Traffic forecasting using least squares support vector machines

Abstract

Accurate and timely forecasting of traffic parameters is crucial for effective management of intelligent transportation systems. Travel time index (TTI) is a fundamental measure in transportation. In this article, a non-parametric technique called least squares support vector machines (LS-SVMs) is proposed to forecast TTI. To the best of our knowledge, it is the first time to cooperate the rising computational intelligence technique with state space approach in traffic forecasting. Five other baseline predictors are selected for comparison purposes because of their proved effectiveness. Having good generalisation ability and guaranteeing global minima, LS-SVMs perform better than the others. Experimental results demonstrate that our predictor can significantly reduce mean absolute percentage errors and variance of absolute percentage errors, especially for predicting traffic data with weak regularity. Persuasive comparisons clearly show that it provides a large improvement in stability and robustness, which reveals that it is a promising approach in traffic forecasting and time series analysis.