데이터 큐브 모델을 활용한 DTG 데이터 기반의 상업용 차량 위험운전행동에 대한 다차원 분석

진주빈; 김성훈; 강남일; 김해성; 이상호; 노병준

Domestic · Domestic Journal (KCI only) · 2024

데이터 큐브 모델을 활용한 DTG 데이터 기반의 상업용 차량 위험운전행동에 대한 다차원 분석

Authors 진주빈, 김성훈, 강남일, 김해성, 이상호, 노병준

Venue 대한교통학회논문지 42(6), pp.627-648, Dec 2024.

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AI-ready brief

Traffic accidents not only pose a serious risk in modern society, leading to injuries, disabilities, and even fatalities, but they also emerge as significant economic and social issues. Among these, commercial vehicle accidents entail higher safety concerns due to the vehicles' large size and weight, increasing the risk of fatalities.

Author abstract

Traffic accidents not only pose a serious risk in modern society, leading to injuries, disabilities, and even fatalities, but they also emerge as significant economic and social issues. Among these, commercial vehicle accidents entail higher safety concerns due to the vehicles' large size and weight, increasing the risk of fatalities. Despite various studies aimed at reducing accidents involving commercial vehicles, there is still a lack of research focused on establishing safety education and evaluation standards that reflect the risky driving behaviors specific to commercial vehicles within transport companies. Therefore, this study aims to design a data cube model for multidimensional analysis using Digital Tachograph (DTG) data. It performs a multidimensional analysis on the 11 major risky driving behaviors of commercial vehicles by transport company, utilizing various levels of abstraction in Online Analytical Processing (OLAP) operations. This experiment conducted scenario-based analyses focusing on two scenarios: (1) analyzing risky driving behaviors based on the size of the transport company and individual driver characteristics, and (2) analyzing the risky driving behaviors of transport companies based on temporal and spatial factors. The analysis results illustrate the relationship between traffic accidents and the risky driving behaviors of commercial vehicles, identifying and characterizing specific transport companies in detail. Additionally, spatiotemporal analysis identified regions and times where risky driving behaviors frequently occur. These findings are expected to serve as a practical foundation for establishing traffic safety policies and contribute to policy development for managing safety and reducing risky driving behaviors in commercial vehicles. Furthermore, this study provides important insights for technical and policy approaches to reducing traffic accidents.

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How does the paper model future vehicle motion or maneuver intent?

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Retrieval cues

trajectory predictionlane changedriving behaviormotion forecastingurban trafficrisky drivingITScontext-aware predictionDomesticDomestic Journal (KCI only)

Citation-ready BibTeX

@article{noh2024dtg,
  title   = {데이터 큐브 모델을 활용한 DTG 데이터 기반의 상업용 차량 위험운전행동에 대한 다차원 분석},
  author  = {진주빈 and 김성훈 and 강남일 and 김해성 and 이상호 and 노병준},
  year    = {2024},
  journal = {대한교통학회논문지 42(6), pp.627-648, Dec 2024.}
}

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