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Smart Transportation: A Reference Architecture for Big Data Analytics

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Smart Cities: A Data Analytics Perspective

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

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Acknowledgements

This research is supported by Fulbright Colombia and the Center of Excellence and Appropriation in Big Data and Data Analytics (CAOBA), supported by the Ministry of Information Technologies and Telecommunications of the Republic of Colombia (MinTIC) through the Colombian Administrative Department of Science, Technology, and Innovation (COLCIENCIAS) within contract No. FP44842-anexo46-2015.

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Authors and Affiliations

  1. Department of Computing and System Engineering, Universidad de Los Andes, Bogotá, Colombia

    Camilo Castellanos, Boris Perez & Dario Correal

  2. Department of Systems and Informatics, Universidad Francisco de Paula Stder, Cúcuta, Colombia

    Boris Perez

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  1. Camilo Castellanos
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  2. Boris Perez
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  3. Dario Correal
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Correspondence to Camilo Castellanos .

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Editors and Affiliations

  1. College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia

    Mohammad Ayoub Khan

  2. College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia

    Fahad Algarni

  3. College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia

    Mohammad Tabrez Quasim

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Castellanos, C., Perez, B., Correal, D. (2021). Smart Transportation: A Reference Architecture for Big Data Analytics. In: Khan, M.A., Algarni, F., Quasim, M.T. (eds) Smart Cities: A Data Analytics Perspective. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-60922-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-60922-1_8

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  • Online ISBN: 978-3-030-60922-1

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