Predicting Travel Times of Bus Transit in Washington DC using Artificial Neural Network (ANN)

The accurate prediction of travel time is necessary to enable public transit agencies to provide patrons with efficient transit service and for patron to effectively plan their commute. Transit agencies are continuously evaluating best practices available to improve reliability of their services. The use of technology, particularly in bus transit has been critical for this purpose. This includes the use of Automatic Vehicle Location (AVL) technology, which has been instrumental in the tracking of buses in real-time. AVL technology employs Global Positioning System (GPS) installed onboard of transit buses to track its location and displaying it on a geographical map of the area. This research is aimed at developing ANN model to predict travel times of public bus transit in Washington DC.

    Project

    • Status: Active
    • Funding: $158494
    • Contract Numbers:

      69A3551747127'

    • Sponsor Organizations:

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Managing Organizations:

      Office of the Assistant Secretary for Research and Technology

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

      Stearns, Amy

    • Performing Organizations:

      Mineta Consortium for Transportation Mobility

      San Jose State University
      San Jose, CA  United States  95112
    • Principal Investigators:

      Arhin, Stephen

    • Start Date: 20190501
    • Expected Completion Date: 20200831
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01713205
    • Record Type: Research project
    • Source Agency: Mineta Consortium for Transportation Mobility
    • Contract Numbers: 69A3551747127'
    • Files: UTC, RiP
    • Created Date: Aug 1 2019 4:08PM