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Title	Smart Transportation: A Reference Architecture For Big Data Analytics
ID_Doc	51622
Authors	Castellanos C.; Perez B.; Correal D.
Year	2021
Published	Lecture Notes in Intelligent Transportation and Infrastructure, Part F1386
DOI	http://dx.doi.org/10.1007/978-3-030-60922-1_8
Abstract	Smart transportation systems (STS) play a key role in smart cities as an underlying infrastructure that supports cities’ core services, such as public transport, mobility, and logistics. Information sources and analytics in STS include datasets with high volume, speed, and heterogeneity characteristics. Next-generation architectures must deal with the quality attributes of big data analytics (BDA) applications to store and process this kind of data. The combination of the extensive catalog of new technologies and multiple big data sources implies a large number of possible software solutions. Hence, organizations managing STS are facing many challenges when requiring fast adoption of these technologies, due to the lack of guidance on finding the right solution architecture. Previous works have proposed reference architectures (RA) for BDA or STS, but not both. The current STS RAs do not pay special attention to analytics capabilities over large volumes of data. This chapter presents a RA to address the challenges of big data analysis in STS, including architectural patterns and tactics. We use three case studies in accident analysis, real-time delay prediction, and mobility to illustrate and validate our proposal © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Analytics; Big data; Intelligent transport systems; Patterns; Reference architecture; Smart cities; Smart transportation; Tactics

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