Kernelized Map Matching for noisy trajectories. Jawad, A. & Kersting., K. In Atzmüller, M., Benz, D., Hotho, A., & Stumme, G., editors, Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet, Kassel, Germany, 2010.
Kernelized Map Matching for noisy trajectories [pdf]Paper  abstract   bibtex   
Map matching is a fundamental operation in many applications such as traffic analysis and location-aware services, the killer apps for ubiquitous computing. In the past, several map matching approaches have been proposed. Roughly speaking they can be categorized into four groups: geometric, topological, probabilistic, and other advanced techniques. Surprisingly, kernel methods have not received attention yet although they are very popular in the machine learning community due to their solid mathematical foundation, tendency toward easy geometric interpretation, and strong empirical performance in a wide variety of domains. In this paper, we show how to employ kernels for map matching. Specifically, ignoring map constraints, we first maximize the consistency between the similarity measures captured by the kernel matrices of the trajectory and relevant part of the street map. The resulting relaxed assignment is then "rounded" into an hard assignment fulfilling the map constraints. On synthetic and real-world trajectories, we show that kernels methods can be used for map matching and perform well compared to probabilistic methods such as HMMs.
@inproceedings{kdml10,
  abstract = {Map matching is a fundamental operation in many applications such as traffic analysis and location-aware services, the killer apps for ubiquitous computing. In the past, several map matching approaches have been proposed. Roughly speaking they can be categorized into four groups: geometric, topological, probabilistic, and other advanced techniques. Surprisingly, kernel methods have not received attention yet although they are very popular in the machine learning community due to their solid mathematical foundation, tendency toward easy geometric interpretation, and strong empirical performance in a wide variety of domains. In this paper, we show how to employ kernels for map matching. Specifically, ignoring map constraints, we first  maximize the consistency between the similarity measures captured by the kernel matrices of the trajectory and relevant part of the street map. The resulting relaxed assignment is then "rounded" into an hard assignment fulfilling the map constraints. On synthetic and real-world trajectories, we show that kernels methods can be used for map matching and perform well compared to probabilistic methods such as HMMs.},
  address = {Kassel, Germany},
  author = {Jawad, Ahmed and Kersting., Kristian},
  booktitle = {Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen {\&} Adaptivitaet},
  crossref = {lwa2010},
  editor = {Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  interhash = {1a06d2e6d31229425a6d8a2bc74643cb},
  intrahash = {7f45b386fc67dfe23b4dfab8f7900892},
  presentation_end = {2010-10-06 10:07:30},
  presentation_start = {2010-10-06 09:45:00},
  room = {0446},
  session = {kdml3},
  title = {Kernelized Map Matching for noisy trajectories},
  track = {kdml},
  url = {http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/kdml10.pdf},
  year = 2010
}
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