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Publications (3)0 Total impact

  • Xu Jin, H. Abdulrab, M. Itmi
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    ABSTRACT: Multi-agent simulation has been looked as an efficient tool for urban dynamic traffic services. However, the main problem is how to build an agent-based model for it. This research presents a multi-agent based demand responsive transport (DRT) services model, which adopts a practical multi-agents planning approach for urban DRT services control that satisfies the main constraints: minimize total slack time, travel time, waiting time, clientpsilas special requests, and using minimum number of vehicle. In this paper, we propose an agent based multi-layer distributed hybrid planning model for the real-time problem which can solve this question. In the proposed method, an agent for each vehicle finds a set of routes by its local search, and selects a route by cooperation with other agents in its planning domain. By computational experiments, we examine the effectiveness of the proposed method.
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on; 07/2008
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    ABSTRACT: This research presents a multi-agent based urban demand responsive transport (DRT) system intelligent control model, which adopts a practical multi-agents planning approach for urban DRT services control that satisfies the main constraints: minimize total slack time, travel time, waiting time, client’s special requests, and using minimum number of vehicle. In this paper, we propose an agent based multi-layer distributed hybrid planning model for the real-time problem which can solve this question. In the proposed method, an agent for each vehicle finds a set of routes by its local search, and selects a route by cooperation with other agents in its planning domain. By computational experiments, we examine the effectiveness of the proposed method.
    Intelligent Vehicles Symposium, 2008 IEEE; 07/2008
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    ABSTRACT: Agent-based urban traffic simulation has been looked as an efficient tool for traffic planning. However, the main problem is how to build an agent-based model for traffic simulation. This research presents an urban traffic information intelligent control model, which adopts a multi-agents coordination approach for urban traffic information control to coordinate the traffic network. In this paper, we propose a new traffic information intelligent control hybrid model based on multi-agent system that performs the basic interface, planning and supports services for managing different types of demand responsive transportation. In this research, we expose the main features and the behaviors exhibited of the multi-agent system. Based on this model, a simplified multi-agent traffic information control system can be developed that is effective for reducing traffic congestion and air pollution.
    Proceedings of the 2007 Summer Computer Simulation Conference, SCSC 2007, San Diego, California, USA, July 16-19, 2007; 01/2007