Urban Traffic Control Meets Decision Recommendation System: A Survey and Perspective

被引:0
|
作者
Ji, Qingyuan [1 ]
Wen, Xiaoyue [2 ]
Jin, Junchen [3 ]
Zhu, Yongdong [1 ]
Lv, Yisheng [4 ]
机构
[1] Zhejiang Lab, Hangzhou 311121, Peoples R China
[2] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou 310058, Peoples R China
[3] Zhejiang Univ, Coll Elect Engn, Hangzhou 310013, Peoples R China
[4] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
关键词
Surveys; Adaptation models; Computational modeling; Scalability; Transfer learning; Transportation; Traffic control; Recommendation system; traffic control; traffic perception; traffic prediction; NEURAL-NETWORKS; SIGNAL CONTROL; PREDICTIVE CONTROL; MODEL; GRAPH; ALGORITHMS; MANAGEMENT; LIGHTS;
D O I
10.1109/JAS.2024.124659
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems. Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level, utilizing their knowledge and expertise. However, this process is cumbersome, labor-intensive, and cannot be applied on a large network scale. Recent studies have begun to explore the applicability of recommendation system for urban traffic control, which offer increased control efficiency and scalability. Such a decision recommendation system is complex, with various interdependent components, but a systematic literature review has not yet been conducted. In this work, we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control, demonstrates the utility and efficacy of such a system in the real world using data and knowledge-driven approaches, and discusses the current challenges and potential future directions of this field.
引用
收藏
页码:2043 / 2058
页数:16
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