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Régulation de trafic urbain multimodal : une modélisation multi-agents

Abstract : Since several decades, urban congestion is more and more widespread and deteriorate the quality of life of citizens who live in cities. Several methods are used to reduce urban congestion, notably traffic regulation and promotion of public transportation. Since the 1990's, the usage of tools from artificial intelligence, particularly distributed systems and multi-agent systems, allowed to design new methods for traffic regulation. Indeed, these methods ease to take into account the complexity of traffic-related problems with distribution. Moreover, the improvement of the communication abilities of the vehicles and the coming of autonomous vehicles allow to consider new approaches for regulation.The research work presented in this work is twofold. First we propose a method for traffic regulation at an intersection based on automatic negotiation. Our method is based on an argumentation system describing the state of the traffic and the preferences of each vehicle, relying on reasonning methods for vehicles and infrastructures. In the second part of this thesis, we propose a coordination method for buses for the rest of the traffic. This method allows a bus to coordinate in an anticipatory way with the next intersections on its trajectory, in order to define a common regulation policy allowing the bus to reach its next stop without suffering from potential congestions
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Submitted on : Monday, March 20, 2017 - 4:55:23 PM
Last modification on : Tuesday, December 8, 2020 - 10:20:44 AM


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  • HAL Id : tel-01488151, version 2


Matthis Gaciarz. Régulation de trafic urbain multimodal : une modélisation multi-agents. Intelligence artificielle [cs.AI]. Université de Lyon, 2016. Français. ⟨NNT : 2016LYSE1281⟩. ⟨tel-01488151v2⟩



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