Cluster-based correlation of severe driving events with time and location

Guoyan Cao, John Michelini, Karolos Grigoriadis, Behrouz Ebrahimi, Matthew A. Franchek

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

In this article, a systematic strategy is proposed to identify severe driving events occurrence correlation with time and location. The proposed approach, which is constructed based on batch clustering and real-time clustering techniques, incorporates historical and real-time data to predict the time and location of severe driving events. Batch clustering is implemented with the combination of subtractive clustering and fuzzy c-means clustering to generate clusters representing the initial correlation patterns. Real-time clustering is then developed to create and update real-time correlation patterns on the foundation of the batch clustering using the evolving Gustafson–Kessel like (eGKL) algorithm. In both clustering processes, the correlation of the events within time domain is identified first, and then two different levels of accurate correlations are conducted for the location domain. Real-time data of operating vehicles each equipped with a data acquisition and wireless communication platform are used to validate the proposed strategy. Batch clustering results reveal the severe braking events distribution and concentration at daytime and nighttime. Real-time clustering provides and updates the variation of the correlations/intercorrelation of different regions. Drivers can be notified of the potential severe driving locations through maps showing the driving routes. Through the variation of the correlations, drivers can recognize the events occurrence at different times and locations. The generated time series can be potentially used to develop spatial-time models for regions to model and forecast the events occurrence.

Original languageEnglish (US)
Pages (from-to)516-531
Number of pages16
JournalJournal of Intelligent Transportation Systems: Technology, Planning, and Operations
Volume20
Issue number6
DOIs
StatePublished - Nov 1 2016

Keywords

  • clustering
  • correlation identification
  • evolving Gustafson Kessel approach
  • severe driving events

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

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