Visual Analytic Method for Metro Anomaly Detection and Diffusion
With the rapid development of urbanization in recent years, thousands of people have flooded into the city, which has brought tremendous pressure on the supervision and operation of relevant traffic management departments. In particular, the unexpected events in the urban rail transit system have ca...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/9082370 |
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| _version_ | 1832546964471808000 |
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| author | Yunhui Li Yong Zhang He Shi Yun Wei Baocai Yin |
| author_facet | Yunhui Li Yong Zhang He Shi Yun Wei Baocai Yin |
| author_sort | Yunhui Li |
| collection | DOAJ |
| description | With the rapid development of urbanization in recent years, thousands of people have flooded into the city, which has brought tremendous pressure on the supervision and operation of relevant traffic management departments. In particular, the unexpected events in the urban rail transit system have caused great troubles for city managers. Aiming at the problem of abnormal passenger flow in the metro, this paper proposes a visual analytic method to support the abnormal passenger flow detection, verification, and diffusion analysis in the metro system. The method provides an intuitive visual metaphor and allows users to perform simple interactive operations to verify abnormal passenger flow. In addition, the method reveals the diffusion law of abnormal passenger flow in time and space in a two-dimensional diffusion view. The Beijing Rail Transit AFC data are used to validate the developed system, and two reliable analysis cases are presented. The system can help users quickly grasp the abnormal propagation rules and help them to develop different scheduling strategies for different anomalous propagation paths. |
| format | Article |
| id | doaj-art-a33743bf99be4356b99642ede0f8f62f |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a33743bf99be4356b99642ede0f8f62f2025-02-03T06:46:33ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/90823709082370Visual Analytic Method for Metro Anomaly Detection and DiffusionYunhui Li0Yong Zhang1He Shi2Yun Wei3Baocai Yin4Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Urban Construction Design & Development Group Co. Limited, Beijing 100124, ChinaBeijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaWith the rapid development of urbanization in recent years, thousands of people have flooded into the city, which has brought tremendous pressure on the supervision and operation of relevant traffic management departments. In particular, the unexpected events in the urban rail transit system have caused great troubles for city managers. Aiming at the problem of abnormal passenger flow in the metro, this paper proposes a visual analytic method to support the abnormal passenger flow detection, verification, and diffusion analysis in the metro system. The method provides an intuitive visual metaphor and allows users to perform simple interactive operations to verify abnormal passenger flow. In addition, the method reveals the diffusion law of abnormal passenger flow in time and space in a two-dimensional diffusion view. The Beijing Rail Transit AFC data are used to validate the developed system, and two reliable analysis cases are presented. The system can help users quickly grasp the abnormal propagation rules and help them to develop different scheduling strategies for different anomalous propagation paths.http://dx.doi.org/10.1155/2020/9082370 |
| spellingShingle | Yunhui Li Yong Zhang He Shi Yun Wei Baocai Yin Visual Analytic Method for Metro Anomaly Detection and Diffusion Journal of Advanced Transportation |
| title | Visual Analytic Method for Metro Anomaly Detection and Diffusion |
| title_full | Visual Analytic Method for Metro Anomaly Detection and Diffusion |
| title_fullStr | Visual Analytic Method for Metro Anomaly Detection and Diffusion |
| title_full_unstemmed | Visual Analytic Method for Metro Anomaly Detection and Diffusion |
| title_short | Visual Analytic Method for Metro Anomaly Detection and Diffusion |
| title_sort | visual analytic method for metro anomaly detection and diffusion |
| url | http://dx.doi.org/10.1155/2020/9082370 |
| work_keys_str_mv | AT yunhuili visualanalyticmethodformetroanomalydetectionanddiffusion AT yongzhang visualanalyticmethodformetroanomalydetectionanddiffusion AT heshi visualanalyticmethodformetroanomalydetectionanddiffusion AT yunwei visualanalyticmethodformetroanomalydetectionanddiffusion AT baocaiyin visualanalyticmethodformetroanomalydetectionanddiffusion |