1. bookVolume 22 (2022): Edizione 2 (June 2022)
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License
Formato
Rivista
eISSN
1314-4081
Prima pubblicazione
13 Mar 2012
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese
access type Accesso libero

An Augmented UCAL Model for Predicting Trajectory and Location

Pubblicato online: 23 Jun 2022
Volume & Edizione: Volume 22 (2022) - Edizione 2 (June 2022)
Pagine: 114 - 124
Ricevuto: 24 Jan 2022
Accettato: 29 Mar 2022
Dettagli della rivista
License
Formato
Rivista
eISSN
1314-4081
Prima pubblicazione
13 Mar 2012
Frequenza di pubblicazione
4 volte all'anno
Lingue
Inglese
Abstract

Predicting human mobility between locations plays an important role in a wide range of applications and services such as transportation, economics, sociology and other fields. Mobility prediction can be implemented through various machine learning algorithms that can predict the future trajectory of a user relying on the current trajectory and time, learning from historical sequences of locations previously visited by the user. But, it is not easy to capture complex patterns from the long historical sequences of locations. Inspired by the methods of the Convolutional Neural Network (CNN), we propose an augmented Union ConvAttention-LSTM (UCAL) model. The UCAL consists of the 1D CNN that allows capturing locations from historical trajectories and the augmented proposed model that contains an Attention technique with a Long Short-Term Memory (LSTM) in order to capture patterns from current trajectories. The experimental results prove the effectiveness of our proposed methodology that outperforms the existing models.

Keywords

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