Coordination of IVI and transit signal priority on transit evacuations.
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Coordination of IVI and transit signal priority on transit evacuations.

Filetype[PDF-3.16 MB]


English
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  • Creators:
  • Corporate Creators:
    National Center for Intermodal Transportation
  • Subject/TRT Terms:
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    United States
  • Corporate Publisher:
    National Center for Intermodal Transportation
  • Abstract:
    During an emergency evacuation, execution time is always critical to the evacuees who are

    transit dependent. Transit Signal Priority (TSP) can speed up the transit services by prioritizing

    the approaching bus at a signalized intersection. With the emergence of IntelliDrive (formerly

    known as IVI), which is a wireless communication technology used to transfer data among

    vehicles and infrastructures, a TSP system can obtain more accurate traffic data and react to

    the approaching bus in a wider area. This report proposes an adaptive TSP system to facilitate

    the transit-based emergency evacuation on the basis of the U.S. Department of Transportation

    (DOT)’s IntelliDrive initiative.

    The objective of this project is to study the TSP and IntelliDrive coordination and to evaluate

    the impacts of the proposed TSP strategies on the transit-based emergency evacuation. The

    emergency evacuation model consists of two optimization models: a TSP optimization model

    and a bus routing optimization model. The TSP optimization model includes bus travel time

    prediction and traffic signal optimization. The bus travel time prediction is used to estimate the

    bus arrival time at the intersection. The traffic signal optimization considers both the bus delay

    and the network-wide vehicle delay. It determines when and which TSP strategy will be applied.

    The principal inputs for the TSP optimization model are: bus speed, position, busload, queue

    length, and traffic signal status. The bus routing optimization model is proposed to optimize the

    transit vehicles allocation and routing. The Dijkstra Algorithm has been modified to find out the

    shortest paths among the pickup points and the shelters in the network. Additionally, a hybrid

    intelligence algorithm consisting of a Genetic Algorithm and a Hill Climbing Algorithm, which

    was developed under the sponsorship of a previous project, has been applied to solve the

    transit vehicle routing and allocation problem.

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