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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10505)


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.



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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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Science VIII: Artificial Intelligence UnitTU Dortmund UniversityDortmundGermany

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