A new travel time prediction method for intelligent transportation systems

Abstract

Travel time prediction is an indispensable for numerous intelligent transportation systems (ITS) including advanced traveler information systems. The main purpose of this research is to develop a dynamic travel time prediction model for road networks. In this paper we propose a new method to predict travel times using Naïve Bayesian Classification (NBC) model because Naïve Bayesian Classification has exhibited high accuracy and speed when applied to large databases. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. In addition, we compare the proposed method with such prediction methods as link-based prediction model and time-varying coefficient linear regression model. It is shown from our experiment that NBC predictor can reduce mean absolute relative error significantly rather than the other predictors. We illustrate the practicability of applying NBC in travel time prediction and prove that NBC is suitable and performs well for traffic data analysis. © 2008 Springer-Verlag Berlin Heidelberg.