IDENTIFYING MOTORWAY INCIDENTS BY NOVELTY DETECTION

The class of real interest (e.g., traffic incidents) is under-represented in our database. This is because the abnormalities are rate and difficult to collect in safety-critical applications. Conventional incident detection algorithms focus on identification and characterization of a wide range of different incidents from historic data. In this paper, alternative approaches which estimate the probability density of incident-free data are proposed. The abnormality of an input vector is therefore identified by testing for novelty against the description of normality. Experimental results on both simulated and field data show that the techniques are capable of detecting incidents and can thus be used in dynamic traffic monitoring systems.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0080435904
• Corporate Authors:

  Elsevier

  The Boulevard, Langford Lane
  Kidlington, Oxford  United Kingdom  OX5 1GB
• Authors:
  • Chen, Huanlei
  • Boyle, R D
  • KIRBY, H R
  • Montgomery, F O
• Conference:
  • World Transport Research: Selected Proceedings of the 8th World Conference on Transport Research
  • Location: Antwerp, Belgium
  • Date: 1998-7-12 to 1998-7-17
• Publication Date: 1999

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: p. 251-263

Subject/Index Terms

• TRT Terms: Incident detection; Incident management; Monitoring; Traffic data; Traffic incidents; Traffic measurement; Traffic safety; Traffic simulation
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 00783982
• Record Type: Publication
• ISBN: 0080435904
• Report/Paper Numbers: Volume 2
• Files: TRIS, ATRI
• Created Date: Feb 10 2000 12:00AM