Submission on TripPlanning accepted at MUD’14!

Our joint paper to the  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}
    }