direct experience
Authors: Solter, Alexander
Supervisor(s): Lin, Oscar (Faculty of Science, School of Computing and Information Systems)
Examining Committee: Wen, Dunwei (Faculty of Science, School of Computing and Information Systems)
Leung, Henry (University of Calgary)
Marinova, Rossitza (Concordia University)
Degree: Master of Science, Information Systems (MScIS)
Department: Faculty of Science and Technology
Keywords: Multi-agent systems
Reinforcement learning
Traffic congestion
Multiagent systems
Issue Date: 26-Jun-2020
Abstract: Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we are able to predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This thesis studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. This data is made available to all users, such that they may be able to predict the effects of congestion when building a route. The method is further enhanced by combining the traffic information system data with previous routing experiences. We test our method using a multi-agent simulation, demonstrating that this method produces a lower total route time for all vehicles than when using either a centralized traffic information system or direct experience alone.
Graduation Date: Oct-2020
URI: http://hdl.handle.net/10791/322
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