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.

University: 

Howard University
Mineta Consortium for Transportation Mobility

Principal Investigator: 

Stephen Arhin, Ph.D.

PI Contact Information: 

Associate Professor, Civil & Environmental Engineering Department
Director, Transportation Research Center
Howard University
2300 Sixth Street NW
Suite 2121
Washington, DC 20059
Tel: 202-806-4798/202-806-6577
Fax: 202-462-9498
saarhin@howard.edu

Funding Source(s) and Amounts Provided (by each agency or organization): 

U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology – $158,493.85

Total Project Cost: 

$158,493.85

Agency ID or Contract Number: 

69A3551747127

Dates: 

March 2019 to September 2020

Project Number: 

1943