Trajectory Prediction for Nonuniform Geospatial Mobile Device Data

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.