TransGTE: a transformer-based model with geographical trajectory embedding for the individual trip destination prediction

• Propose a transformer-based deep learning model to predict the individual trip destination location • Propose a context-aware trajectory embedding model to dynamically fuse geographical and sequential features • Conducted comprehensive experiments for the model validation using the four real-world datasets from different cities Destination prediction is an essential problem for many location-based applications and services. Although previous works partly solved the sparsity of GPS location data by methods such as discretization and embedding, the problem of properly extracting and utilizing geographical information of trajectories is still unsolved. The paper proposes the TransGTE model, a Transformer-based framework with a novel geographical embedding and fusion mechanism, to adaptively extract and fuse geographical features with trajectories’ sequential patterns. TransGTE uses the Graph Convolutional Network (GCN) and Transformer to extract geographical and sequential features and adopts a dynamic gating mechanism to control the weights of sequential and geographical information adaptively. We perform extensive experiments on four taxi trajectory real-world datasets from Porto, Chengdu, Shenzhen and San Francisco, where the TransGTE averagely outperform the best benchmark models by 4.24%, 2.87%, 5.91% and 4.11% in terms of the Mean Haversine Distance Error. The ablation study validates the effectiveness of the proposed trajectory location representation and dynamic gating mechanism modules used to embed taxi GPS trajectories. Finally, we compare the proposed trajectory embedding with the commonly used transformer-based model, and it highlights the effectiveness of the proposed embedding approach in representing geographical similarities between trajectories. The code for this paper is available at: https://github.com/qzl408011458/TransGTE.