%0 Conference Paper %1 newson2009hidden %A Newson, Paul %A Krumm, John %B Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems %C New York, NY, USA %D 2009 %I ACM %K localisation map matching topoi %P 336--343 %R 10.1145/1653771.1653818 %T Hidden Markov Map Matching Through Noise and Sparseness %U http://doi.acm.org/10.1145/1653771.1653818 %X The problem of matching measured latitude/longitude points to roads is becoming increasingly important. This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs. The HMM elegantly accounts for measurement noise and the layout of the road network. We test our algorithm on ground truth data collected from a GPS receiver in a vehicle. Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced. We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration. We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work. %@ 978-1-60558-649-6

@inproceedings{newson2009hidden, abstract = {The problem of matching measured latitude/longitude points to roads is becoming increasingly important. This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs. The HMM elegantly accounts for measurement noise and the layout of the road network. We test our algorithm on ground truth data collected from a GPS receiver in a vehicle. Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced. We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration. We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work.}, acmid = {1653818}, added-at = {2015-10-16T15:11:42.000+0200}, address = {New York, NY, USA}, author = {Newson, Paul and Krumm, John}, biburl = {https://www.bibsonomy.org/bibtex/2d96b5257028b430cbc80951a3f598ac4/stumme}, booktitle = {Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems}, description = {Hidden Markov map matching through noise and sparseness}, doi = {10.1145/1653771.1653818}, interhash = {8d4148a1291dbcdbf1c3e7ae540f8d98}, intrahash = {d96b5257028b430cbc80951a3f598ac4}, isbn = {978-1-60558-649-6}, keywords = {localisation map matching topoi}, location = {Seattle, Washington}, numpages = {8}, pages = {336--343}, publisher = {ACM}, series = {GIS '09}, timestamp = {2015-10-16T15:11:42.000+0200}, title = {Hidden Markov Map Matching Through Noise and Sparseness}, url = {http://doi.acm.org/10.1145/1653771.1653818}, year = 2009 }