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.

Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

“…Throughout the years from 1970 to 1980, perceptronlike neural networks that utilized approximation of nonlinear functions operated with an integral of multilayer neurons and nonlinear signal processing. A significant research extension has accomplished in this specific domain, and the results included successful determinants of using neural networks for prediction [15][16].…”

“…By simplifying ( ) = ( 2 ), the argument of ψ is the squared norm, which is more pleasurable to differentiate, and the result shows in Eqn. (16).…”

Short-term Passenger Flow Prediction of railway epidemic Based on SARIMA - LSTM combined model

“…Secondly, the current railway operation management still relies on the experience of dispatchers to judge the change of passenger flow [2] , which results in the lack of ability and preparation of some railway departments to deal with major public health emergencies such as new coronary pneumonia, and it is difficult to adapt to the passenger flow. Fluctuation characteristics [3] . Therefore, it is necessary to study the short-term passenger flow forecasting of railway emergencies to provide theoretical basis for operation management.…”

Investigation of the Effect of Rail Passenger Transport on Air Passenger Transport in Intermodal Transport: Samsun and Trabzon Example

“…When the studies in the literature are examined in the light of all this information, many studies on rail passenger transport (Baumol, 1975;Due, 1997;Jing and Yin, 2020;Kulbovskyi et al, 2019) and air passenger transport (de Oliveira Dias, 2019; Demirhan et al, 2021;Goetz, 1992;Moura et al, 2020;Oleshko and Heiets, 2018;Wolle, 2021;Yang et al, 2018) have been found. Although there are studies using different methods that examine the two modes of transport together (Albalate et al, 2015;Chen, 2017;Clewlow, 2012;Erdogan et al, 2020;Grimme, 2007;Pagliara et al,.…”