TY - GEN
T1 - Predicting Next Whereabouts Using Deep Learning
AU - Galarreta, Ana Paula
AU - Alatrista-Salas, Hugo
AU - Nunez-del-Prado, Miguel
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Neural Network
KW - Node prediction
KW - Self-attention
KW - Trajectory prediction
KW - Transformers
UR - http://www.scopus.com/inward/record.url?scp=85161240287&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-33498-6_15
DO - 10.1007/978-3-031-33498-6_15
M3 - Conference contribution
AN - SCOPUS:85161240287
SN - 9783031334979
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 214
EP - 225
BT - Modeling Decisions for Artificial Intelligence - 20th International Conference, MDAI 2023, Proceedings
A2 - Torra, Vicenç
A2 - Narukawa, Yasuo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023
Y2 - 19 June 2023 through 22 June 2023
ER -