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

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    • 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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