A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system

In order to effectively manage the use of existing infrastructures and prevent the emergency caused by the large gathered crowd, the short-term passenger flow forecasting technology becomes more and more significant in the field of intelligent transportation system. However, there are few studies discussing how to predict different kinds of passenger flows in the subway system. In this paper, a novel hybrid model Wavelet-SVM is proposed, and it combines the complementary advantages of Wavelet and SVM models, and meanwhile overcomes their shortcomings respectively. The Wavelet-SVM forecasting approach consists of three important stages. The first stage decomposes the passenger flow data into different high frequency and low frequency series by wavelet. During the prediction stage, the SVM method is applied to learn and predict the corresponding high frequency and low frequency series. In the last stage, the diverse predicted sequences are reconstructed by wavelet. The experimental results show that the approach not only has the best forecasting performance compared with the state-of-the-art techniques but also appears to be the most promising and robust based on the historical passenger flow data in Beijing subway system and several standard evaluation measures.