The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Predicting of the Severity of Car Traffic Accidents on a Highway Using Light Gradient Boosting Model

LightGBM 알고리즘을 활용한 고속도로 교통사고심각도 예측모델 구축

  • 이현미 (아주대학교 교통시스템공학과) ;
  • 전교석 (아주대학교 교통시스템공학과) ;
  • 장정아 (아주대학교 교통시스템공학과)
  • Received : 2020.10.07
  • Accepted : 2020.12.15
  • Published : 2020.12.31
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Abstract

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

본 연구는 고속도로 교통사고 심각도 예측모델을 구축하기 위해 다섯가지 머신러닝 기반의 분류모형 적용하였다. 2015년~2017년 동안 전국 고속도로에서 발생한 사고 데이터 21,013건을 5가지의 분류 모형을 적용한 결과 LightGBM(Light Gradient Boosting Model)이 가장 좋은 성능을 나타내는 것으로 나타났다. LightGBM에서는 교통사고심각도 추정에 있어 우선순위 요인으로 사고차량 수, 사고유형, 사고지점, 사고차로유형, 사고차량 유형 순으로 나타났다. 이러한 모형의 결과를 기반으로 일관적인 사고심각도 예측 과정을 통하여 교통사고대응관리 전략 수립에 활용할 수 있다. 본 연구는 국내 기계학습을 활용한 사례가 적은 여건에서 향후 빅데이터 기반의 다양한 기계학습 기법을 활용이 가능함을 제시하고 있다.

Keywords

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