期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 17, 期 3, 页码 620-630出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2015.2480157
关键词
Big data; social transportation; intelligent transportation system; data analytics; crowdsourcing
资金
- National 973 Program of China [2015CB352503]
- Major Program of the National Natural Science Foundation of China [61232012, 71232006, 61233001, 61533019]
- National Natural Science Foundation of China [61202279, 71402157, 61473320]
- 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]
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.
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