A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed

Abstract

The ability to predict traffic variables such as speed, travel time or flow, based on real time data and historic data, collected by various systems in transportation networks, is vital to the intelligent transportation systems (ITS) components such as in-vehicle route guidance systems (RGS), advanced traveler information systems (ATIS), and advanced traffic management systems (ATMS). In the contest of prediction methodologies, different time series, and artificial neural networks (ANN) models have been developed in addition to the historic and real time approach. The present paper proposes the application of a recently developed pattern classification and regression technique called support vector machines (SVM) for the short-term prediction of traffic speed. An ANN model is also developed and a comparison of the performance of both these techniques is carried out, along with real time and historic approach results. Data from the freeways of San Antonio, Texas were used for the analysis.