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 flow prediction (TFP) is an important topic in the fields of operation research and traffic engineering. It is dedicated to predicting the flow of people and vehicles in the transportation network within a specific time frame in the future. Accurate TFP has great significance for traffic management, urban planning, road design, and the development of intelligent transportation systems (ITS). This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. However, traditional TFP methods only focus on predicting time series in traffic data, and it is difficult for these methods to capture the interdependent relationship between the spatial distribution of traffic across a network and the temporal evolution of traffic conditions at each location. sequences. How to fully extract the spatiotemporal correlation of traffic flow (SCTF) is an urgent problem that needs to be solved based on DL prediction models. Concurrently, as science and technology advance, a growing variety of academics are attempting to incorporate reinforcement learning (RL) into TFP. Experimental results show that it can reduce vehicle queuing time and average delay to a greater extent, and alleviate air pollution. The article summarizes the models of DL and RL in TFP, comprehensively compares the benefits and drawbacks of various approaches, and proposes a vision for existing problems and future development.
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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