Abstract
Intelligent transportation system (ITS) is a key enabler for future road traffic management systems. The core components of ITS include vehicles, roadside units, and traffic command centers. They generate a large amount of data flow that is made up of both mobility and service-related data. Therefore, some data science methods to handle the transportation data are very necessary for ITS. Although some attempts have been done to explore data science methods for ITS, there exist various scientific and engineering challenges including software and hardware development, computational complexity, data multi-source heterogeneity, and privacy protection. Consequently, to fully explore the benefits of ITS applications like connected and autonomous vehicles, traffic control and prediction, road safety, and accident prediction, advanced data science methodologies and applications are in great need.
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