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.
피인용 0
초록·키워드
Urban traffic accidents are closely related to street characteristics, and investigating this relationship is crucial for enhancing traffic safety. Street-level features primarily include the built environment elements that makes up the streets, such as greenery, sky, and other elements. As users of urban street spaces, travelers" perception indicators of various streets also include subjective perspectives describing the differential characteristics among urban streets. Therefore, this study focuses on exploring the impact of the street built-environment and travelers" perception indicators on the crash frequencies. The modeling process considers severity levels related to traffic crashes and then extends analysis to geospatial dimension to account for the spatial heterogeneity and correlation of coefficients. The study uses 587 road segments in the central area of Daejeon in Korea, with traffic crash data from the full year of 2019. All crashes are classified into killed/severe injury (KSI) and slight injury. Street built-environment elements are extracted from the street view images of the studied road sections using semantic segmentation technology, while street perception indicators are scored using deep learning methods based on the street view images, outputting results in six perceptual dimensions to create related variables. In addition, the impact of traffic variables such as AADT and road geometry (number of lanes, speed limits, etc.) is considered. The results suggest significant correlations among street built-environment, travelers" perception indicators and the frequency of traffic crashes, with different trends of impact for various crash classification. The estimation results of coefficients also show variations in the spatial influence of different feature, which could help traffic authorities identify high-risk areas and understand contributing factors, thereby taking precaution actions.
본문·목차
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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