Forecasting of Short-Term Freeway Volume with v-Support Vector Machines

Abstract

Predictions for short-term traffic volume provide important inputs for traveler information and traffic management. Traffic volumes in the near future are often estimated based on historical volumes. Because of the complicated nonlinear relationship between historical and future traffic volume data, many previous studies used neural networks to predict short-term traffic volumes. In this research, a v-support vector machine (v-SVM) model, which has the particular strength of overcoming local minima and overfitting common to neural network models, is proposed for short-term traffic volume prediction. The v-SVM model is compared with a widely used multilayer feed-forward neural network (MLFNN) model using four data sets collected from three interstate freeways. Testing results show that for both one-step and two-step forecasting, the v-SVM model outperforms the MLFNN model for all data sets in terms of mean absolute percentage error and root-mean-square error. Key issues in applying both models are also discussed in this article.