Exploring the value of traffic flow data in bus travel time prediction

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, passenger demand, etc, 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 based on 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 impact 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 to 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. While the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared to when temporal variables are used.