The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Quantification Method of Driver's Dangerous Driving Behavior Considering Continuous Driving Time

연속주행시간을 고려한 운전자 위험운전행동의 정량화 방법

  • 이현미 (아주대학교 교통시스템공학과) ;
  • 이원우 (도로교통연구원 ICT융합연구실) ;
  • 장정아 (아주대학교 교통시스템공학과)
  • Received : 2022.06.25
  • Accepted : 2022.08.17
  • Published : 2022.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study is a method for evaluating and quantifying driver's dangerous driving behavior. The quantification method calculates various driving information in real time after starting the vehicle operation such as the time that the vehicle has been continuously driven without a break, overspeed, rapid acceleration, and overspeed driving time. These quantified risk of driving behavior values can be individually provided as a safe driving index, or can be used to objectify the evaluation of a group of drivers on roads, or vehicle groups such as cargo/bus/passenger vehicles.

본 연구는 운전자의 위험운전행동을 평가하고 정량화한 방법에 관한 것이다. 정량화 방법은 차량의 운행을 시작 이후 휴식 없이 연속적으로 주행한 시간, 과속, 급가속, 과속주행 시간 등 다양한 주행정보를 실시간으로 산출하도록 한다. 이러한 정량화된 위험운전행동 값은 개별로 안전운전지수로 제공하거나, 도로를 운행하는 운전자에 대하여 집단에 대한 평가 혹은 차량군에 대한 평가 등으로 객관화하는데 사용할 수 있다.

Keywords

References

  1. K. Kwon, J. Joo, and J. Jin, "Driving behavior and Driving Habit Analysis Utilizing Big Data Platform," J. of Information Technology and Architecture, vol. 12, no. 4, 2015, pp. 667-675.
  2. J. Jin, B. Yoo, W. Lee, and G. Kim, "Development of technology in estimating of high-risk driver's behavior," J. of the Korea Institute of Electronic Communication Science, vol. 11, no. 5, 2016, pp. 531-538. https://doi.org/10.13067/JKIECS.2016.11.5.531
  3. Y. Kong, H. Kim, Y. Lee, and S. Kang, "Development of incident detection algorithm using GPS Data," J. of the Korea Institute of Electronic Communication Science, vol. 16, no. 4, 2021, pp. 771-782. https://doi.org/10.13067/JKIECS.2021.16.4.771
  4. E. G. Mantouka, E. N. Barmpounakis, and E. I. Vlahogianni, "Mobile sensing and machine learning for identifying driving safety profiles," Transportation Research Board 97th Annual Meeting, Washington DC, United States, 2017.
  5. A. Stavrakaki, D. I. Tselentis, E. Barmpounakis, E. I. Vlahogianni, and G. Yannis, "Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior" Sensors, vol. 20, no. 9, 2020, pp. 2600-2612. https://doi.org/10.3390/s20092600
  6. I. D. Brown, "Driver fatigue," the J. of the Human Factors and Ergonomics Society, vol. 36, no. 2, 1994, pp. 98-314. https://doi.org/10.1177/001872089403600210
  7. S. Park, "Analysis of Motor Carrier Crash Risk with Driver Hours of Service," Korean Society of Road Engineers, vol. 12, no. 1, 2010, pp. 21-27.
  8. J. Cho, H. Lee, J. Lee, and D. Kim, "The Hazardous Expressway Sections for Drowsy Driving Using Digital Tachograph in Truck," Korean Society of Transportation, vol. 35, no. 2, 2017, pp. 160-168. https://doi.org/10.7470/jkst.2017.35.2.160
  9. S. Lee, J. Kang, H. Bae, and D. Yoon, "Vulnerability Assessment of the Air Pollution Using Entropy Weights: Focused on Ozone," J. of the Korean Association of Regional Geographers, vol. 24, no. 4, 2015, pp. 751-763.
  10. B. Kal, J. Park, S. Kim, K. Shim, S. Shin, and S. Choi, "A Study on Evaluation of Water Quality Measurement Network in the Nakdong River Tributary Using TOPSIS," Korean Wetlands Society, vol. 24, no. 1, Feb. 2022, pp. 44-51.