Transit Bus Travel Time Prediction using AVL Data

Transit Bus Travel Time Prediction using AVL Data
Authors : Dr. Stephen Arhin, Regis Z. Stinson
Publication Date: 02-12-2016


Author(s):  Dr. Stephen Arhin, Regis Z. Stinson

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 5 - Issue. 12 , December - 2016

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV5IS120019


The prediction of transit bus travel times along corridors is critical in the planning and operation of buses, especially in urban areas. Bus patrons tend to have more confidence in a transit system if travel times can be adequately predicted, within a certain margin of error. Washington DC’s the transit agency, the Washington Metropolitan Authority (WMATA), recently equipped some of its fleet with Automated Vehicle Location (AVL) systems and Passenger Count Systems (PCS) to obtain data as buses travel along corridors. In this study, data from the AVL/PCS system on transit buses were used to develop a travel time model to predict how long buses travel along selected corridors in Washington DC. AVL and PCS data for a period of one-month during the summer of 2016 for eight arterial bus routes used was in this study. The advertised travel times for the selected corridors from the selected origins and destinations were also obtained. Based on the literature review, a number of variables were selected as input for the prediction of bus travel times. From the data analysis, it was determined that the number of passengers alighting, passengers boarding, number of access approaches and signalized intersections, significantly predicted transit bus travel time at 95% confidence interval. In addition, the bus travel time prediction model was determined to be statistically significant with validation tests indication model adequacy at 5% level of significance.


Number of Citations for this article:  Data not Available


Key Word(s):    


Number of Downloads:     388

7   Paper(s) Found related to your topic:    

Call for Papers - May - 2017



                 Call for Thesis - 2017 

     Publish your Ph.D/Master's Thesis Online

              Publish Ph.D Master Thesis Online as Book