Short-term passenger flow forecasting method of urban rail transit based on combined model

Weiting Pan; Hongmeng Cui; Ben Zhao; Xi Wang

doi:10.1117/12.2656848

13 January 2023 Short-term passenger flow forecasting method of urban rail transit based on combined model

Weiting Pan, Hongmeng Cui, Ben Zhao, Xi Wang

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125101G (2023) https://doi.org/10.1117/12.2656848
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Passenger demand is the necessary foundation of urban rail transit operation management, and it’s very important to study the accuracy of short-term passenger flow prediction. However, the passenger flow is affected by many factors, so more comprehensive research is needed. This paper is based on multi-source data and the combined model. Firstly, the preliminary correlation between the passenger flow sequence and external factors is analyzed before the prediction, and the required features are extracted by using the convolutional neural network (CNN) and inputted into the prediction process. Also, the model is combined by Bi-directional Long Short-term memory network (Bi-LSTM) and attention mechanism (Attention), which can adapt to the nonlinearity and periodicity of short-term passenger flow prediction. Finally, the real example analysis shows that there is a certain correlation between subway passenger flow and multi-source data, and the CNN-BiLSTM-Attention model can improve the accuracy of passenger flow prediction and reduce errors, which is better than other traditional prediction methods.

Citation Download Citation

Weiting Pan, Hongmeng Cui, Ben Zhao, and Xi Wang "Short-term passenger flow forecasting method of urban rail transit based on combined model", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125101G (13 January 2023); https://doi.org/10.1117/12.2656848

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