Exploring the Value of Traffic Flow Data in Bus Travel Time Prediction

Abstract

The accurate prediction of transit travel times has a range of applications to benefit operators and passengers. Transit travel time is affected by several factors such as traffic flow and passenger demand, which have to be considered to make accurate predictions. However, previous studies have not considered real world traffic flow variables in their prediction models. This paper develops artificial neural network (ANN) models to predict bus travel time on the basis of a range of input variables including traffic flow data collected from a bus route in Melbourne, Australia. To overcome the drawback of ANNs in determining the effect of each input variable on the independent variable, the paper adopts a regression analysis to determine the important input variables for prediction. The paper examines the value that traffic flow data would make to the prediction accuracy. To this end, two alternative models are developed and the results are compared with those obtained from the traffic flow data–based models. A historical data–based ANN in which temporal variables are substituted with the traffic flow variable and a timetable-based model that traditionally utilizes scheduled travel times are developed. Although the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared with when temporal variables are used.