Abstract
A sudden increase in passenger flow can primitively lead to continuous congestion of a subway network and thus have a profound impact on the subway system. To prevent the risk caused by sudden overcrowding, the prediction of passenger flow is a daily task of the rail transit management. Most current short-term passenger flow forecasts rely only on inbound passenger flow, which cannot accurately characterize the total impact of sudden passenger flow. To enhance the prediction accuracy, we propose a sudden passenger flow prediction model with two factors, the outbound and inbound passenger flows. The wavelet neural network (WNN) model was used to detect the sudden passenger flow, and subsequently, it is optimized by the genetic algorithm (GA), according to two-factor data characteristics. Sudden passenger flow events from 2014 to 2016 in the Beijing Dongsishitiao Station (DS) were used to train and verify the reliability of the prediction model. The optimized WNN results proved better than the conventional WNN, and the error of models based on two factors was significantly smaller than the models with a single-factor.
Highlights
A sudden passenger flow caused by large-scale activities or special holidays impact railway stations, as passengers gather in the limited station space, affecting regular operations of the station and raising the probability of risk events
On November 6, 2014, at the Huixinxilu Station of Beijing Metro Line 5, a woman was crushed to death by the subway door because of overcrowding on the platform; on April 18, 2015, a trampling occurred at the Huangbeiling Station of Shenzhen Metro Line 5, which resulted in many injuries
To prevent risks caused by the sudden crowding of passengers, the prediction of such an event has become a daily task of the rail transit management department, and the corresponding flow control measures can be implemented based on the predicted passenger flow
Summary
A sudden passenger flow caused by large-scale activities or special holidays impact railway stations, as passengers gather in the limited station space, affecting regular operations of the station and raising the probability of risk events. We propose an optimized sudden passenger flow prediction model to enhance the prediction accuracy and Journal of Advanced Transportation aim to provide scientific forecast data for the passenger flow management of Beijing Metro. Jiang and Adeli [13] used dynamic wavelet analysis to build a short-term traffic flow prediction model based on neural networks and predicted actual freeway traffic flow data. Erefore, this study integrated two factors, the outbound and inbound passenger flows caused by activities into the improved BP neural network model based on wavelet analysis, to enhance the learning ability and accuracy of the prediction model. Data samples of sudden passenger flow events caused by activities at the Beijing Dongsishitiao Station from 2014 to 2016 were used to train and verify the reliability of the proposed model.
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