Framework for traffic event detection using Shapelet Transform

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Early detection of traffic events is essential for informing Traffic Centers, drivers and intelligent vehicles about incoming dangers or congestion. In this study, a framework based on the shapelets technique for automated incident detection is proposed. Using the shapelets time series classification technique, sub sequences of the time series are generated, which represent patterns of incidents/congestion as well as regular traffic situations. Using such shapelets, our framework is able to detect whether an incident is occurring or not. Application of this approach to real-life data of the London Orbital Motorway (M25) proved that our approach not only has the potential to improve the performance of the classification in terms of false alarm rates or/and accuracy but also provides the human expert with insightful interpretation of the decision made by the event detectors.

Original languageBritish English
Pages (from-to)226-235
Number of pages10
JournalEngineering Applications of Artificial Intelligence
StatePublished - Jun 2019


  • Automated incident detection
  • Shapelet Transform
  • Time series analysis


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