Big Data for Social Transportation
Zheng, Xinhu1; Chen, Wei2; Wang, Pu3; Shen, Dayong4; Chen, Songhang5; Wang, Xiao5; Zhang, Qingpeng6; Yang, Liuqing7
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2016-03-01
Volume17Issue:3Pages:620-630
SubtypeArticle
AbstractBig 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.
KeywordBig Data Social Transportation Intelligent Transportation System Data Analytics Crowdsourcing
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TITS.2015.2480157
WOS KeywordHUMAN MOBILITY ; SYSTEMS ; ISSUE ; NETWORKS ; PATTERNS ; EXPLORATION
Indexed BySCI
Language英语
Funding OrganizationNational 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 Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000371982600002
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11374
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.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
Recommended Citation
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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