Neural Network-Based Prediction Model for Passenger Flow in a Large Passenger Station: An Exploratory Study

Abstract

As the hub and carrier to transfer the passengers, the railway station is an important factor that affects the rail passenger transportation because the normal operation of the station without load redundancy is determined by the moderate passenger flow. It means reasonable and accurate prediction of passengers entering and leaving the station can provide the basis and guarantee for the station security, the resources allocation and the personnel deployment. Since the neural network model is good at processing the common regular data changes through training the network and adjusting the weight value based on a large number of training samples, the neural network model is used in processing the short-term irregular data to predict the passenger flow at the railway station which is susceptible to the constantly changing external factors. In this paper, the neural network is used to predict the passenger flow. First, the key factors affecting the change of the passenger flow are selected and analyzed as the input of the neural network. Second, the learning and the rate updating of variable step size are adopted to estimate the number people entering the station during a certain time interval, which is then weighted with the historical data to derive the prediction of the passenger flow during the next time interval. The simulation results show that the experiment results show that the method proposed in this paper can better track and predict the sudden changes in the passenger flow caused by emergencies. Meanwhile, it can be found that in the process of forecasting abnormal passenger flow, the most critical link is to summarize and summarize the characteristics of railway station passenger flow, clarify the type and time distribution of passenger flow at each station, and analyze the factors that cause abnormal changes in passenger flow.