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.