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Predictive Analysis of Traffic Accidents caused by Negligence of Safe Driving in Elderly using Seasonal ARIMA

계절 ARIMA 모형을 이용한 고령운전자의 안전운전불이행에 의한 교통사고건수 예측분석

  • Kim, Jae-Moon (Graduate School of Consulting, Kumoh National Institute of Technology) ;
  • Chang, Sung-Ho (School of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Kim, Sung-Soo (School of Industrial Engineering, Kumoh National Institute of Technology)
  • 김재문 (금오공과대학교 컨설팅대학원) ;
  • 장성호 (금오공과대학교 산업공학부) ;
  • 김성수 (금오공과대학교 산업공학부)
  • Received : 2016.11.08
  • Accepted : 2017.03.08
  • Published : 2017.03.31

Abstract

Even though cars have a good effect on modern society, traffic accidents do not. There are traffic laws that define the regulations and aim to reduce accidents from happening; nevertheless, it is hard to determine all accident causes such as road and traffic conditions, and human related factors. If a traffic accident occurs, the traffic law classifies it as 'Negligence of Safe Driving' for cases that are not defined by specific regulations. Meanwhile, as Korea is already growing rapidly elderly population with more than 65 years, so are the number of traffic accidents caused by this group. Therefore, we studied predictive and comparative analysis of the number of traffic accidents caused by 'Negligence of Safe Driving' by dividing it into two groups : All-ages and Elderly. In this paper, we used empirical monthly data from 2007 to 2015 collected by TAAS (Traffic Accident Analysis System), identified the most suitable ARIMA forecasting model by using the four steps of the Box-Jenkins method : Identification, Estimation, Diagnostics, Forecasting. The results of this study indicate that ARIMA $(1, 1, 0)(0, 1, 1)_{12}$ is the most suitable forecasting model in the group of All-ages; and ARIMA $(0, 1, 1)(0, 1, 1)_{12}$ is the most suitable in the group of Elderly. Then, with this fitted model, we forecasted the number of traffic accidents for 2 years of both groups. There is no large fluctuation in the group of All-ages, but the group of Elderly shows a gradual increase trend. Finally, we compared two groups in terms of the forecast, suggested a countermeasure plan to reduce traffic accidents for both groups.

Keywords

References

  1. Chang et al., Traffic Accident Analysis of Older Drivers, Journal of Korean Society of Transportation, 2007, pp. 249-258.
  2. Cho, J.H., A Study on types and causes of distracted driving, Korea Transportation Safety Authority, 2012.
  3. Chung, D.B. and Yoon, J.S., The Analysis of Demand Forecasting using Minitab, Gunpo-si, Gyeong gi-do, Korea : Eraetech, 2007, pp. 57-70.
  4. Chung, D.J., Forecasting Manpower Demand on Aged-Friendly Industry in Busan : Using ARIMA Model, Journal of Social Science Review, 2012, Vol. 43, No. 2, pp. 1-19.
  5. Han, G.J., SPSS Application-Forecasting and Time-series Analysis, 1st ed, Seoul, Korea : Baeksan Publication, 2015, pp. 287-302.
  6. Han, S.J. and Kim, K.J., Road Accident Trends Analysis with Time Series Models for Various Road Types, International Journal of Highway Engineering, 2012, Vol. 9, No. 3, pp. 1-12.
  7. Kim, H.K. and Kim, T.S., Time-Series Analysis and Prediction Theory, 1st ed, Seoul, Korea : Kyungmoonsa, 2003, pp. 92-100.
  8. Kim, K.B., The Characteristics of Traffic Accidents and Reduction Methods by Elderly Drivers to Prepare for the Aging Society-Focused on Jeju, Journal of The Korea Contents Association, 2014, Vol. 14, No. 7, pp. 151-160. https://doi.org/10.5392/JKCA.2014.14.07.151
  9. Kwon, S.H. and Oh, H.S., Short-term Forecasting of Power Demand based on AREA, Journal of Society of Korea Industrial and Systems Engineering, 2016, Vol. 39, No. 1, pp. 25-30. https://doi.org/10.11627/jkise.2016.39.1.025
  10. Lee, H.J., Traffic Safety Measure Establishment on Cause for Elderly People Traffic Accident, Ministry of Land, Infrastructure and Transport, 2011.
  11. Park, J.H. and Kim, S.G., Development of Accident Forecasting Models in Freeway Tunnels using Multiple Linear Regression Analysis, The Journal of the Korea Institute of Intelligent Transport Systems, 2012, Vol. 11, No. 6, pp. 145-154. https://doi.org/10.12815/kits.2012.11.6.145
  12. Park, S.J. and Jeon, T.J., A study on Introduction of Box-Jenkins Prediction Method, Journal of The Korea Management Science Review, 1984, Vol. 1, No. 1, pp. 68-80.
  13. Sun, I.S., An ARIMA model based Prediction study on Storage and Warehousing business, Journal of Korean Review of Management Consulting, 2015, Vol. 6, No. 1, pp. 77-91.
  14. Yoo, J.H. and Choi, K.I., A Comparative Analysis on Characteristics between Elder Drivers and Younger Drivers by Accident Types : With Commercial Vehicles, Journal of Transportation Technology and Policy, 2013, Vol. 10, No. 5, pp. 11-25.

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