A Multilevel Traffic Incidents Detection Approach: Identifying Traffic Patterns and Vehicle Behaviours using real-time GPS data

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

23 Citations (Scopus)


This paper presents a multilevel approach for detecting traffic incidents causing congestion on major roads. It incorporates algorithms to detect unusual traffic patterns and vehicle behaviours on different road segments by utilising the real-time GPS data obtained from vehicles. The incident detection process involves two phases: (1) Identifies of road segments where abnormal traffic pattern is observed and further divides the 'abnormal segments' into smaller segments in order to isolate the potential incident area; (2) Performs a hierarchical analysis of the vehicles' GPS data, using predefined rules to detect any occurrence of abnormal behaviour within the 'abnormal' road section identified in phase 1. The strength of such approach lays in isolating road segments sequentially and then analysing vehicle data specific to the identified road segment. In this way, the processing of vast data is avoided which is an essential requirement for the better performance of such complex systems. The approach is demonstrated using a simulation of motorway segments near Coventry, UK.
Original languageEnglish
Title of host publication2007 IEEE Intelligent Vehicles Symposium
ISBN (Print)1-4244-1067-3
Publication statusPublished - Jun 2007
Event 2007 IEEE Intelligent Vehicles Symposium - Istanbul, Turkey
Duration: 13 Jun 200715 Jun 2007


Conference 2007 IEEE Intelligent Vehicles Symposium


Bibliographical note

This conference paper is not yet available on the repository. The paper was given at the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, 13-15 June 2007


  • hierarchical analysis
  • motorway segments
  • multilevel traffic incident detection
  • real-time GPS data
  • road segment
  • traffic patterns
  • vehicle behaviours

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