Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context. (26th December 2019)

• Record Type: Journal Article
• Authors: Jin, Canghong; Lin, Zhiwei; Wu, Minghui
• Other Names: Seeling Patrick Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2019(2019)
• Publication Date: 2019-12-26
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2019/2860165
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : 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.