Journal of Multimedia Information System

한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Traffic Accident using Association Rule Model

  • 투고 : 2018.03.10
  • 심사 : 2018.04.04
  • 발행 : 2018.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.

키워드

참고문헌

  1. J. Abellan, G. Lopez and J. Ona, "Analysis of traffic accident severity using Decision Rules via Decision Trees," Expert Systems with Applications, vol.40, no.15, pp.6047-6054, Nov. 2013. https://doi.org/10.1016/j.eswa.2013.05.027
  2. U. Castro and Y. Kim, "Data mining on road safety: factor assessment on vehicle accidents using classification models," International Journal of Crashworthiness, vol.21, no.2, pp.104-111, 2014.
  3. M. Chong, A. Abraham and M. Paprzycki, "Traffic Accident Analysis Using Machine Learning Paradigms," Information, vol.29, pp.89-98, 2005.
  4. J.Y. Lee, J.H. Chung and B. Son, "Analysis of traffic accident size for Korean highway using structural equation models," Accident Analysis and Prevention, vol.40, pp.1955-1963, 2008. https://doi.org/10.1016/j.aap.2008.08.006
  5. L. Li, S. Sherestha and G. Hu, "Analysis of Road Traffic Fatal Accidents Using Data Mining Techniques," in Proceeding of the Software Engineering Research, Management and Applications, London, July 2017.
  6. "J. Ona, R. Mujalli and F.J. Calvo, "Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks," Accident Analysis and Prevention, vol.43, no.1, pp.402-411, Jan. 2011. https://doi.org/10.1016/j.aap.2010.09.010
  7. V. Rovsek, M. Batista and B. Bogunovic "Identifying the key risk factors of traffic accident injury severity on Slovenian roads using a non-parametric classification tree," Transport, vol.32, no.3, pp.272-281, 2014. https://doi.org/10.3846/16484142.2014.915581
  8. Md.S. Satu, S. Ahamed, F. Hossain, T. Akter and D.Md. Farid, "Mining traffic accident data of N5 national highway in Bangladesh employing decision trees," in Proceeding of Humanitarian Technology Conference, Dhaka, Dec 2017.
  9. S. Sohn and H. Shin, "Pattern recognition for road traffic accident severity in Korea," Ergonomics, vol.44, no.1, pp.107-117, 2010. https://doi.org/10.1080/00140130120928
  10. M. Taamneh, S. Alkheder and S. Taamneh, "Datamining techniques for traffic accident modeling and prediction in the United Arab Emirates," Journal of Transportation Safety & Security, vol.9, no.2, pp.146-166, 2016.