Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM
Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/2589681 |
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| _version_ | 1832551829951479808 |
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| author | Haijun Li Yongpeng Zhao Changxi Ma Ke Wang Xiaoting Huang Wentao Zhang |
| author_facet | Haijun Li Yongpeng Zhao Changxi Ma Ke Wang Xiaoting Huang Wentao Zhang |
| author_sort | Haijun Li |
| collection | DOAJ |
| description | Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive decomposition is proposed as a solution to the data leaking issue in traditional sequence decomposition. Then, based on this strategy, the passenger flow time series with complex features is decomposed into a relatively single trend and fluctuation component. Finally, the LSTM network is employed to perform short-term predictions on each component separately. The predicted value of each component is accumulated to obtain the original passenger flow’ predicted result. The example shows that, compared with the single LSTM and other hybrid models, the proposed method offers a greater overall prediction accuracy in the experimental days, and the method has specific applicability. |
| format | Article |
| id | doaj-art-b91a7045ee6e4be7b5f9db274f87dcd5 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b91a7045ee6e4be7b5f9db274f87dcd52025-02-03T06:00:26ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/2589681Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTMHaijun Li0Yongpeng Zhao1Changxi Ma2Ke Wang3Xiaoting Huang4Wentao Zhang5School of Traffic and TransportationSchool of Traffic and TransportationSchool of Traffic and TransportationSchool of Traffic and TransportationSchool of Traffic and TransportationOperation Branch of Xi’an Rail Transit Group Co.Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive decomposition is proposed as a solution to the data leaking issue in traditional sequence decomposition. Then, based on this strategy, the passenger flow time series with complex features is decomposed into a relatively single trend and fluctuation component. Finally, the LSTM network is employed to perform short-term predictions on each component separately. The predicted value of each component is accumulated to obtain the original passenger flow’ predicted result. The example shows that, compared with the single LSTM and other hybrid models, the proposed method offers a greater overall prediction accuracy in the experimental days, and the method has specific applicability.http://dx.doi.org/10.1155/2022/2589681 |
| spellingShingle | Haijun Li Yongpeng Zhao Changxi Ma Ke Wang Xiaoting Huang Wentao Zhang Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM Journal of Advanced Transportation |
| title | Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM |
| title_full | Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM |
| title_fullStr | Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM |
| title_full_unstemmed | Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM |
| title_short | Short-Term Passenger Flow Prediction of Urban Rail Transit Based on SDS-SSA-LSTM |
| title_sort | short term passenger flow prediction of urban rail transit based on sds ssa lstm |
| url | http://dx.doi.org/10.1155/2022/2589681 |
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