Big Data for Social Transportation

doi:10.1109/TITS.2015.2480157

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Big Data for Social Transportation
	Zheng, Xinhu 1; Chen, Wei 2; Wang, Pu 3; Shen, Dayong 4; Chen, Songhang5 ; Wang, Xiao5 ; Zhang, Qingpeng 6; Yang, Liuqing7
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2016-03-01
卷号	17 期号:3 页码:620-630
文章类型	Article
摘要	Big data for social transportation brings us unprecedented opportunities for resolving transportation problems for which traditional approaches are not competent and for building the next-generation intelligent transportation systems. Although social data have been applied for transportation analysis, there are still many challenges. First, social data evolve with time and contain abundant information, posing a crucial need for data collection and cleaning. Meanwhile, each type of data has specific advantages and limitations for social transportation, and one data type alone is not capable of describing the overall state of a transportation system. Systematic data fusing approaches or frameworks for combining social signal data with different features, structures, resolutions, and precision are needed. Second, data processing and mining techniques, such as natural language processing and analysis of streaming data, require further revolutions in effective utilization of real-time traffic information. Third, social data are connected to cyber and physical spaces. To address practical problems in social transportation, a suite of schemes are demanded for realizing big data in social transportation systems, such as crowdsourcing, visual analysis, and task-based services. In this paper, we overview data sources, analytical approaches, and application systems for social transportation, and we also suggest a few future research directions for this new social transportation field.
关键词	Big Data Social Transportation Intelligent Transportation System Data Analytics Crowdsourcing
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TITS.2015.2480157
关键词[WOS]	HUMAN MOBILITY ; SYSTEMS ; ISSUE ; NETWORKS ; PATTERNS ; EXPLORATION
收录类别	SCI
语种	英语
项目资助者	National 973 Program of China(2015CB352503) ; Major Program of the National Natural Science Foundation of China(61232012 ; National Natural Science Foundation of China(61202279 ; Fundamental Research Funds for the Central Universities ; National University of Singapore-Zheijiang University SeSaMe Center ; City University of Hong Kong(7200399) ; Natural Science Foundation of Guangdong Province, China(2014A030313753) ; Fok Ying Tong Education Foundation(141075) ; National Science Foundation(CNS-1343189) ; 71232006 ; 71402157 ; 61233001 ; 61473320) ; 61533019)
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000371982600002
引用统计	被引频次：147[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11374
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	1.Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA 2.Zhejiang Univ, State Key Lab Comp Aided Design & Comp Graph, Hangzhou 310058, Zhejiang, Peoples R China 3.Cent S Univ, Sch Traff & Transportat Engn, Changsha 400083, Hunan, Peoples R China 4.Natl Univ Def Technol, Coll Informat Syst & Management, Changsha 410073, Hunan, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 6.City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon, Hong Kong, Peoples R China 7.Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80525 USA
推荐引用方式 GB/T 7714	Zheng, Xinhu,Chen, Wei,Wang, Pu,et al. Big Data for Social Transportation[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2016,17(3):620-630.
APA	Zheng, Xinhu.,Chen, Wei.,Wang, Pu.,Shen, Dayong.,Chen, Songhang.,...&Yang, Liuqing.(2016).Big Data for Social Transportation.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,17(3),620-630.
MLA	Zheng, Xinhu,et al."Big Data for Social Transportation".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 17.3(2016):620-630.

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