Geo-aware graph-augmented self-attention network for individual mobility prediction

Even though some studies have found encouraging results, the sparsity of data and the complexity of mobility patterns remain significant challenges in predicting individual mobility. In order to effectively address the two challenges, we propose a novel framework called Geo-aware Graph-Augmented Self-Attention Network (GaGASAN). In GaGASAN, we construct a heterogeneous location graph consisting of a geospatial subgraph and a location transition subgraph to simultaneously model the impacts of geospatial distance and location transitions on all users. With the heterogeneous location graph, the impacts of geospatial distances and global location transitions of all users can be effectively merged, thereby mitigating the sparsity of individual mobility data. For the complex and variable mobility patterns of individuals, we employ a multi-scale time encoding technique and a self-attention mechanism to model different temporal patterns and capture long- and shortrange contexts of sequence transitions. Extensive experiments shows that GaGASAN significantly outperforms eight baseline methods on all metrics. Our implementation code is available at https://github.com/mrcwyh/GaGASAN.