Latest revision as of 10:52, 15 February 2021

Abstract

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.

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The different versions of the original document can be found in:

• http://downloads.hindawi.com/journals/jat/2018/7191549.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2018/7191549 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2018/7191549.pdf,

http://downloads.hindawi.com/journals/jat/2018/7191549.xml,

http://dx.doi.org/10.1155/2018/7191549 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1155/2018/7191549,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2018/7191549,

http://downloads.hindawi.com/journals/jat/2018/7191549.pdf,

https://academic.microsoft.com/#/detail/2886248458