Information about bus travel time and its variability is a key indicator of service performance, and it is valued by passengers and operators. Despite the important effect of traffic flow on bus travel time, previous predictive approaches have not fully considered a traffic measure making their predictions unresponsive to the dynamic changes in traffic congestion. In addition, existing methodologies have primarily concerned predicting average travel time given a certain set of input values. However, predicting travel-time variability has not received sufficient attention in previous research. This article proposes an integrated framework to predict bus average travel time and its variability on the basis of a range of input variables including traffic flow data. The framework is applied using GPS-based travel-time data for a bus route in Melbourne, Australia, in conjunction with dynamic traffic flow data collected by the Sydney Coordinated Adaptive Traffic Systems loop detectors and a measure of schedule ...
['Ehsan Mazloumi', 'Geoff Rose', 'Graham Currie', 'Majid Sarvi']
An Integrated Framework to Predict Bus Travel Time and Its Variability Using Traffic Flow Data