Title:
Trajectory Prediction for Nonuniform Geospatial Mobile Device Data

dc.contributor.advisor Davenport, Mark A.
dc.contributor.author Branham, Sara M.
dc.contributor.committeeMember Konda, Roshan
dc.contributor.committeeMember Ahad, Nauman
dc.contributor.department Computer Science
dc.contributor.department Computer Science
dc.date.accessioned 2020-11-09T17:01:45Z
dc.date.available 2020-11-09T17:01:45Z
dc.date.created 2020-08
dc.date.issued 2020-08
dc.date.submitted August 2020
dc.date.updated 2020-11-09T17:01:45Z
dc.description.abstract GPS collection has become increasingly popular with the rise of mobile devices and applications. This data is collected for an incredibly wide range of reasons, including applications like Google Maps providing directions, weather applications providing weather predictions, Uber or Lyft providing transportation, or service providers seeking to better understand their users. While GPS data has many uses in our society, there exist enormous obstacles surrounding long term data collection, namely how to acquire uniformly sampled device data. We propose using Transformers and GRUs with added Attention to extract long term habits for individual users in the context of nonuniform GPS data. These models are traditionally used for Neural Machine Translation (NMT), so they are well equipped for nonuniform problem spaces.
dc.description.degree Undergraduate
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/63904
dc.publisher Georgia Institute of Technology
dc.subject Transformers
dc.subject Trajectory prediction
dc.subject Sparse GPS prediction
dc.subject GRUs
dc.subject Attention
dc.title Trajectory Prediction for Nonuniform Geospatial Mobile Device Data
dc.type Text
dc.type.genre Undergraduate Thesis
dspace.entity.type Publication
local.contributor.advisor Davenport, Mark A.
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Computer Science
local.contributor.corporatename Undergraduate Research Opportunities Program
local.relation.ispartofseries Undergraduate Research Option Theses
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relation.isOrgUnitOfPublication 0db885f5-939b-4de1-807b-f2ec73714200
relation.isSeriesOfPublication e1a827bd-cf25-4b83-ba24-70848b7036ac
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