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2026 Vol.44, Issue 4 Preview Page

Article

Differentiated Safety Management of Commercial Vehicle Risky Driving: Linking Nationally Standardized DTG Indicators to Accident-Count Correlates Through Staged Multidimensional Analysis

단계적 다차원 분석을 통한 국가 표준 DTG 위험운전 지표와 노출 보정 사고건수 연관 요인의 연계 기반 사업용 차량 차별화 안전관리

Joobin JIN1†
,
Jeonghoon SONG1†
,
Jinwon YOON2
,
Jaeyun KIM3*
,
Byeongjoon NOH4
진 주빈1†
,
송 정훈1†
,
윤 진원2
,
김 재윤3*
,
노 병준4
1Master Student, Department of Future Convergence Technology, Soonchunhyang University, Asan 31538, Korea
2Assistant Professor, Department of Mobility Science and Engineering, Korea University, Sejong 30019, Korea
3Associate Professor, Department of AI and Big Data, Soonchunhyang University, Asan 31538, Korea
4Assistant Professor, Department of AI and Big Data, Soonchunhyang University, Asan 31538, Korea
1순천향대학교 미래융합기술학과 석사과정
2고려대학교 미래모빌리티학과 조교수
3순천향대학교 AI·빅데이터학과 부교수
4순천향대학교 AI·빅데이터학과 조교수
*Corresponding Author
†JIN, Joobin and SONG, Jeonghoon contributed equally to this work as co-first authors.
31 August 2026. pp. 703-721
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Abstract

Commercial vehicle crashes remain a disproportionate road-safety burden, yet risky-driving oversight often relies on uniform event-count thresholds that ignore organizational, demographic, and spatiotemporal context. This study develops an evidence-linked framework for differentiated commercial vehicle safety management by connecting nationally standardized digital tachograph (DTG) indicators with accident, company, driver, spatial, and temporal information through staged multidimensional analysis and scenario-specific validation. A star-schema data cube integrates the 11 regulated DTG event categories with traffic-accident, company, and driver data, and online analytical processing guides two scenarios. The 2019 Daejeon case study covers roughly 80 million DTG events, 247 companies, and 6,814 drivers. Scenario I shows that harsh-acceleration event burden is concentrated in large-fleet Local/ Route Bus operators with higher long-career driver concentration; HC3-robust OLS partially confirms this pattern (R2 = 0.403), while accidents per vehicle is not significant. Scenario II shows multi-layered hotspots, with bus/taxi events in the urban core and freight events on peripheral logistics corridors; NB GLM with a trip-exposure offset indicates that accident counts are most clearly associated with freight-event share, whereas harsh-acceleration EPV and nighttime are not significant. Cross-category replication shows category-specific patterns across harsh acceleration, harsh braking, and speeding. The framework distinguishes DTG event-burden hotspots from exposure-adjusted accident-count correlates for differentiated commercial-fleet safety management.

Keywords
data cube
multidimensional analysis
OLAP
risky-driving events
statistical validation

사업용 차량 사고는 도로안전 부담에서 불균형적으로 큰 비중을 차지하지만, 위험운전 관리는 여전히 조직적, 인구통계학적, 시공간적 맥락을 충분히 반영하지 못하는 획일적인 이벤트 건수 기준에 의존하는 경우가 많다. 본 연구는 국가 표준 디지털 운행기록계(DTG) 지표를 사고, 운송업체, 운전자, 공간, 시간 정보와 연계하고, 단계적 다차원 분석과 시나리오별 검증 절차를 결합함으로써 사업용 차량의 차별화된 안전관리를 위한 근거 기반 프레임워크를 제안한다. 스타 스키마 기반 데이터 큐브는 11개 DTG 위험운전 이벤트 범주를 교통사고, 운송업체, 운전자 자료와 통합하며, 온라인 분석처리(OLAP)를 통해 두 가지 시나리오 분석을 수행하였다. 2019년 대전광역시 사례연구는 약 8천만 건의 DTG 이벤트, 247개 업체, 6,814명의 운전자를 포함한다. 시나리오 I은 급가속 이벤트 부담이 장기 경력 운전자 비중이 높은 대규모 시내·노선버스 업체에 집중됨을 보였으며, HC3-robust OLS는 이러한 패턴을 부분적으로 확인하였다(R2 = 0.403). 반면 차량당 사고 건수는 통계적으로 유의하지 않았다. 시나리오 II는 버스·택시 이벤트가 도심부에, 화물 이벤트가 외곽 물류축에 집중되는 다층적 핫스팟 구조를 보였다. Trip exposure를 offset으로 포함한 NB GLM 결과, 사고 건수는 화물 이벤트 비중과 가장 뚜렷하게 연관되었으며, 급가속 EPV와 야간 시간대는 통계적으로 유의하지 않았다. 범주 간 반복 검증은 급가속, 급제동, 과속 간에 서로 다른 범주별 패턴이 존재함을 보였다. 본 프레임워크는 DTG 이벤트 부담 핫스팟과 노출 보정 사고건수 연관 요인을 구분함으로써 사업용 차량의 차별화된 안전관리를 지원한다.

키워드
데이터 큐브
다차원 분석
OLAP
위험운전 이벤트
통계적 검증
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 44
  • No :4
  • Pages :703-721
  • Received Date : 2026-05-19
  • Revised Date : 2026-06-02
  • Accepted Date : 2026-06-08
  • DOI :https://doi.org/10.7470/jkst.2026.44.4.703
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