KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Black Ice Formation Prediction Model Based on Public Data in Land, Infrastructure and Transport Domain

국토 교통 공공데이터 기반 블랙아이스 발생 구간 예측 모델

  • 나정호 (전북대학교 기록관리학과) ;
  • 윤성호 (전북대학교 기록관리학과) ;
  • 오효정 (전북대학교 문헌정보학과, 문화융복합아카이빙연구소)
  • Received : 2021.02.09
  • Accepted : 2021.03.31
  • Published : 2021.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.

매년 동절기 블랙아이스(Black Ice)로 인한 사고는 빈번하게 발생하고 있으며, 치사율은 다른 교통사고에 비해 매우 높다. 따라서 블랙아이스 발생 구간을 사전에 예측하기 위한 체계화된 방법이 필요하다. 이에 본 논문에서는 이질(heterogeneous)·다형(diverse)의 데이터를 활용한 블랙아이스 발생 구간 예측 모델을 제안한다. 이를 위해 국토 교통 공공데이터와 기상 공공데이터 42종의 12,574,630건을 수집하여, 결측값을 처리하고 정규화하는 등의 전처리 과정을 수행한 뒤 최종 약 60만여 건의 정제 데이터셋을 구축하였다. 수집된 요인들의 상관관계를 분석하여 블랙아이스 예측에 유효한 영향을 주는 21개 요인을 선별, 다양한 학습모델을 조합하는 방법을 통해 블랙아이스 발생 예측 모델을 구현하였다. 이를 통해 개발된 예측 모델은 최종적으로 노선별 블랙아이스 위험지수 도출에 사용되어 블랙아이스 발생 경고 서비스를 위한 사전 연구로 활용될 것이다.

Keywords

Acknowledgement

이 논문은 2021년도 전북대학교 연구기반 조성비 지원에 의하여 연구되었음. 이 논문은 2020년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2019S1A5B8099507).

References

  1. S. Y. Kim, S. Y. Kim, Y. S. Jang, S. K. Kim, D. C. Min, H. H. Na, and J. S. Choi, "A study on the effects of factors of traffic accidents caused by frozen urban road surfaces in the winter," International Journal of Highway Engineering, Vol.17, No.2, pp.79-87, 2015. https://doi.org/10.7855/IJHE.2015.17.2.079
  2. Ilyo News Article, [Internet] https://ilyo.co.kr/?ac=article_view&entry_id=384970, 2020.
  3. G. Y. Park, S. H. Lee, E. J. Kim, and B. Y. Yun, "A case study on meteorological analysis of freezing rain and black ice formation on the load at winter," Journal of Environmental Science International, Vol.26 No.7 pp.827-836, 2017. https://doi.org/10.5322/JESI.2017.26.7.827
  4. Y. M. Lee, S. Y. Oh, and S. J. Lee, "A study on prediction of road freezing in Jeju," Journal of Environmental Science International, Vol.27, No.7, pp.531-541, 2018. https://doi.org/10.5322/JESI.2018.27.7.531
  5. J. Y. Kim, H. J. Lee, and J. R. Paik, "Survey of distinction of black ice using sensors," Journal of The Korea Society of Computer and Information, Vol.28, No.1, pp.78-87, 2020.
  6. Y. H. Kim, "Government 3.0 based consumer oriented big data service activation plan: Busan city service analysis information system and busan public data portal," Local Information Magazine, Vol.101, pp.16-21, 2016.
  7. D. Lai, Y. Zhang, X. Zhang, Y. Su, and M. B. Bin Heyat, "An automated strategy for early risk identification of sudden cardiac death by using machine learning approach on measurable arrhythmic risk markers," IEEE Access, Vol.7, pp.94701-94716, 2019. https://doi.org/10.1109/ACCESS.2019.2925847
  8. K. V. Sujatha, and S. Meenakshi Sundaram, "A combined PCA- MLP model for predicting stock index," A2CWiC '10: Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India, pp.1-6, Sep. 2010.
  9. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Parallel distributed processing: Explorations in the microstructure of cognition, Volume 1: Foundation," MIT Press, pp.318-362, Jan. 1986.
  10. S. M. Lee, J. S. Yeon, J. S. Kim, and S. S. Kim, "Semisupervised learning using the AdaBoost algorithm with SVM-KNN," The transactions of The Korean Institute of Electrical Engineers, Vol.61, No.9, pp.1336-1339, 2012. https://doi.org/10.5370/KIEE.2012.61.9.1336
  11. M. W. Lee, Y. G. Kim, Y. J. Jun, and Y. H. Shin, "Random Forest based Prediction of Road Surface Condition Using Spatio-Temporal Features," Journal of Korean Society of Transportation, Vol.37, No.4, pp.338-349, 2019. https://doi.org/10.7470/jkst.2019.37.4.338
  12. Y. H. Kim, J. Y. Hong, and B. J. Kim, "Performance Comparison of Machine Learning Classification Methods for Decision of Disc Cutter Replacement of Shield TBM," Journal of Korean Tunnelling and Underground Space Association, Vol.22, No.5, pp.575-589, 2020. https://doi.org/10.9711/KTAJ.2020.22.5.575
  13. M. C. Jeong, J. H. Lee, and H. Y. Oh, "Ensemble Machine Learning Model Based Youtube Spam Comment Detection," Journal of the Korea Institute of Information and Communication Engineering, Vol.24, No.5, pp.576-583, 2020. https://doi.org/10.6109/JKIICE.2020.24.5.576