Passenger Flow Prediction for Rail Transit Stations Based on an Improved SSA-LSTM Model

Xing Zhao (), Chenxi Li, Xueting Zou, Xiwang Du and Ahmed Ismail
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Xing Zhao: College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China
Chenxi Li: College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China
Xueting Zou: College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China
Xiwang Du: Jsti Group, No. 8, Fuchun Jiangdong Street, Jianye District, Nanjing 210019, China
Ahmed Ismail: College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China

Mathematics, 2024, vol. 12, issue 22, 1-24

Abstract: Accurate and timely passenger flow prediction is important for the successful deployment of rail transit intelligent operation. The Sparrow Search Algorithm (SSA) has been applied to the parameter optimization of a Long-Short-Term Memory (LSTM) model. To solve the inherent weaknesses of SSA, this paper proposes an improved SSA-LSTM model with optimization strategies including Tent Map and Levy Flight to practice the short-term prediction of boarding passenger flow at rail transit stations. Aimed at the passenger flow at four rail transit stations in Nanjing, China, it is found that the day of a week and rainfall are the influencing factors with the highest correlation. On this basis, we apply the proposed SSA-LSTM and four baseline models to realize the short-term prediction, and carry out the prediction experiments with different time granularities. According to the experimental results, the proposed SSA-LSTM model has a more effective performance than the Support Vector Regression (SVR) method, the eXtreme Gradient Boosting (XGBoost) model, the traditional LSTM model, and the improved LSTM model with the Whale Optimization Algorithm (WOA-LSTM) in the passenger flow prediction. In addition, for most stations, the prediction accuracy of the proposed SSA-LSTM model is greater at a larger time granularity, but there are still exceptions.

Keywords: passenger flow prediction; long-short-term memory; improved sparrow search algorithm; machining learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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