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A Big Data Architecture for Traffic Forecasting Using Multi-Source Information

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Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10230))

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Abstract

An important strand of predictive analytics for transport related applications is traffic forecasting. Accurate approximations of the state of transport networks in short, medium or long-term future horizons can be used for supporting traveller information, or traffic management systems. Traffic forecasting has been the focus of many researchers over the last two decades. Most of the existing works, focus on single point, corridor, or intersection based predictions with limited efforts to solutions that cover large metropolitan areas. In this work, an open big-data architecture for road traffic prediction in large metropolitan areas is proposed. The functional characteristics of the architecture, that allows processing of data from various sources, such as urban and inter-urban traffic data streams and social media, is investigated. Furthermore, its conceptual design using state-of-the-art computing technologies is realised.

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Acknowledgments

This research was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 636160-2, the Optimum project, http://www.optimumproject.eu.

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Authors and Affiliations

  1. Sustainable Transport Research Group, Department of Civil Engineering, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK

    Yannis G. Petalas, Ahmad Ammari, Panos Georgakis & Chris Nwagboso

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  1. Yannis G. Petalas
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  2. Ahmad Ammari
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  3. Panos Georgakis
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  4. Chris Nwagboso
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Correspondence to Yannis G. Petalas .

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Editors and Affiliations

  1. Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, Victoria, Australia

    Timos Sellis

  2. Informatics, Ionian University, Kerkyra, Greece

    Konstantinos Oikonomou

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Petalas, Y.G., Ammari, A., Georgakis, P., Nwagboso, C. (2017). A Big Data Architecture for Traffic Forecasting Using Multi-Source Information. In: Sellis, T., Oikonomou, K. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2016. Lecture Notes in Computer Science(), vol 10230. Springer, Cham. https://doi.org/10.1007/978-3-319-57045-7_5

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