2,965 research outputs found

    Using common graphics hardware for multi-agent traffic simulation with CUDA

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    Today's graphics processing units (GPU) have tremendous resources when it comes to raw computing power. The simulation of large groups of agents in transport simulation has a huge demand of computation time. Therefore it seems reasonable to try to harvest this computing power for traffic simulation. Unfortunately simulating a network of traffic is inherently connected with random memory access. This is not a domain that the SIMD (single instruction, multiple data) architecture of GPUs is known to work well with. In this paper the authors will try to achieve a speedup by computing multi-agent traffic simulations on the graphics device using NVIDIAs CUDA framework

    Quickest Paths in Simulations of Pedestrians

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    This contribution proposes a method to make agents in a microscopic simulation of pedestrian traffic walk approximately along a path of estimated minimal remaining travel time to their destination. Usually models of pedestrian dynamics are (implicitly) built on the assumption that pedestrians walk along the shortest path. Model elements formulated to make pedestrians locally avoid collisions and intrusion into personal space do not produce motion on quickest paths. Therefore a special model element is needed, if one wants to model and simulate pedestrians for whom travel time matters most (e.g. travelers in a station hall who are late for a train). Here such a model element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte

    Dynamic vehicle routing problems: Three decades and counting

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    Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

    SPRINT: more runners, fewer hurdles

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    Road Network Simulation Using FLAME GPU

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    Demand for high performance road network simulation is increasing due to the need for improved traffic management to cope with the globally increasing number of road vehicles and the poor capacity utilisation of existing infrastructure. This paper demonstrates FLAME GPU as a suitable Agent Based Simulation environment for road network simulations, capable of coping with the increasing demands on road network simulation. Gipps’ car following model is implemented and used to demonstrate the performance of simulation as the problem size is scaled. The performance of message communication techniques has been evaluated to give insight into the impact of runtime generated data structures to improve agent communication performance. A custom visualisation is demonstrated for FLAME GPU simulations and the techniques used are described
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