Abstract
Anomaly detection is an important problem that has been well researched in diverse application domains. However, to the best of our knowledge, the anomaly detection for metro traffic flow has not been investigated before. In this paper, we proposed a new framework to solve two problems about anomaly detection in the metro traffic flow: obtaining the potential information by every passenger’s trip and detecting anomalies among metro traffic flow. For the first problem, we proposed a novel encoding path model to infer the passing stations’ information for each trip. For the second problem, we provide an improved K-Nearest Neighbor Distort (KNN-Distort) algorithm to quantify the anomalies in the metro traffic flow. We conduct intensive experiments on a large real-world metro dataset to demonstrate the performance of our algorithms.
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Zhang, Y., Hu, N., Wang, W., Yu, H. (2015). A New Framework for Anomaly Detection Based on KNN-Distort in the Metro Traffic Flow. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_12
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DOI: https://doi.org/10.1007/978-3-319-20472-7_12
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