Travel time prediction with support vector regression

Travel time prediction is essential for the development of advanced traveler information systems. In this paper, we apply support vector regression (SVR) for travel-time predictions and compare its results to the 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 support vector regression performs well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can reduce significantly 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 perform well for traffic data analysis.