교통안전 빅데이터와 Transformer-LSTM을 활용한 시계열 교통류 예측 분석 및 Xgboost 기반 실시간 고속도로 화물운송경로 위험도 평가 방법론 개발

초록

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.

키워드