With the rapid development of high-speed railway in China, high-speed railway transport hub (HRTH) has become the high-density distribution center of passenger flow. In order to accurately detect potential safety hazard hidden in passenger flow, it is necessary to forecast the status of passenger flow. In this paper, we proposed a hybrid temporal-spatio forecasting approach to obtain the passenger flow status in HRTH. The approach combined temporal forecasting based on radial basis function neural network (RBF NN) and spatio forecasting based on spatial correlation degree. Computational experiments on actual passenger flow status from a specific bottleneck position and its correlation points in HRTH showed that the proposed approach is effective to forecast the passenger flow status with high precision. 1. Introduction As main influence factors for the safety and sustainability of transportation system, the insecure behaviors and statuses of people are hot issues and difficult problems in traffic safety engineering [1, 2]. With the rapid development of high-speed railway in China, HRTH has become an interface of multitransportation which includes high-speed railway, civil aviation, highway, waterway, urban rail transit, public transport, and private vehicles, and the safety of passenger flow in HRTH has attracted more and more attention. As the vital node of passenger transport net, HRTH is an important collection and distribution center of various transportation modes and massive passenger flow. The distribution quantity of passengers will be sustained to sharply increase with the growth of high-speed railway operation mileage. As the dramatic increase of passengers, high-density passenger flow is generated, which imposed a rigorous challenge to the safety management of HRTH. In order to avoid and solve the problems caused by passenger flow abnormal status, many approaches are proposed in literatures which can be mainly classified into two categories. The first category is the studies on passenger flow modeling and simulating in transport hubs. Gipps and Marksj？ [3] focused on the prediction the alteration passenger flow in the passing environment and proposed a model for the interactions between passengers which is intended for use in a graphical computer simulation. Seyfried et al. [4] analyzed the influence of various approaches for the interaction between the passengers on the resulting velocity-density relation based on a modified social force model. Jia et al. [5] analyzed the characteristics of passenger flow and present parameters relation
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