Predicting Next Whereabouts Using Deep Learning

Ana Paula Galarreta, Hugo Alatrista-Salas, Miguel Nunez-del-Prado

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Trajectory prediction is a key task in the study of human mobility. This task can be done by considering a sequence of GPS locations and using different mechanisms to predict the following point that will be visited. The trajectory prediction is usually performed using methods like Markov Chains or architectures that rely on Recurrent Neural Networks (RNN). However, the use of Transformers neural networks has lately been adopted for sequential prediction tasks because of the increased efficiency achieved in training. In this paper, we propose AP-Traj (Attention and Possible directions for TRAJectory), which predicts a user’s next location based on the self-attention mechanism of the transformers encoding and a directed graph representing the road segments of the area visited. Our method achieves results comparable to the state-of-the-art model for this task but is up to 10 times faster.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 20th International Conference, MDAI 2023, Proceedings
EditorsVicenç Torra, Yasuo Narukawa
PublisherSpringer Science and Business Media Deutschland GmbH
Pages214-225
Number of pages12
ISBN (Print)9783031334979
DOIs
StatePublished - 2023
Event20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023 - Umeå, Sweden
Duration: 19 Jun 202322 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13890 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023
Country/TerritorySweden
CityUmeå
Period19/06/2322/06/23

Keywords

  • Neural Network
  • Node prediction
  • Self-attention
  • Trajectory prediction
  • Transformers

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