Trajectory Pattern Identification and Anomaly Detection of Pedestrian Flows Based on Visual Clustering

  • Li LiEmail author
  • Christopher Leckie
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 486)


Extracting pedestrian movement patterns and determining anomalous regions/time periods is a major challenge in data mining of massive trajectory datasets. In this paper, we apply contour map and visual clustering algorithms to visually identify and analyse areas/time periods with anomalous distributions of pedestrian flows. Contour maps are adopted as the visualization method of the origin-destination flow matrix to describe the distribution of pedestrian movement in terms of entry/exit areas. By transforming the origin-destination flow matrix into a dissimilarity matrix, the iVAT visual clustering algorithm is applied to visually cluster the most popular and related areas. A novel method based on the iVAT algorithm is proposed to detect normal/abnormal time periods with similar/anomalous pedestrian flow patterns. Synthetic and large, real-life datasets are used to validate the effectiveness of our proposed algorithms.


Data mining Pedestrian trajectory pattern Visualization Clustering iVAT algorithm 



This work is partially supported by China Scholarship Council. The authors want to acknowledge Mr. Xiaoting Wang for his suggestions on our works and the anonymous reviewers for their constructive suggestions and feedback.


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Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Department of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

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