Urban Sensing for Anomalous Event Detection:

Distinguishing Between Legitimate Traffic Changes and Abnormal Traffic Variability
  • Masoomeh ZameniEmail author
  • Mengyi He
  • Masud Moshtaghi
  • Zahra Ghafoori
  • Christopher Leckie
  • James C. Bezdek
  • Kotagiri Ramamohanarao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


Sensors deployed in different parts of a city continuously record traffic data, such as vehicle flows and pedestrian counts. We define an unexpected change in the traffic counts as an anomalous local event. Reliable discovery of such events is very important in real-world applications such as real-time crash detection or traffic congestion detection. One of the main challenges to detecting anomalous local events is to distinguish them from legitimate global traffic changes, which happen due to seasonal effects, weather and holidays. Existing anomaly detection techniques often raise many false alarms for these legitimate traffic changes, making such techniques less reliable. To address this issue, we introduce an unsupervised anomaly detection system that represents relationships between different locations in a city. Our method uses training data to estimate the traffic count at each sensor location given the traffic counts at the other locations. The estimation error is then used to calculate the anomaly score at any given time and location in the network. We test our method on two real traffic datasets collected in the city of Melbourne, Australia, for detecting anomalous local events. Empirical results show the greater robustness of our method to legitimate global changes in traffic count than four benchmark anomaly detection methods examined in this paper. Data related to this paper are available at:


Pedestrian event detection Vehicle traffic event detection Anomaly detection Urban sensing Smart cities 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Masoomeh Zameni
    • 1
    Email author
  • Mengyi He
    • 2
  • Masud Moshtaghi
    • 3
  • Zahra Ghafoori
    • 1
  • Christopher Leckie
    • 1
  • James C. Bezdek
    • 1
  • Kotagiri Ramamohanarao
    • 1
  1. 1.The University of MelbourneMelbourneAustralia
  2. 2.Kepler AnalyticsMelbourneAustralia
  3. 3.AmazonManhattan BeachUSA

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