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A deep marked graph process model for citywide traffic congestion forecasting

文献类型：期刊论文

作者Zhang, Tong3; Wang, Jianlong6; Wang, Tong; Pang, Yiwei7; Wang, Peixiao1; Wang, Wangshu2,4
刊名COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
出版日期2023-12-07
卷号N/A
DOI10.1111/mice.13131
文献子类Article ; Early Access
英文摘要Forecasting citywide traffic congestion on large road networks has long been a nontrivial research problem due to the challenge of modeling complex evolution patterns of congestion in highly stochastic traffic environments. Arguing that purely data-driven methods may not perform well for congestion forecasting, we propose a deep marked graph process model for predicting the congestion indices and the occurrence time of traffic congestion events for complex signalized road networks. Traffic congestion is considered as a nonrigorous spatiotemporal extreme event. We extend the traditional point process model by integrating a specially designed spatiotemporal graph convolutional network. This hybrid strategy takes advantage of the simple form of the point process model as well as the ability of graph neural networks to emulate the evolution of congestion. Experiments on real-world congestion data sets show that the proposed method outperforms state-of-the-art baseline methods, yielding satisfactory prediction results on a large signalized road network with superior computational efficiency.
WOS关键词NEURAL-NETWORK ; FLOW
WOS研究方向Computer Science ; Construction & Building Technology ; Engineering ; Transportation
WOS记录号WOS:001116022400001
源URL[http://ir.igsnrr.ac.cn/handle/311030/200926]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.TU Wien, Dept Geodesy & Geoinformat, Res Unit Cartog, Vienna, Austria
4.Wuhan Univ, LIESMARS, 129 Luoyu Rd, Wuhan 430079, Peoples R China
5.TU Wien, Dept Geodesy & Geoinformat, A-1040 Vienna, Austria
6.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China
7.Changjiang Space Informat Technol Engn Co Ltd, Wuhan, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Tong,Wang, Jianlong,Wang, Tong,et al. A deep marked graph process model for citywide traffic congestion forecasting[J]. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING,2023,N/A.
APA Zhang, Tong,Wang, Jianlong,Wang, Tong,Pang, Yiwei,Wang, Peixiao,&Wang, Wangshu.(2023).A deep marked graph process model for citywide traffic congestion forecasting.COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING,N/A.
MLA Zhang, Tong,et al."A deep marked graph process model for citywide traffic congestion forecasting".COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING N/A(2023).

入库方式： OAI收割

来源：地理科学与资源研究所

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