An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow
Subway is an important transportation means for residents, since it is always on schedule. However, some temporal management policies or unpredicted events may change passenger flow and then affect passengers requirement for punctuality. Thus, detecting anomaly event, mining its propagation law, and...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/7191549 |
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| _version_ | 1832564849883742208 |
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| author | Xuehui Wang Yong Zhang Hao Liu Yang Wang Lichun Wang Baocai Yin |
| author_facet | Xuehui Wang Yong Zhang Hao Liu Yang Wang Lichun Wang Baocai Yin |
| author_sort | Xuehui Wang |
| collection | DOAJ |
| description | Subway is an important transportation means for residents, since it is always on schedule. However, some temporal management policies or unpredicted events may change passenger flow and then affect passengers requirement for punctuality. Thus, detecting anomaly event, mining its propagation law, and revealing its potential impact are important and helpful for improving management strategy; e.g., subway emergency management can predict flow change under the condition of knowing specific policy and estimate traffic impact brought by some big events such as vocal concerts and ball games. In this paper, we propose a novel anomalies detection method of subway passenger flow. In this method, an improved robust principal component analysis model is presented to detect anomalies; then ST-DBSCAN algorithm is used to group the station-level anomaly data on space-time dimensions to reveal the propagation law and potential impact of different anomaly events. The real flow data of Beijing subway are used for experiments. The experimental results show that the proposed method is effective for detecting anomalies of subway passenger flow in practices. |
| format | Article |
| id | doaj-art-b91636e2f24c4f9e8ba925ecce388307 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b91636e2f24c4f9e8ba925ecce3883072025-02-03T01:10:04ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/71915497191549An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger FlowXuehui Wang0Yong Zhang1Hao Liu2Yang Wang3Lichun Wang4Baocai Yin5Beijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Transportation Information Center, Beijing, ChinaBeijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaSubway is an important transportation means for residents, since it is always on schedule. However, some temporal management policies or unpredicted events may change passenger flow and then affect passengers requirement for punctuality. Thus, detecting anomaly event, mining its propagation law, and revealing its potential impact are important and helpful for improving management strategy; e.g., subway emergency management can predict flow change under the condition of knowing specific policy and estimate traffic impact brought by some big events such as vocal concerts and ball games. In this paper, we propose a novel anomalies detection method of subway passenger flow. In this method, an improved robust principal component analysis model is presented to detect anomalies; then ST-DBSCAN algorithm is used to group the station-level anomaly data on space-time dimensions to reveal the propagation law and potential impact of different anomaly events. The real flow data of Beijing subway are used for experiments. The experimental results show that the proposed method is effective for detecting anomalies of subway passenger flow in practices.http://dx.doi.org/10.1155/2018/7191549 |
| spellingShingle | Xuehui Wang Yong Zhang Hao Liu Yang Wang Lichun Wang Baocai Yin An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow Journal of Advanced Transportation |
| title | An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow |
| title_full | An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow |
| title_fullStr | An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow |
| title_full_unstemmed | An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow |
| title_short | An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow |
| title_sort | improved robust principal component analysis model for anomalies detection of subway passenger flow |
| url | http://dx.doi.org/10.1155/2018/7191549 |
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