A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing

doi:10.1109/TVCG.2018.2889834

	A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing
	Bi, Huikun 1,2,3; Mao, Tianlu 2; Wang, Zhaoqi 2; Deng, Zhigang 4
	2020-07-01
发表期刊	IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
ISSN	1077-2626
卷号	26 期号:7 页码:2335-2348
摘要	Most of existing traffic simulation methods have been focused on simulating vehicles on freeways or city-scale urban networks. However, relatively little research has been done to simulate intersectional traffic to date despite its broad potential applications. In this paper, we propose a novel deep learning-based framework to simulate and edit intersectional traffic. Specifically, based on an in-house collected intersectional traffic dataset, we employ the combination of convolution network (CNN) and recurrent network (RNN) to learn the patterns of vehicle trajectories in intersectional traffic. Besides simulating novel intersectional traffic, our method can be used to edit existing intersectional traffic. Through many experiments as well as comparative user studies, we demonstrate that the results by our method are visually indistinguishable from ground truth, and our method can outperform existing methods.
关键词	Trajectory Solid modeling Computational modeling Vehicle dynamics Traffic control Data models Deep learning Traffic simulation crowd simulation data-driven deep learning intersectional traffic
DOI	10.1109/TVCG.2018.2889834
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFC0804900] ; National Natural Science Foundation of China[61532002] ; 13th Five-Year Common Technology pre Research Program[41402050301-170441402065] ; Science and Technology Mobilization Program of Dongguan[KZ2017-06] ; US NSF[IIS 1524782] ; CSC Fellowship
WOS研究方向	Computer Science
WOS类目	Computer Science, Software Engineering
WOS记录号	WOS:000542933100001
出版者	IEEE COMPUTER SOC
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15133
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Deng, Zhigang
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Houston, Comp Graph & Interact Media Lab, Houston, TX 77204 USA 4.Univ Houston, Comp Sci Dept, Houston, TX 77004 USA
推荐引用方式 GB/T 7714	Bi, Huikun,Mao, Tianlu,Wang, Zhaoqi,et al. A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2020,26(7):2335-2348.
APA	Bi, Huikun,Mao, Tianlu,Wang, Zhaoqi,&Deng, Zhigang.(2020).A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,26(7),2335-2348.
MLA	Bi, Huikun,et al."A Deep Learning-Based Framework for Intersectional Traffic Simulation and Editing".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 26.7(2020):2335-2348.