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2024 Vol.42, Issue 6

Article

Multi-dimensional Analytical for Risky Driving Behaviors of Commercial Vehicles Based on Digital TachoGraph Data using Data Cube Model

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

Joobin JIN1
,
Sunghun KIM1
,
Namil KANG1
,
Haesoung KIM1
,
Sangho LEE2
,
Byeongjoon NOH3*
진 주빈1
,
김 성훈1
,
강 남일1
,
김 해성1
,
이 상호2
,
노 병준3*
1Student, Department of AI and Big Data, SoonChunHyang University, Asan 31538, Korea
2Senior Researcher, ITS Team, Nota AI, Seoul 06164, Korea
3Assistant Professor, Department of AI and Big Data, SoonChunHyang University, Asan 31538, Korea
1순천향대학교 AI·빅데이터학과 학부과정
2Nota AI ITS팀 수석연구원
3순천향대학교 AI·빅데이터학과 조교수
*Corresponding Author
31 December 2024. pp. 627-648
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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.

Keywords
commercial vehicle behavior analysis
data cube model
multi-dimensional analysis
OLAP
risky driving behavior

교통사고는 부상, 장애, 심지어 인명 손실까지 이어져 현대 사회에 심각한 위험을 초래할 뿐만 아니라 경제적, 사회적으로도 중요한 문제로 대두되고 있다. 그중 상업용 차량의 사고는 운행의 특성상 차량의 크기와 무게로 인해 인명 손실의 위험이 더 높아지는 안전 문제를 내포한다. 이러한 상업용 차량의 사고를 줄이기 위해 다양한 연구들이 이루어져 왔지만, 운수업체를 중심으로 하여 상업용 차량의 위험운전행동을 반영한 안전 교육 및 평가 기준 설정에 관한 연구는 아직 미비한 실정이다. 따라서, 본 연구는 DTG 데이터를 활용하여 데이터 큐브 모델을 설계하고, 여러 수준의 추상화 단계에서 온라인 분석 기법을 통해 추상화 수준을 달리하여 운수업체별 11대 위험운전행동에 관한 다차원 분석을 수행하는 것을 목적으로 한다. 본 실험에서는 (1) 운수업체 규모 및 개별 운전자의 특성에 따른 위험운전행동 분석, (2) 시간대별 및 공간별 운수업체의 위험운전행동 분석, 두 가지 시나리오를 설계하여 시나리오 기반의 분석을 실시하였다. 분석 결과는 교통사고와 상업용 차량의 위험운전 사이의 연관성을 확인하였으며, 특정 운수업체들을 세밀하게 선별하여 그 특성을 식별하였다. 또한, 시공간별 분석을 통해 위험운전행동이 자주 나타나는 지역과 시간대를 확인하였다. 이는 운수업체별 상업용 차량의 위험운전행동을 식별하고 교통안전 관련 정책 수립에 있어 실질적인 기초 자료로 활용될 것으로 기대된다. 또한, 상업용 차량의 안전 관리와 위험운전행동 감소를 위한 정책 개발에 기여할 것이며, 향후 교통사고 감소를 위한 기술적, 정책적 접근 방법에 중요한 시사점을 제공할 것으로 기대된다.

키워드
상업용 차량
데이터 큐브 모델
다차원 분석
OLAP 연산
위험운전행동
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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 : 42
  • No :6
  • Pages :627-648
  • Received Date : 2024-05-28
  • Revised Date : 2024-06-24
  • Accepted Date : 2024-08-09
  • DOI :https://doi.org/10.7470/jkst.2024.42.6.627
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