Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model
[Objective] To effectively address the pressure of inbound/outbound passenger volume on metro lines during peak hours, it is necessary to develop an accurate passenger volume prediction model to understand the spatiotemporal distribution patterns of metro station inbound/outbound volumes and enhance...
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| Format: | Article |
| Language: | zho |
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Urban Mass Transit Magazine Press
2025-07-01
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| Series: | Chengshi guidao jiaotong yanjiu |
| Subjects: | |
| Online Access: | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.20245803.html |
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| _version_ | 1849319428120903680 |
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| author | XU Ling GUAN Jianbo XU Xiwei BAN Yong |
| author_facet | XU Ling GUAN Jianbo XU Xiwei BAN Yong |
| author_sort | XU Ling |
| collection | DOAJ |
| description | [Objective] To effectively address the pressure of inbound/outbound passenger volume on metro lines during peak hours, it is necessary to develop an accurate passenger volume prediction model to understand the spatiotemporal distribution patterns of metro station inbound/outbound volumes and enhance the scientific basis for operational and scheduling decisions of metro lines. [Method] Passenger volume data from Hangzhou Metro is selected, with an introduction to the types of data and the requirements for data preprocessing and analysis. Building upon the LSTNet (long- and short-term time-series network) model, a Bi-LSTM (bidirectional long- and short-term memory) model and the attention mechanism are incorporated to establish an improved LSTNet prediction model. Furthermore, a metro passenger volume prediction method integrating multi-scale temporal sequence features is proposed. Passenger flow data from 6 Hangzhou Metro stations are selected, and predictions are carried out using the LSTM model, the LSTNet model, and the improved LSTNet model respectively. Based on the prediction results, the performance of the improved LSTNet model is evaluated. [Result & Conclusion] Compared with the adopted LSTM and LSTNet models, the improved LSTNet model reduces the mean absolute percentage error (MAPE) of total passenger volume prediction at metro stations by 5.3% and 2.4%, respectively. The improved LSTNet model significantly enhances the accuracy and stability of metro passenger flow prediction. |
| format | Article |
| id | doaj-art-d45377e919d342e2812fe930a43fa14d |
| institution | Kabale University |
| issn | 1007-869X |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Urban Mass Transit Magazine Press |
| record_format | Article |
| series | Chengshi guidao jiaotong yanjiu |
| spelling | doaj-art-d45377e919d342e2812fe930a43fa14d2025-08-20T03:50:26ZzhoUrban Mass Transit Magazine PressChengshi guidao jiaotong yanjiu1007-869X2025-07-0128716316910.16037/j.1007-869x.20245803Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet ModelXU Ling0GUAN Jianbo1XU Xiwei2BAN Yong3Ningbo Rail Transit Group Co, Ltd, 315101, Ningbo, ChinaNingbo Rail Transit Group Co, Ltd, 315101, Ningbo, ChinaNingbo Rail Transit Group Co, Ltd, 315101, Ningbo, ChinaNingbo Rail Transit Group Co, Ltd, 315101, Ningbo, China[Objective] To effectively address the pressure of inbound/outbound passenger volume on metro lines during peak hours, it is necessary to develop an accurate passenger volume prediction model to understand the spatiotemporal distribution patterns of metro station inbound/outbound volumes and enhance the scientific basis for operational and scheduling decisions of metro lines. [Method] Passenger volume data from Hangzhou Metro is selected, with an introduction to the types of data and the requirements for data preprocessing and analysis. Building upon the LSTNet (long- and short-term time-series network) model, a Bi-LSTM (bidirectional long- and short-term memory) model and the attention mechanism are incorporated to establish an improved LSTNet prediction model. Furthermore, a metro passenger volume prediction method integrating multi-scale temporal sequence features is proposed. Passenger flow data from 6 Hangzhou Metro stations are selected, and predictions are carried out using the LSTM model, the LSTNet model, and the improved LSTNet model respectively. Based on the prediction results, the performance of the improved LSTNet model is evaluated. [Result & Conclusion] Compared with the adopted LSTM and LSTNet models, the improved LSTNet model reduces the mean absolute percentage error (MAPE) of total passenger volume prediction at metro stations by 5.3% and 2.4%, respectively. The improved LSTNet model significantly enhances the accuracy and stability of metro passenger flow prediction.https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.20245803.htmlmetrostationpassenger volume predictionimproved lstnet modelbi-lstm neural networkattention mechanism |
| spellingShingle | XU Ling GUAN Jianbo XU Xiwei BAN Yong Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model Chengshi guidao jiaotong yanjiu metro station passenger volume prediction improved lstnet model bi-lstm neural network attention mechanism |
| title | Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model |
| title_full | Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model |
| title_fullStr | Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model |
| title_full_unstemmed | Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model |
| title_short | Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model |
| title_sort | metro station passenger volume prediction algorithm based on improved lstnet model |
| topic | metro station passenger volume prediction improved lstnet model bi-lstm neural network attention mechanism |
| url | https://umt1998.tongji.edu.cn/journal/paper/doi/10.16037/j.1007-869x.20245803.html |
| work_keys_str_mv | AT xuling metrostationpassengervolumepredictionalgorithmbasedonimprovedlstnetmodel AT guanjianbo metrostationpassengervolumepredictionalgorithmbasedonimprovedlstnetmodel AT xuxiwei metrostationpassengervolumepredictionalgorithmbasedonimprovedlstnetmodel AT banyong metrostationpassengervolumepredictionalgorithmbasedonimprovedlstnetmodel |