인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Traffic accidents pose significant global challenges, causing substantial injuries, fatalities, and economic losses. Current research predominantly focuses on single-prediction objectives (e.g., fatality prediction) while neglecting property damage assessments and critical interactions between prediction tasks. Although neural networks demonstrate superior predictive capabilities, their application in traffic safety analysis remains constrained by inherent limitations in causal interpretability, coupled with challenges posed by data imbalance, heterogeneity, and complexity in crash datasets. This study proposes an interpretable multi-task learning framework (Adv MT-DNN) that synergistically integrates an enhanced deep neural network with post-hoc explanation methods for comprehensive crash severity prediction. Our dual-focused approach addresses multiple prediction targets (including fatalities, severe injuries, and property damage). It provides granular insights into contributing factors through SHAP-based feature importance rankings and interaction analysis. Validated using four-year (2018-2021) multi-source traffic data from China, the framework demonstrates significant improvements in prediction accuracy compared to baselines. Nonparametric estimation of the top-8 critical factors (e.g., blood alcohol content, collision type, and accident occurrence period) confirms statistically significant associations with crash severity. The explicit interpretation mechanism bridges the critical gap between predictive performance and model interpretability in traffic safety analytics, providing engineering-relevant insights. This research establishes a robust methodological foundation for developing data-driven road safety policies and intelligent transportation systems, particularly in developing countries with complex traffic ecosystems.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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