Submission on TripPlanning accepted at MUD’14!

Our joint paper to the ย Workshop on Mining Urban Data held at the International Conference on Extending Database Technology (EDBT) got accepted. It presents dynamic cost estimation for smart trip planning. Situation-aware trip planning is a major goal in the European INSIGHT project.

Abstract

Smart route planning gathers increasing interest as cities become crowded and jammed. We present a system for individual trip planning that incorporates future traffic hazards in routing. Future traffic conditions are computed by a Spatio-Temporal Random Field based on a stream of sensor readings. In addition, our approach estimates traffic flow in areas with low sensor coverage using a Gaussian Process Regression. 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 and test model assumptions with a real-world use-case from Dublin city, Ireland.

  • T. Liebig, N. Piatkowski, C. Bockermann, and K. Morik, “Predictive Trip Planning – Smart Routing in Smart Cities,” in Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014, 2014, pp. 331-338.
    [Bibtex] [[PDF] Draft]
    @inproceedings{liebig14,
    author = {Thomas Liebig and Nico Piatkowski and Christian Bockermann and Katharina Morik},
    title = {Predictive Trip Planning - Smart Routing in Smart Cities},
    booktitle = {Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014},
    year = {2014},
    publisher = {CEUR-WS.org},
    volume = {1133},
    pages = {331--338}
    }