Development of Freeway Travel Time Forecasting Models by Integrating Different Sources of Traffic Data

Abstract

Artificial neural network (ANN) techniques are applied to build a travel time estimation model. The model exhibits a functional relation between real-time traffic data as the input variables and the actual bus travel time as the output variable. A great quantity of traffic data is collected from intercity buses equipped with global positioning systems, vehicle detectors along the roadway, and the incident database. For model development, data from neighboring sections and time intervals are considered to present the time-space relation of traffic. To account for the various methods of specifying freeway sections, four criteria are employed to partition the freeway into comparable units. These are based on interchanges, similar distances, travel times, and geometry. The southern part of the number one national freeway in Taiwan is selected as the case study. In most sections of the four partitions, the mean absolute percentage errors (MAPEs) of the predicted travel time are under 20%, which indicates a good forecasting effect. For practical use purposes, the path travel time is obtained from the section models with a dynamic forecast concept. Through the validation process, the MAPEs of the travel times at each O-D path (from original point to destination point) are known to be mostly under 20%. These results suggest that this dynamic forecasting approach is practical and reliable for modeling travel time characteristics.