Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks

Stephen Arhin,Babin Manandhar,Hamdiat Baba-Adam

doi:10.28991/cej-2020-03091615

Abstract

This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF

Highlights

Washington, D.C. is ranked second among cities in terms of highest public transit commuters in the United States with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute [1]
Automatic Passenger Counters (APC) installed on buses count the number of passengers alighting and boarding at each bus stop which helps in the computation of the total number of patrons onboard
Neural Network models were developed in this research which can be potentially helpful for transit agencies to improve bus travel time prediction

Summary

Introduction

Washington, D.C. is ranked second among cities in terms of highest public transit commuters in the United States with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute [1]. The Metrobus in D.C. is the fifth largest bus system in the United States. It has over 1,450 buses and services approximately 350 routes across the D.C., Maryland and Virginia area [2]. The accurate prediction of travel time is necessary to enable public transit agencies to provide patrons with efficient transit service and for them to effectively plan their commute or travel in the region. The use of technology, 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. Automatic Passenger Counters (APC) installed on buses count the number of passengers alighting and boarding at each bus stop which helps in the computation of the total number of patrons onboard

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