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Spatiotemporal Big Data Challenges for Traffic Flow Analysis

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Book cover Reliability and Statistics in Transportation and Communication (RelStat 2017)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 36))

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Abstract

This paper contains a survey of spatiotemporal big data challenges in the area of urban traffic flow analysis. Existing sources and types of traffic flow data were reviewed and evidences that traffic flow data can be considered as spatiotemporal big data were provided. Current trends in spatiotemporal big data analytics and in urban traffic flow modelling and forecasting were consolidated and a list of joint emerging challenges was composed. The stated challenges cover different spatiotemporal aspects of big data and are linked to optimal time and space data resolution, spatial and temporal relationships in traffic data, computational complexity of spatiotemporal algorithms, fusion of traffic data from heterogeneous data sources into a single predictive scheme, and development of responsive streaming algorithms. The raised challenges are supported by an extensive literature review, and suggestions for future work are offered.

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Acknowledgements

This work was financially supported by the post-doctoral research aid programme of the Republic of Latvia (project no. 1.1.1.2/VIAA/1/16/112, “Spatiotemporal urban traffic modelling using big data”), funded by the European Regional Development Fund.

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Authors and Affiliations

  1. Transport and Telecommunication Institute, Riga, Latvia

    Dmitry Pavlyuk

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  1. Dmitry Pavlyuk
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Correspondence to Dmitry Pavlyuk .

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Editors and Affiliations

  1. Transport and Telecommunication Institute, Riga, Latvia

    Igor Kabashkin

  2. Transport and Telecommunication Institute, Riga, Latvia

    Irina Yatskiv

  3. Vilnius Gediminas Technical University, Vilnius, Lithuania

    Olegas Prentkovskis

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Pavlyuk, D. (2018). Spatiotemporal Big Data Challenges for Traffic Flow Analysis. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2017. Lecture Notes in Networks and Systems, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-319-74454-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-74454-4_22

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-74454-4

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