Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis
Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems (ITS). According to previous studies, it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and...
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| Format: | Article |
| Language: | English |
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Maximum Academic Press
2023-02-01
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| Series: | Digital Transportation and Safety |
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| Online Access: | https://www.maxapress.com/article/doi/10.48130/DTS-2023-0002 |
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| author | Qianru Qi Rongjun Cheng Hongxia Ge |
| author_facet | Qianru Qi Rongjun Cheng Hongxia Ge |
| author_sort | Qianru Qi |
| collection | DOAJ |
| description | Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems (ITS). According to previous studies, it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy. In order to provide persuasive passenger flow forecast data for ITS, a deep learning model considering the influencing factors is proposed in this paper. In view of the lack of objective analysis on the selection of influencing factors by predecessors, this paper uses analytic hierarchy processes (AHP) and one-way ANOVA analysis to scientifically select the factor of time characteristics, which classifies and gives weight to the hourly passenger flow through Duncan test. Then, combining the time weight, BILSTM based model considering the hourly travel characteristics factors is proposed. The model performance is verified through the inbound passenger flow of Ningbo rail transit. The proposed model is compared with many current mainstream deep learning algorithms, the effectiveness of the BILSTM model considering influencing factors is validated. Through comparison and analysis with various evaluation indicators and other deep learning models, the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968, and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%. |
| format | Article |
| id | doaj-art-65a0849d64de4ed7b90ed1d260aa0c8c |
| institution | OA Journals |
| issn | 2837-7842 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | Maximum Academic Press |
| record_format | Article |
| series | Digital Transportation and Safety |
| spelling | doaj-art-65a0849d64de4ed7b90ed1d260aa0c8c2025-08-20T02:27:19ZengMaximum Academic PressDigital Transportation and Safety2837-78422023-02-0121122210.48130/DTS-2023-0002DTS-2023-0002Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysisQianru Qi0Rongjun Cheng1Hongxia Ge2Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315211, ChinaAccurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems (ITS). According to previous studies, it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy. In order to provide persuasive passenger flow forecast data for ITS, a deep learning model considering the influencing factors is proposed in this paper. In view of the lack of objective analysis on the selection of influencing factors by predecessors, this paper uses analytic hierarchy processes (AHP) and one-way ANOVA analysis to scientifically select the factor of time characteristics, which classifies and gives weight to the hourly passenger flow through Duncan test. Then, combining the time weight, BILSTM based model considering the hourly travel characteristics factors is proposed. The model performance is verified through the inbound passenger flow of Ningbo rail transit. The proposed model is compared with many current mainstream deep learning algorithms, the effectiveness of the BILSTM model considering influencing factors is validated. Through comparison and analysis with various evaluation indicators and other deep learning models, the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968, and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.https://www.maxapress.com/article/doi/10.48130/DTS-2023-0002rail transit passenger flow predicttime travel characteristicsbilstminfluence factordeep learning model |
| spellingShingle | Qianru Qi Rongjun Cheng Hongxia Ge Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis Digital Transportation and Safety rail transit passenger flow predict time travel characteristics bilstm influence factor deep learning model |
| title | Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis |
| title_full | Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis |
| title_fullStr | Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis |
| title_full_unstemmed | Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis |
| title_short | Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis |
| title_sort | short term inbound rail transit passenger flow prediction based on bilstm model and influence factor analysis |
| topic | rail transit passenger flow predict time travel characteristics bilstm influence factor deep learning model |
| url | https://www.maxapress.com/article/doi/10.48130/DTS-2023-0002 |
| work_keys_str_mv | AT qianruqi shortterminboundrailtransitpassengerflowpredictionbasedonbilstmmodelandinfluencefactoranalysis AT rongjuncheng shortterminboundrailtransitpassengerflowpredictionbasedonbilstmmodelandinfluencefactoranalysis AT hongxiage shortterminboundrailtransitpassengerflowpredictionbasedonbilstmmodelandinfluencefactoranalysis |