Traffic Volume Forecasting Model of Freeway Toll Stations During Holidays – An SVM Model

Abstract

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current day traffic volume is used as the dependent variable and the previous 7-day traffic volume is used as the independent variable for model learning. This paper found that the basic SVM model is not accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model.

Keywords

• traffic volume
• forecasting
• svm
• holiday
• quarterly conversion factor
• freeway toll station