A Data-Driven-Based Wavelet Support Vector Approach for Passenger Flow Forecasting of the Metropolitan Hub

With the rapid development of the construction and operation of mass transit hubs, passenger data collection, modeling, and prediction for optimal control have become very important. In this paper, pedestrian facilities are abstracted into connected nodes, and the passenger flow network is formed according to the facility connection relationship determined by the traffic organization; therefore, the state variables of the hub, such as saturation, and the traveling time can be estimated by pedestrian flow information collected by camera monitors and a free Wi-Fi network, including the fast analysis of data features and traffic flow prediction. The method is applied to a real case. The features of pedestrian flows are classified as chaotic and nonchaotic. We use a regression model to predict the nonchaotic situation, and the wavelet support vector machine model is proposed for the chaotic. The results can be used for the control of exits and ramps in the hub.