| 引用本文: | 郇宁,姚恩建,薛飞.深度特征提取下城轨客流异常状态识别[J].哈尔滨工业大学学报,2021,53(3):94.DOI:10.11918/201907013 |
| HUAN Ning,YAO Enjian,XUE Fei.Anomaly detection of metro passenger flow using a deep learning based feature extraction method[J].Journal of Harbin Institute of Technology,2021,53(3):94.DOI:10.11918/201907013 |
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| 摘要: |
| 为实现对城市轨道交通突发大客流的及时预警,提出一种基于自动售检票数据的客流异常状态识别方法. 首先,确定符合客流时变特性的滑动时间窗口长度以适应动态的数据环境；其次,建立深度置信网络模型以提取窗口内待检样本的客流特征,并实现样本特征模式的自适应划分；最后,将待检样本和相同模式的历史样本映射至多维特征空间,进行基于局部异常因子的客流异常状态识别. 通过广州地铁的案例分析,结果表明:该方法的模式划分精度为92.5%,异常识别误检率和准确率分别为3.98%和91.9%,识别效果与异常的形式和程度相关,且受识别合格判定条件中反应时效要求的影响,整体上能够在保证较低误检率的情况下,实现对各类客流异常状态的灵敏识别. |
| 关键词: 城市轨道交通 异常检测 深度置信网络 客流量 滑动时间窗口 |
| DOI：10.11918/201907013 |
| 分类号:U231.92 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2018YFB1601300)；中央高校基本科研业务费项目(2019JBZ7,9YJS102) |
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| Anomaly detection of metro passenger flow using a deep learning based feature extraction method |
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HUAN Ning,YAO Enjian,XUE Fei
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(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport (Beijing Jiaotong University), Beijing 100044, China)
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| Abstract: |
| To provide timely alerts for the unexpected surges of passenger flow in urban rail transit, an anomaly detection method was proposed based on the smart card data collected by the automated fare collection system. First, the length of the sliding time window was determined in light of the time-varying characteristics of passenger flow to adapt to the dynamic data environment. Then, a deep belief network model was developed to extract the features hidden in the target sample, and further recognize the pattern of the target sample within the time window. Finally, by mapping the target sample and the historical samples of the same pattern to a multi-dimensional feature space, the anomalies in metro passenger flow could thus be identified in terms of local outlier factor. According to the case study of Guangzhou Metro, results show that the pattern recognition accuracy of the proposed method was 92.5% and the anomaly detection false rate and accuracy were 3.98% and 91.9%, respectively. The detection performance of the method was related to the forms and degrees of the anomalies, and affected by the acceptance criteria of the response time. Overall, the proposed method could accurately detect a broad range of anomalies in the cost of a comparatively low false rate. |
| Key words: urban rail transit outlier detection deep belief network passenger flow sliding time window |
