| dc.date.accessioned |
2017-06-20T12:09:06Z |
und |
| dc.date.accessioned |
2017-10-24T12:24:30Z |
|
| dc.date.available |
2017-06-20T12:09:06Z |
und |
| dc.date.available |
2017-10-24T12:24:30Z |
|
| dc.date.issued |
2017-06-20T12:09:06Z |
|
| dc.identifier.uri |
http://radr.hulib.helsinki.fi/handle/10138.1/6130 |
und |
| dc.identifier.uri |
http://hdl.handle.net/10138.1/6130 |
|
| dc.title |
Mobility Modelling through Trajectory Decomposition and Prediction |
en |
| ethesis.department.URI |
http://data.hulib.helsinki.fi/id/225405e8-3362-4197-a7fd-6e7b79e52d14 |
|
| ethesis.department |
Institutionen för datavetenskap |
sv |
| ethesis.department |
Department of Computer Science |
en |
| ethesis.department |
Tietojenkäsittelytieteen laitos |
fi |
| ethesis.faculty |
Matematisk-naturvetenskapliga fakulteten |
sv |
| ethesis.faculty |
Matemaattis-luonnontieteellinen tiedekunta |
fi |
| ethesis.faculty |
Faculty of Science |
en |
| ethesis.faculty.URI |
http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca |
|
| ethesis.university.URI |
http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97 |
|
| ethesis.university |
Helsingfors universitet |
sv |
| ethesis.university |
University of Helsinki |
en |
| ethesis.university |
Helsingin yliopisto |
fi |
| dct.creator |
Faghihi, Farbod |
|
| dct.issued |
2017 |
|
| dct.language.ISO639-2 |
eng |
|
| dct.abstract |
The ubiquity of mobile devices with positioning sensors make it possible to derive user's location at any time. However, constantly sensing the position in order to track the user's movement is not feasible, either due to the unavailability of sensors, or computational and storage burdens. In this thesis, we present and evaluate a novel approach for efficiently tracking user's movement trajectories using decomposition and prediction of trajectories. We facilitate tracking by taking advantage of regularity within the movement trajectories.
The evaluation of our approach is done using three large-scale spatio-temporal datasets, from three different cities: San Francisco, Porto, and Beijing. Two of these datasets contain only cab traces and one contains all modes of transportation. Therefore, our approach is solely dependent on the inherent regularity within the trajectories regardless of the city or transportation mode. |
en |
| dct.subject |
trajectory prediction |
en |
| dct.subject |
trajectory analysis |
en |
| dct.subject |
human mobility |
en |
| dct.subject |
mobility modelling |
en |
| dct.language |
en |
|
| ethesis.language.URI |
http://data.hulib.helsinki.fi/id/languages/eng |
|
| ethesis.language |
English |
en |
| ethesis.language |
englanti |
fi |
| ethesis.language |
engelska |
sv |
| ethesis.thesistype |
pro gradu-avhandlingar |
sv |
| ethesis.thesistype |
pro gradu -tutkielmat |
fi |
| ethesis.thesistype |
master's thesis |
en |
| ethesis.thesistype.URI |
http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis |
|
| ethesis.degreeprogram |
Algorithms and Machine Learning |
en |
| dct.identifier.urn |
URN:NBN:fi-fe2017112251774 |
|
| dc.type.dcmitype |
Text |
|
