Anomaly Detection on Collective Moving Patterns: Manifold Learning Based Analysis of Traffic Streams

Some special natural and social events like natural disasters, terrorism attacks, and traffic accidents may have remarkable effect on people´s collective behaviors, which means the behaviors of a large number of people viewed as a whole. Analysis of the patterns of collective behaviors in the sense of data mining is an important topic. Solving this problem is crucial and practical in that online detection of abnormal patterns of people´s collective behaviors may lead to rapid response to emergent affairs. In terms of technology, the task can be regarded as detection of abnormal particle movement patterns from a global point of view. In this study, we propose a solution to solve this problem. First, the traffic flows observed by more than 4000 sensors are organized as high-dimensional time series. Second, a widely used manifold learning technique referred to as locally linear embedding (LLE) is used to compute the K coefficients in fitting every data point with its K nearest neighbors in the high-dimensional s ce, which can be regarded as a K-dimensional local feature associated with every data point. Then, the local features of the data points within a time window are summarized by using principal component analysis (PCA) to obtain a global feature to represent the traffic data in every time window. Finally, outlier detection is performed on such PCALLE feature space. The experimental results show that the proposed method can effectively detect abnormal traffic patterns, which correspond with some special days, like the New Year day, the national day of America, and some days with extreme weather.