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비지도 이상치 검지 기반 자전거 이용자의 대체 안전 지표 적용 가능성 연구: 자전거도로 AI 안전관리시스템 적용 사례 중심으로 KCI 등재
Applicability of Unsupervised Anomaly Detection as Surrogate Safety Measure for Bicycle Users: Case Application of AI-based Bike-Safe monitoring system
pp.259-266
The purpose of this study is to evaluate the applicability of an unsupervised outlier-detection method as a surrogate safety measure (SSM) to estimate the effect of AI-based Bike-Safe monitoring system. An SSM that utilizes near-miss data immediately before an accident occurs must be developed to compensate for inadequate bicycle accident data and missing reports. In particular, the omission level of accident reports related to bicycle users is higher on bicycle paths, which implies that the importance of an SSM in safety management is much greater than in the general road environment. Therefore, the unsupervised outlier-detection method was set as the SSM because it can be learned without a label, is suitable for streaming data, and is generalizable under limited data. Additionally, the DeepAnT(deep learningbased anomaly detection) model was selected as the most appropriate time-series outlier-detection method. Using the time-series prediction module of the learned DeepAnT model, we analyzed the frequency of outliers or avoidance behaviors based on a linear relationship between estimated and observed values. The history data of the acceleration change rate of each bicycle were applied to the DeepAnT model to evaluate the possibility of using alternative safety indicators. Thus, those data are expected to be applicable as an alternative safety indicator for bicycle paths.
1. 서론
2. 자전거도로 AI 안전관리 시스템(ABS) 소개
3. 대체 안전지표 연구 고찰
3.1. 주요 대체 안전지표
3.2. 자전거 이용자 관련 대체 안전 지표
4. 비지도 이상치 검지 방법 고찰
4.1. 시계열 이상치 유형 분류
4.2. 이상치 검지 방법
4.3. CNN 기반 DeepAnT 모델
5. 비지도 이상치 검지 방법 기반 자전거도로 대체안전지표 적용 제안
5.1. 현장 조사 및 관측 데이터 전처리
5.2. 모델 설정
5.3. 실험 결과 분석 및 소결
6. 결론
감사의 글
REFERENCES
