Association Rules for Road Traffic Ayccident in Korea with Multiple Outcomes

다수의 결과를 고려한 한국의 도로교통사고 연관규칙분석

  • Sohn, So-Young (Department of Computer Science and Industrial Systems Engineering, Yonsei University) ;
  • Oh, Ki-Yeol (Department of Computer Science and Industrial Systems Engineering, Yonsei University) ;
  • Shin, Hyoung-Won (Department of Computer Science and Industrial Systems Engineering, Yonsei University)
  • 손소영 (연세대학교 컴퓨터과학.산업시스템공학과) ;
  • 오기열 (연세대학교 컴퓨터과학.산업시스템공학과) ;
  • 신형원 (연세대학교 컴퓨터과학.산업시스템공학과)
  • Received : 20020100
  • Accepted : 20020800
  • Published : 2002.12.31


In many cases, the result of a road traffic accident can be described with more than one response variables. Nonetheless, most of the existing road accident data analysis deal with only one response variable and try to explain why it occurs. In this paper, we train association rules for a set of more than two response variables conditional on personal, environmental and vehicular/behavioral aspects of accident. Association rules are derived at 8% support and 70% confidence from the 1996 data of three police stations in Korea. We expect that these rules can contribute to effective safety practice in Korea.


  1. Agrawal, R, Imielinski, T., and Swami, A.(1993), Database Mining: A Performance Perspective", in IEEE trans. on knowledge and data engineering., 5(6),914-925
  2. Kim, Eui Kyoung., Lee, Do Heon., Kim, Myoung Ho. and Lee, Yoon Joon.(1997), Mining Unidirectional Quantized Association Rules in Large Database, Journal of KlSS(B): Software and Applications, 24(4), 373-385
  3. Kim, Mi Young. (1993), Review of Road Traffic Accident Analysis Techniques and its Case Study Using Korean Data, Amaster's thesis of Seoul National University
  4. Lee, JI Byoung and Lim, Hyon Yean (1990), A Study on Development of Forecasting Model for Traffic Accident in Korea, journal of Korean Society of Transportation, 8(1), 73-88
  5. Mannila, H. and Raiha, K-J. (1987),"Dependency Inference," Proc. of 3rd Inri. Conf. on Very Large Data Bases., 155-158
  6. Oh, Yun Pyo and Ko, Sang Sean (1992), A Study about Establishment of Discrimination Model of Impact Factors of Big Traffic Accident - with Laws Violation Type -,Journal of Korean Society of Transportation, 10(3), 173-180
  7. Sohn, S. Y. andLee, S. H. (2001), Data Fusion, Ensemble and Clustering to Improve the Classification Accuracy for the Severity of Road Traffic Accident in Korea. accepted to Safety Science 2001
  8. Sohn, So Young. And Shin, Hyung Won. (1998), Data Mining for Road Traffic Accident Type Classification, journal of Korean Society of Transportation, 16(4), 187-194