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Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz)

KI 2017: KI 2017: Advances in Artificial Intelligence pp 128–141Cite as

Real-Time Public Transport Delay Prediction for Situation-Aware Routing

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10505))

Abstract

Situation-aware route planning gathers increasing interest. The proliferation of various sensor technologies in smart cities allows the incorporation of real-time data and its predictions in the trip planning process. We present a system for individual multi-modal trip planning that incorporates predictions of future public transport delays in routing. Future delay times are computed by a Spatio-Temporal-Random-Field based on a stream of current vehicle positions. The conditioning of spatial regression on intermediate predictions of a discrete probabilistic graphical model allows to incorporate historical data, streamed online data and a rich dependency structure at the same time. We demonstrate the system with a real-world use-case at Warsaw city, Poland.

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Notes

  1. 1.

    Our source code and the required virtual machine are publicly available as vagrant box at https://bitbucket.org/tliebig/developvm.

  2. 2.

    https://developers.google.com/transit/gtfs/.

  3. 3.

    Data was provided via https://api.um.warszawa.pl.

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Acknowledgements

This research received funding under the Horizon 2020 programme, grant number 688380 VaVeL - Variety, Veracity, VaLue: Handling the Multiplicity of Urban Sensors. We gratefully thank Nico Piatkowski for supply of his STRF library, support and discussion.

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Authors and Affiliations

  1. Computer Science VIII: Artificial Intelligence Unit, TU Dortmund University, Dortmund, Germany

    Lukas Heppe & Thomas Liebig

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  1. Lukas Heppe
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  2. Thomas Liebig
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Correspondence to Thomas Liebig .

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Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  2. FB Informatik, TU Darmstadt, Darmstadt, Hessen, Germany

    Johannes Fürnkranz

  3. FB Informatik, Universität Koblenz, Koblenz, Rheinland-Pfalz, Germany

    Matthias Thimm

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Heppe, L., Liebig, T. (2017). Real-Time Public Transport Delay Prediction for Situation-Aware Routing. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67189-5

  • Online ISBN: 978-3-319-67190-1

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