| 제목 | 교통안전 빅데이터와 Transformer-LSTM을 활용한 시계열 교통류 예측 분석 및 Xgboost 기반 실시간 고속도로 화물운송경로 위험도 평가 방법론 개발 |
|---|---|
| Title | A Methodology for Real-time Risk Assessment of Freeway Freight Routes using Transformer-LSTM Time-series Prediction with Traffic Safety Big Data |
| 저자 |
박동혁 (한양대학교 ERICA 교통물류공학과) 박준영* (한양대학교 ERICA 교통물류공학과 및 스마트시티공학과) 김덕녕 (한국도로공사 도로교통연구원) |
| Author |
Donghyeok Park (Department of Smart City Engineering, Hanyang University ERICA) Juneyoung Park* (Department of Transportation and Logistics Engineering/Smart City Engineering, Hanyang University ERICA) Ducknyung Kim (Korea Expressway Corporation Research Institute) |
| Bibliography | Journal of Logistics Science & Technology, 5(1),44-58, 2024, |
| DOI | 10.23178/jlst.5.1.202403.003 |
| Key Words | Freight vehicle, Freight routes, Real-time risk assessment, Deep learning, Traffic data |
| 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. |
| PDF download | JM_5[1]-P44-58[3].pdf |