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Driver Group Clustering Technique and Risk Estimation Method for Traffic Accident Prevention

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(8), pp.53-58
  • DOI : 10.9708/jksci.2024.29.08.053
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : June 11, 2024
  • Accepted : July 26, 2024
  • Published : August 30, 2024

Tae-Wook Kim 1,  Ji-Woong Yang 2,  Hyeon-Jin Jung 3,  Han-Jin Lee 3,  Ellen J. Hong 3

1연세대학교(미래캠퍼스)
2한양대학교
3연세대학교

Accredited

ABSTRACT

Traffic accidents are not only a threat to human lives but also pose significant societal costs. Recently, research has been conducted to address the issue of traffic accidents by predicting the risk using deep learning technology and spatiotemporal information of roads. However, while traffic accidents are influenced not only by the spatiotemporal information of roads but also by human factors, research on the latter has been relatively less active. This paper analyzes driver groups and characteristics by applying clustering techniques to a traffic accident dataset and proposes and applies a method to calculate the Risk Level for each driver group and characteristic. In this process, the preprocessing technique suggested in this paper demonstrates a higher Silhouette Score of 0.255 compared to the commonly used One-Hot Embedding & Min-Max Scaling techniques, indicating its suitability as a preprocessing method.

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This paper was written with support from the National Research Foundation of Korea.
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