DOI QR코드

DOI QR Code

Correlation between Car Accident and Car Color for Intelligent Service

지능형서비스를 위한 자동차사고와 자동차색깔의 상관관계

  • Shin, Seong-Yoon (Department of Computer Information Engineering, Kunsan National University) ;
  • Lee, Sangwon (Division of Information and Electronic Commerce (Institute of Convergence and Creativity), Wonkwang University)
  • 신성윤 (군산대학교 컴퓨터정보공학과) ;
  • 이상원 (원광대학교 정보전자상거래학부)
  • Received : 2013.12.18
  • Accepted : 2013.12.24
  • Published : 2013.12.31

Abstract

In designing Intelligent Traffic Systems, it should be necessary to consider telecommunications, appearance, environment, auxiliary functions, safety, and so on. Also, in choosing a car, a consumer considers those properties. This paper tried to elucidate the fact that car color has a very significant meaning for car safety when administrating intelligent traffic services and making car-purchasing decision. We first studied on occurrence probability of car accident according to car color that has something to do with car safety. Then, we studied on the concepts of advancing color and receding color. Advancing color causes less accidents since the color looks closer than it actually is. And receding color causes more accidents since the color looks farther than it actually is. And we classified car colors into eight classes and assign their ranking to each class, considering the number of car accidents. We tried to verify our research by use of telephone questionnaire for residents in Kunsan, Republic of Korea.

지능형 교통 시스템을 설계하는데 있어서, 통신, 외관, 환경, 부수 기능, 안전 등이 반드시 고려되어야 한다. 또한, 자동차를 선택하는데 있어서도, 소비자는 이러한 특성들을 고려한다. 본 논문에서는, 지능화된 교통 서비스를 운영하거나 자동차 구매 의사결정을 하는데 있어서, 자동차 색깔이 자동차 안전에 있어서 매우 중요한 의미를 갖는다는 것을 밝히고자 한다. 자동차 안전과 관련이 있는 자동차 색깔에 따른 자동차 사고 확률에 대해 연구하였다. 그리고, 전진색과 후퇴색의 개념에 대해서도 연구하였다. 전진색은 실제보다 가깝게 보이기 때문에 사고를 적게 유발하지만, 후퇴색은 실제보다 멀리 보이기 때문에 사고를 많이 유발한다. 우리는 자동차 사고 건수를 고려하여, 자동차 색깔을 8개로 분류하고 이들의 등급을 부여하였다. 또한, 우리의 연구를 실증하기 위해서, 군산에 있는 거주자를 대상으로 전화상담을 통해 조사를 하였다.

Keywords

References

  1. Anders, R. L., "On-road Investigation of Fluorescent Sign Colors to Improve Conspicuity," Virginia Polytechnic Institute and State University, 2000.
  2. Ansah, O. and S. Osei, "Investigation of the Relationship between Vehicle Color and Safety," Master of Science, University of Dayton, Department of Civil Engineering, 2010.
  3. Baek, W. and N. Kim, "A Case Study on Forecasting Inbound Calls of Motor Insurance Company Using Interactive Data Mining Technique," Journal of Intelligence and Information Systems, Vol.16, No.3(2010), 99-120.
  4. Cho, I. and N. Kim, "Recommending Core and Connecting Keywords of Research Area Using Social Network and Data Mining Techniques," Journal of Intelligence and Information Systems, Vol.17, No.1(2011), 127-138.
  5. FEMA(Federal Emergency Management Agency), Emergency Vehicle Visibility and Conspicuity Study, U.S. Department of Homeland Security, 2009.
  6. Furness, S., J. Connor, E. Robinson, R. Norton, S. Ameratunga and R. Jackson, "Car Colour and Risk of Car Crash Injury: Population Based Case Control Study," British Medical Journal, Vol.327, No.7429(2003), 1455-1456. https://doi.org/10.1136/bmj.327.7429.1455
  7. Gates, T. J. and H. G. Hawkins, "Effect of Higher-Conspicuity Warning and Regulatory Signs on Driver Behavior," Texas Transportation Institute Report, No. 0-4271-S(2004), Texas A&M University.
  8. Hawkins, H. G., P. J. Carlson and M. Elmquist, "Evaluation of Fluorescent Orange Signs," Texas Transportation Institute Report, No. 0-2962-S(2000), Texas A&M University.
  9. Kim, M., N. Kim and J. Ahn, "An Investigation on Expanding Co-occurrence Criteria in Association Rule Mining," Journal of Intelligence and Information Systems, Vol.18, No.1 (2010), 23-38.
  10. Newstead, S. and A. D'Elia, "An Investigation into the Relationship between Vehicle Color and Crash Risk," Accident Research Centre Report, Monash University, Vol.263(2007).
  11. Shin, S., Y. Rhee, D. Jang, S. Lee, H. Lee and C. Jin, "Relationship Between Car Color and Car Accident on the Basis of Chromatic Aberration', Future Information Communication Technology and Applications," Lecture Notes in Electrical Engineering, Vol. 235, No.1(2013), 45-51. https://doi.org/10.1007/978-94-007-6516-0_6
  12. Yu, E., J. Kim, C. Lee, N. Kim and S. Jeong, "Predicting the Direction of the Stock Index by Using a Domain-Specific Sentiment Dictionary," Journal of Intelligence and Information Systems, Vol.19, No.1(2013), 95-110. https://doi.org/10.13088/jiis.2013.19.1.095

Cited by

  1. End to End Model and Delay Performance for V2X in 5G vol.22, pp.1, 2016, https://doi.org/10.13088/jiis.2016.22.1.107