486 research outputs found

    Testing demand responsive shared transport services via agent-based simulations

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    Demand Responsive Shared Transport DRST services take advantage of Information and Communication Technologies ICT, to provide on demand transport services booking in real time a ride on a shared vehicle. In this paper, an agent-based model ABM is presented to test different the feasibility of different service configurations in a real context. First results show the impact of route choice strategy on the system performance

    Distributed agent-based traffic simulations

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    Modeling and simulation play an important role in transportation networks analysis. With the widespread of personalized real-time information sources, it is relevant for the simulation model to be individual-centered. The agent-based simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we tackle the problem of using high-performance computing for agent-based traffic simulations. To do so, we define two generic agent-based simulation models, representing the existing sequential agent-based traffic simulations. The first model is macroscopic, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is microscopic, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. We define patterns to distribute these simulations in a high-performance environment. The first distributes agents equally between available computation units. The second pattern splits the environment over the different units. We finally propose a diffusive method to dynamically balance the load between units during execution. The results show that agent-based distribution is more efficient with macroscopic simulations, with a speedup of 6 compared to the sequential version, while environmentbased distribution is more efficient with microscopic simulations, with a speedup of 14. Our diffusive load-balancing algorithm improves further the performance of the environment based approach by 150%

    Self-Management in Urban Traffic Control – an Automated Planning Perspective

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    Advanced urban traffic control systems are often based on feed-back algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems are needed that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. This chapter focuses on the design of a generic architecture for auto- nomic urban traffic control, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self- management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering.Preliminary test results of the plan generation phase of the architecture are considered and evaluated

    A Framework for Enhancing the Operational Phase of Traffic Management Plans

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    Road traffic emergencies are dangerous and unexpected situations that require immediate actions by the authorities. These actions involve to attend to the people who have been affected by the emergency and to minimize its consequences. A Traffic Management Plan (TMP) is a set of pre-defined measures and actions designed to produce an effective and efficient use of available resources in order to deal with a specific road incident. The operational phase of a TMP involves the coordination of several independent agencies (road managers, traffic police, firemen, etc.). These agencies must provide the resources required by the TMP in the deployment of the measures and actions. In this paper, a new framework to support the TMP operational phase is presented. This framework models each agency as an intelligent agent and it uses a reverse combinatorial distributed auction as the core component of a negotiation process. The goal of this negotiation process is to obtain a common agreement on the best possible allocation of resources taking into account the role, competencies and interest of the involved agencies. The framework has been implemented in a real scenario with real data. The tests developed have demonstrated that the system is able to manage the resources in terms of the execution time and the quality of the provided solutions

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