Argonne Simulation Framework for Intelligent Transportation Systems
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Argonne Simulation Framework for Intelligent Transportation Systems

Filetype[PDF-1.06 MB]


English
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  • Creators:
  • Corporate Creators:
    Argonne National Laboratory
  • Corporate Contributors:
    United States. Department of Energy
  • Subject/TRT Terms:
  • Resource Type:
    Proceedings
  • Abstract:
    A simulation framework has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS). The simulator is designed to run on parallel computers and distributed (networked) computer systems; however, a version for a stand alone workstation is also available. The ITS simulator includes an Expert Driver Model (EDM) of instrumented "smart" vehicles with in-vehicle navigation units. The EDM is capable of performing optimal route planning (based either on minimum distance or on minimum travel time) and communicating with Traffic Management Centers (TMC). A dynamic road map data base is used for optimum route planning, where the data is updated periodically to reflect any changes in road or weather conditions. The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces that includes human-factors studies to support safety and operational research. Realistic modeling of variations of the posted driving speed are based on human factor studies that take into consideration weather, road conditions, driver's personality and behavior and vehicle type.

    The simulator has been developed on a distributed system of networked UNIX computers, but is designed to run on ANL's IBM SP-X parellel computer system for large scale problems. A novel feature of the developed simulator is that vehicles will be represented by autonomous computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. Vehicle processes interact with each other and with ITS components by exchanging messages. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.

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