Big Data Analytics in Intelligent Transportation Systems: A Survey
Abstract:
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.
Existing System:
Big Data analytics has resolved three problems: data storage, data analysis and data management. Big Data platforms such as Apache Hadoop and Spark are capable to processing massive amounts of data, and they have been widely used in academia and industry [12], [13]. Big Data analytics can also offer new opportunities to identify assets problems, such as pavement degradation, ballast aging, etc. It can help make maintenance decision in an appropriate time, and prevent the vehicle or infrastructure from being in a failure state.
Proposed System:
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.
CONCLUSIONS
In this paper, we presented the development of Big Data and the relevant knowledge of ITS. The framework of conducting Big Data analytics in ITS was discussed. We summarized the data source and collection methods, data analytics methods and platforms, and Big Data analytics application categories in ITS. We presented several applications of Big Data analytics in ITS, including asset maintenance, road traffic flow prediction, road traffic accidents analysis, public transportation service planning, personal travel route planning and rail transportation management and control. Several open challenges of using Big Data analytics in ITS were discussed in this paper, including data collection, data privacy, data storage, data processing, and data opening. Big Data analytics will have profound impacts on the design of intelligent transportation system, and make it safer, more efficient and profitable.
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