교통안전 빅데이터와 Transformer-LSTM을 활용한 시계열 교통류 예측 분석 및 Xgboost 기반 실시간 고속도로 화물운송경로 위험도 평가 방법론 개발
A Methodology for Real-time Risk Assessment of Freeway Freight Routes using Transformer-LSTM Time-series Prediction with Traffic Safety Big Data
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물리적 피해와 인명 피해가 큰 화물차 사고는 공급망 차원에서 리드 타임에 크게 영향을 미쳐 신뢰도 저하와 재주문율 하락으로 이어져 화물차 교통사고를 잠재적 위험 요소로 판단해야 한다. 본 연구는 교통‧기상‧모바일 데이터를 통해 교통안전과 복합적으로 연관된 요소를 기반으로 단기 교통류를 예측하고 사전에 사고를 포착할 수 있는 실시간 화물 운송 경로 위험도 평가 방법론을 개발하는 것을 목적으로 한다. 우선, 실시간 교통 데이터를 기반으로 Transformer-LSTM을 활용하여 시계열 교통류를 예측하였다. 다음으로 Xgboost 기반의 화물차 사고 예측모형을 개발하였다. 분석 결과 위양성률이 5.23%로 도출되어 실시간 화물운송경로 위험도 평가에 효과적일 것으로 판단된다. 연구의 결과물은 향후 화물차 운전자를 위한 안전경로안내 서비스를 제공할 수 있으며 민간 내비게이션 업체와 협력을 통해 화물차 사고 예방을 위한 실시간 사고위험 경고 서비스 제공에 활용할 수 있다.
Abstract
Freight vehicle crashes with physical damage and human casualties can significantly affect lead times at the supply chain level, leading to decreased reliability and lower reorder rates, making it necessary to consider freight vehicle traffic accidents as a potential risk factor. This study aims to develop a real-time freight transportation route risk assessment methodology that can predict short-term traffic flows and detect crashes in advance based on factors that are complexly related to traffic safety through traffic, weather, and mobile data. First, based on real-time traffic data, Transformer-LSTM was used to predict time series traffic flow. Next, Xgboost based real-time crash prediction model for freight vehicles was developed. As a result of the analysis, a false positive rate of 5.23% was obtained, and it is judged to be effective for real-time risk assessment of cargo transportation routes. The results of the study can be used to provide a safe route guidance service for freight vehicle drivers in the future. Moreover, real-time crash risk warning services can be applicable to prevent freight vehicle crashes through cooperation with private navigation companies.
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