Density-based outlier detection by local outlier factor on largescale traffic data

Mathew X. Ma, Henry Y T NGAN, Wei Liu

Research output: Contribution to journalConference articlepeer-review

29 Citations (Scopus)


A density-based outlier detection (OD) method is presented by measuring the local outlier factor (LOF) on a projected principal component analysis (PCA) domain from real world spatialtemporal (ST) traffic signals. Its aim is to detect traffic data outliers which are errors in data and traffic anomalies in real situations such as accidents, congestions and low volume. Since the ST traffic signals have a high degree of similarities, they are first projected to two-dimensional (2D) (x,y)-coordinates by the PCA to reduce its dimension as well as to remove noise, while keeping the anomaly information of the signals. Based on the designed LOF algorithm, a semi-supervised approach is employed to label any embedded outliers. It reaches an average detection success rate of 93.5%.

Original languageEnglish
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Publication statusPublished - 2016
EventImage Processing: Machine Vision Applications IX 2016 - San Francisco, United States
Duration: 14 Feb 201618 Feb 2016

Scopus Subject Areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics


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