A comparative study of outlier detection for large-scale traffic data by one-class SVM and kernel density estimation

Henry Y T NGAN, Nelson H.C. Yung, Anthony G.O. Yeh

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

13 Citations (Scopus)


This paper aims at presenting a comparative study of outlier detection (OD) for large-scale traffic data. The traffic data nowadays are massive in scale and collected in every second throughout any modern city. In this research, the traffic flow dynamic is collected from one of the busiest 4-armed junction in Hong Kong in a 31-day sampling period (with 764,027 vehicles in total). The traffic flow dynamic is expressed in a high dimension spatial-temporal (ST) signal format (i.e. 80 cycles) which has a high degree of similarities among the same signal and across different signals in one direction. A total of 19 traffic directions are identified in this junction and lots of ST signals are collected in the 31-day period (i.e. 874 signals). In order to reduce its dimension, the ST signals are firstly undergone a principal component analysis (PCA) to represent as (x,y)-coordinates. Then, these PCA (x,y)-coordinates are assumed to be conformed as Gaussian distributed. With this assumption, the data points are further to be evaluated by (a) a correlation study with three variant coefficients, (b) one-class support vector machine (SVM) and (c) kernel density estimation (KDE). The correlation study could not give any explicit OD result while the one-class SVM and KDE provide average 59.61% and 95.20% DSRs, respectively.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Image Processing
Subtitle of host publicationMachine Vision Applications VIII
EditorsKurt S. Niel, Edmund Y. Lam
ISBN (Electronic)9781628414950
Publication statusPublished - 2015
EventImage Processing: Machine Vision Applications VIII - San Francisco, United States
Duration: 10 Feb 201511 Feb 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceImage Processing: Machine Vision Applications VIII
Country/TerritoryUnited States
CitySan Francisco

Scopus Subject Areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Correlation
  • Gaussian mixture model
  • Kernel density estimation
  • Outlier detection
  • Spatial-temporal signal
  • Support vector machine
  • Traffic data


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