Travel-time prediction with support vector regression

Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.

I. Introduction

Travel-Time data are the raw elements for a number of performance measures in many transportation analyzes. They can be used in transportation planning, design and operations, and evaluation. Especially, travel-time data are critical pretrip and en route information in advanced traveler information systems. They are very informative to drivers and travelers to make decision or plan schedules. With precise travel-time prediction, a route-guidance system can suggest optimal alternate routes or warn of potential traffic congestion to users; users can then decide the best departure time or estimate their expected arrival time based on predicted travel times.