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The prediction of subway passenger flow was an important part of the intelligent transportation system. Currently, most existing prediction models had limited modeling of the spatiotemporal correlation of subway passenger flow and could not take into account the impact of weather factors such as air quality, resulting in low accuracy in predicting subway passenger flow. To address these issues, a short-term prediction model of subway passenger flow based on the attention mechanism of spatio-temporal long short-term memory network(ASTLSTM) was proposed. Firstly, data preprocessing was performed. Then, attention mechanism was combined with graph convolutional network(GCN) and convolutional neural network(CNN) to mine the spatiotemporal correlation in subway data. External features from air quality data were extracted using long short-term memory(LSTM) network. Finally, feature fusion was performed to obtain the final prediction results for subway passenger flow. The experimental results showed that the ASTLSTM model had higher accuracy in short-term prediction of subway passenger flow compared to typical models such as LSTM and Conv LSTM.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2023040
China Classification Code:TP311.13;TP18;U293.13
Citation Information:
[1]TIAN Zhao,CHENG Yujie,ZHANG Qianzhong ,et al.Short-term Forecast of Subway Passenger Flow Based on ASTLSTM[J].Journal of Zhengzhou University(Natural Science Edition),2024,56(05):55-61.DOI:10.13705/j.issn.1671-6841.2023040.
Fund Information:
河南省重点研发与推广专项基金项目(212102310039); 河南省重大公益专项基金项目(201300210300); 综合交通运输大数据应用技术交通运输行业重点实验室开放课题(2022B1201)
2023-02-10
2023
2024-05-27
2025-10-24
2025
3
2023-09-12
2023-09-12
2023-09-12