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On Event Detection from Spatial Time Series for Urban Traffic Applications

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Solving Large Scale Learning Tasks. Challenges and Algorithms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9580))

Abstract

Since the last decades the availability and granularity of location-based data has been rapidly growing. Besides the proliferation of smartphones and location-based social networks, also crowdsourcing and voluntary geographic data led to highly granular mobility data, maps and street networks. In result, location-aware, smart environments are created. The trend for personal self-optimization and monitoring named by the term ‘quantified self’ will speed-up this ongoing process. The citizens in conjunction with their surrounding smart infrastructure turn into ‘living sensors’ that monitor all aspects of urban living (traffic load, noise, energy consumption, safety and many others). The “Big Data”-based intelligent environments and smart cities require algorithms that process these massive amounts of spatio-temporal data. This article provides a survey on event processing in spatio-temporal data streams with a special focus on urban traffic.

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Notes

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Acknowledgements

This research was supported by the National Council for Scientific and Technological Development (CNPq), the European Union’s Seventh Framework Programme under grant agreement number FP7-318225, INSIGHT and from the European Union’s Horizon 2020 Programme under grant agreement number H2020-ICT-688380, VaVeL. Additionally, this work has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876, project A1.

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Authors and Affiliations

  1. Fraunhofer Institute for Software and Systems Engineering, Fraunhofer ISST, Dortmund, Germany

    Gustavo Souto

  2. Artificial Intelligence Group, TU Dortmund University, Dortmund, Germany

    Thomas Liebig

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  1. Gustavo Souto
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Correspondence to Thomas Liebig .

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Editors and Affiliations

  1. TU Dortmund , Dortmund, Germany

    Stefan Michaelis

  2. TU Dortmund , Dortmund, Germany

    Nico Piatkowski

  3. TU Dortmund , Dortmund, Germany

    Marco Stolpe

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Souto, G., Liebig, T. (2016). On Event Detection from Spatial Time Series for Urban Traffic Applications. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-41706-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41705-9

  • Online ISBN: 978-3-319-41706-6

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