Optimizing Combined Truck Routing and Parking based on Parking Availability Prediction

Truck routing and scheduling problems are important because rest periods required by government regulations have a significant impact on travel and arrival times. Vehicle routes generated without considering these regulations are often practically infeasible. Truck parking areas along possible routes offer places for rest and they become an integral part of routing and scheduling truck driving decisions. The lack of adequate truck parking spaces any time needed however makes the truck routing problem more challenging and difficult to solve. The American Transportation Research Institute (ATRI) Research Advisory Committee selected truck parking as one of the top priority research topics for 2015. California is one of the top states with lack of adequate truck parking capacity to meet increasing demand. The lack of truck parking spaces when needed may have several negative consequences: (1) truck drivers may waste energy and generate pollution searching for parking ; (2) truck drivers may continue to drive when unable to find parking for rest; (3) truck drivers may park at unsafe locations when unable to find parking; and (4) truck drivers may take longer routes in order to guarantee the availability of parking leading to longer travel times, more miles traveled which imply more fuel consumption and more pollution The purpose of this proposal is to develop truck routing and scheduling algorithms that incorporate predicted parking availability along possible routes that minimize cost which may include travel time, environmental costs etc under several constraints that include restrictions on hours of service and other possible government regulations as well as imposed time windows for service. An algorithm for predicting parking availability developed under a different project will be modified to be part of the overall optimization procedure. As a first step the simpler problem where the route is fixed and the parking locations and rest times are optimized will be considered using the parking availability predictor. This part will be applicable where the routes for long haul are pretty much defined or drivers prefer such routes due to familiarity and other constraints. This is a simpler problem to solve relative to what is addressed in the literature however the dynamic nature of parking availability makes the problem new, more challenging and closer to the situation encountered in practice. The second step is to consider the more difficult and challenging problem where the routes and parking are all part of the same optimization problem. Traffic and weather conditions, road incidents etc make route selection also dynamic in addition to parking availability. The project plans to use real data currently available at several internet sites to come up with realistic scenarios to test the developed algorithms. The project focus for evaluation will be California which is one of the states with a truck parking problem however routes and parking places of neighboring states will also be considered for demonstrating the results.


    • English


    • Status: Completed
    • Funding: $100,000
    • Contract Numbers:


    • Sponsor Organizations:

      METRANS Transportation Center

      University of Southern California
      Los Angeles, CA  United States  90089-0626

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Brinkerhoff, Cort

    • Principal Investigators:

      Ioannou, Petros

    • Start Date: 20170930
    • Expected Completion Date: 20180930
    • Actual Completion Date: 20181031

    Subject/Index Terms

    Filing Info

    • Accession Number: 01643003
    • Record Type: Research project
    • Source Agency: National Center for Metropolitan Transportation Research
    • Contract Numbers: 17-01
    • Files: UTC, RiP, ATRI
    • Created Date: Aug 1 2017 2:12PM