Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 20, Issue 1, Pages 383-398Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2018.2815678
Keywords
Big data analytics; intelligent transportation systems (ITS); machine learning; transportation
Categories
Funding
- National Natural Science Foundation of China [61603026]
- Beijing Natural Science Foundation [L171004]
- Technological Research and Development Program of China Railway Corporation [2016X008-B]
- State Key Laboratory of Rail Traffic Control and Safety [RCS2017ZT006, KIE017001531]
- Beijing Key Laboratory of Urban Rail Transit Automation and Control
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Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
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