Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context
Joint Authors
Jin, Cang-hong
Wu, Ming-hui
Lin, Zhiwei
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-26
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Human trajectory prediction is an essential task for various applications such as travel recommendation, location-sensitive advertisement, and traffic planning.
Most existing approaches are sequential-model based and produce a prediction by mining behavior patterns.
However, the effectiveness of pattern-based methods is not as good as expected in real-life conditions, such as data sparse or data missing.
Moreover, due to the technical limitations of sensors or the traffic situation at the given time, people going to the same place may produce different trajectories.
Even for people traveling along the same route, the observed transit records are not exactly the same.
Therefore trajectories are always diverse, and extracting user intention from trajectories is difficult.
In this paper, we propose an augmented-intention recurrent neural network (AI-RNN) model to predict locations in diverse trajectories.
We first propose three strategies to generate graph structures to demonstrate travel context and then leverage graph convolutional networks to augment user travel intentions under graph view.
Finally, we use gated recurrent units with augmented node vectors to predict human trajectories.
We experiment with two representative real-life datasets and evaluate the performance of the proposed model by comparing its results with those of other state-of-the-art models.
The results demonstrate that the AI-RNN model outperforms other methods in terms of top-k accuracy, especially in scenarios with low similarity.
American Psychological Association (APA)
Jin, Cang-hong& Lin, Zhiwei& Wu, Ming-hui. 2019. Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context. Wireless Communications and Mobile Computing،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1212064
Modern Language Association (MLA)
Jin, Cang-hong…[et al.]. Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context. Wireless Communications and Mobile Computing No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1212064
American Medical Association (AMA)
Jin, Cang-hong& Lin, Zhiwei& Wu, Ming-hui. Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context. Wireless Communications and Mobile Computing. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1212064
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212064