NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition

doi:10.1109/TVT.2021.3060761

	NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition
	Qin, Yanjun 1; Luo, Haiyong 2; Zhao, Fang 1; Wang, Chenxing 1; Fang, Yuchen 1
刊名	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
	2021-03-01
卷号	70 期号:3 页码:2138-2152
关键词	Transportation Smart phones Feature extraction Discrete wavelet transforms Convolution Acceleration Correlation coefficient Mobile sensing transportation mode NIN Dilate Convolution GCN
ISSN号	0018-9545
DOI	10.1109/TVT.2021.3060761
英文摘要	Transportation mode recognition is a crucial task of Intelligent Transportation Systems (ITS) in smart city. Though many works have been investigated on transportation mode recognition in recent years, the accuracy and generality are still not able to meet the application requirements. In this paper, we propose a novel fusion framework for fine-grained transportation mode recognition, which consists of the Network in Network (NIN), Dilate Convolution and the Graph Convolutional Networks (GCN). In this framework, we first use NIN and Dilate Convolution to capture local and global features, respectively, and then introduce the graph convolutional network to learn the correlation of features. We construct a topological structure of the features based on the maximal information coefficient (MIC) criteria which is used to measure the similarity between two variables, and then obtain the adjacency matrix used for graph convolution. Extensive experimental results on the public Sussex-Huawei Locomotion-Transportation (SHL) dataset demonstrate the superiority of our proposed NDGCN to other state-of-the-art baselines with more than 22.3% higher accuracy.
资助项目	National Key Research and Development Program[2019YFC1511400] ; Action Plan Project of the Beijing University of Posts and Telecommunications - Fundamental Research Funds for the Central Universities[2019XD-A06] ; National Natural Science Foundation of China[61872046] ; Joint Research Fund for Beijing Natural Science Foundation[L192004] ; Haidian Original Innovation[L192004] ; Key Research and Development Project from Hebei Province[19210404D] ; Key Research and Development Project from Hebei Province[20313701D] ; Science and Technology Plan Project of InnerMongolia Autonomous Regio[2019GG328] ; BUPT Excellent Ph.D. Students Foundation[CX2020221] ; Open Project of theBeijingKey Laboratory of Mobile Computing and Pervasive Device
WOS研究方向	Engineering ; Telecommunications ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000637535800009
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/16666]
专题	中国科学院计算技术研究所
通讯作者	Luo, Haiyong; Zhao, Fang
作者单位	1.Beijing Univ Posts & Telecommun, Natl Pilot Software Engn Sch, Sch Comp Sci, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qin, Yanjun,Luo, Haiyong,Zhao, Fang,et al. NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2021,70(3):2138-2152.
APA	Qin, Yanjun,Luo, Haiyong,Zhao, Fang,Wang, Chenxing,&Fang, Yuchen.(2021).NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,70(3),2138-2152.
MLA	Qin, Yanjun,et al."NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 70.3(2021):2138-2152.