Passenger flow prediction of subway stations based on BOA-LSTM model
YANG Xinyu
CHEN Duiyong
Abstract：In response to the issues of single method and low prediction accuracy in subway station passenger flow prediction,a Bayesian optimization algorithm(BOA)-LSTM passenger flow prediction model is proposed based on the global optimization ability of BOA and the long short term memory(LSTM)neural network.Taking the Beiguoshangcheng Station of Shijiazhuang Subway Line 1 as an example,the autoregressive integrated moving average(ARIMA)model,LSTM neural network,and BOA-LSTM model are used to predict the inbound and outbound passenger flows on working days and non-working days in July and August 2021 based on the characteristics of passenger flow at the station.The results show that the BOA-LSTM model has the smallest average absolute percentage error,average absolute error,mean square error,and root mean square error compared to the actual passenger flow,indicating higher prediction accuracy.The BOA-LSTM passenger flow prediction model is applicable from 1 to 2 months and has strong practicality in short-term passenger flow prediction at subway stations.
Keywords：subway stationpassenger flow predictionBOALSTM neural networkaccuracy
Publication Date：2023-12-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：9( 51-59 )