Predicting Human Mobility via Attentive Convolutional Network

Predicting human mobility is an important trajectory mining task for various applications, ranging from smart city planning to personalized recommendation system. While most of previous works adopt GPS tracking data to model human mobility, the recent fast-growing geo-tagged social media (GTSM) data brings new opportunities to this task. However, predicting human mobility on GTSM data is not trivial because of three challenges: 1) extreme data sparsity; 2) high order sequential patterns of human mobility and 3) evolving preference of users for tagging. In this paper, we propose ACN, an attentive convolutional network model for predicting human mobility from sparse and complex GTSM data. In ACN, we firstly design a multi-dimension embedding layer which jointly embeds key features (i.e., spatial, temporal and user features) that govern human mobility. Then, we regard the embedded trajectory as an "image" and learn short-term sequential patterns as local features of the image using convolution filters. Instead of directly using convention filters, we design hybrid dilated and separable convolution filters to effectively capture high order sequential patterns from lengthy trajectory. In addition, we propose an attention mechanism which learns the user long-term preference to augment convolutional network for mobility prediction. We conduct extensive experiments on three publicly available GTSM datasets to evaluate the effectiveness of our model. The results demonstrate that ACN consistently outperforms existing state-of-art mobility prediction approaches on a variety of common evaluation metrics.