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Article: Big Data for Social Transportation

TitleBig Data for Social Transportation
Authors
KeywordsBig data
crowdsourcing
data analytics
intelligent transportation system
social transportation
Issue Date2016
Citation
IEEE Transactions on Intelligent Transportation Systems, 2016, v. 17, n. 3, p. 620-630 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/330382
ISSN
2023 Impact Factor: 7.9
2023 SCImago Journal Rankings: 2.580
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, Xinhu-
dc.contributor.authorChen, Wei-
dc.contributor.authorWang, Pu-
dc.contributor.authorShen, Dayong-
dc.contributor.authorChen, Songhang-
dc.contributor.authorWang, Xiao-
dc.contributor.authorZhang, Qingpeng-
dc.contributor.authorYang, Liuqing-
dc.date.accessioned2023-09-05T12:10:06Z-
dc.date.available2023-09-05T12:10:06Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Intelligent Transportation Systems, 2016, v. 17, n. 3, p. 620-630-
dc.identifier.issn1524-9050-
dc.identifier.urihttp://hdl.handle.net/10722/330382-
dc.description.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.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systems-
dc.subjectBig data-
dc.subjectcrowdsourcing-
dc.subjectdata analytics-
dc.subjectintelligent transportation system-
dc.subjectsocial transportation-
dc.titleBig Data for Social Transportation-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TITS.2015.2480157-
dc.identifier.scopuseid_2-s2.0-84949989783-
dc.identifier.volume17-
dc.identifier.issue3-
dc.identifier.spage620-
dc.identifier.epage630-
dc.identifier.isiWOS:000371982600002-

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