TY - JOUR
T1 - Subway Sudden Passenger Flow Prediction Method Based on Two Factors
T2 - Case Study of the Dongsishitiao Station in Beijing
AU - Xie, Chengguang
AU - Li, Xiaofeng
AU - Chen, Bingfa
AU - Lin, Feng
AU - Lin, Yushun
AU - Huang, Hainan
N1 - Publisher Copyright:
© 2021 Chengguang Xie et al.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1155/2021/5577179
DO - 10.1155/2021/5577179
M3 - Article
AN - SCOPUS:85113800993
SN - 0197-6729
VL - 2021
JO - Journal of Advanced Transportation
JF - Journal of Advanced Transportation
M1 - 5577179
ER -