TY - GEN
T1 - Future locations prediction with uncertain data
AU - Qiu, Disheng
AU - Papotti, Paolo
AU - Blanco, Lorenzo
PY - 2013
Y1 - 2013
N2 - The ability to predict future movements for moving objects enables better decisions in terms of time, cost, and impact on the environment. Unfortunately, future location prediction is a challenging task. Existing works exploit techniques to predict a trip destination, but they are effective only when location data are precise (e.g., GPS data) and movements are observed over long periods of time (e.g., weeks). We introduce a data mining approach based on a Hidden Markov Model (HMM) that overcomes these limits and improves existing results in terms of precision of the prediction, for both the route (i.e., trajectory) and the final destination. The model is resistant to uncertain location data, as it works with data collected by using cell-towers to localize the users instead of GPS devices, and reaches good prediction results in shorter times (days instead of weeks in a representative real-world application). Finally, we introduce an enhanced version of the model that is orders of magnitude faster than the standard HMM implementation.
AB - The ability to predict future movements for moving objects enables better decisions in terms of time, cost, and impact on the environment. Unfortunately, future location prediction is a challenging task. Existing works exploit techniques to predict a trip destination, but they are effective only when location data are precise (e.g., GPS data) and movements are observed over long periods of time (e.g., weeks). We introduce a data mining approach based on a Hidden Markov Model (HMM) that overcomes these limits and improves existing results in terms of precision of the prediction, for both the route (i.e., trajectory) and the final destination. The model is resistant to uncertain location data, as it works with data collected by using cell-towers to localize the users instead of GPS devices, and reaches good prediction results in shorter times (days instead of weeks in a representative real-world application). Finally, we introduce an enhanced version of the model that is orders of magnitude faster than the standard HMM implementation.
UR - http://www.scopus.com/inward/record.url?scp=84886509149&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886509149&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40988-2_27
DO - 10.1007/978-3-642-40988-2_27
M3 - Conference contribution
AN - SCOPUS:84886509149
SN - 9783642409875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 417
EP - 432
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
Y2 - 23 September 2013 through 27 September 2013
ER -