STORM: Exploiting Spatiotemporal Continuity for Trajectory Similarity Learning in Road Networks

Trajectory similarity in road networks is pivotal for numerous applications in transportation, urban planning, and ridesharing. However, due to the varying lengths of trajectories, employing similarity metrics directly on raw trajectory data (e.g., DTW [1]) becomes impractical at scale. Therefore, current research primarily revolves around applying deep learning to embed trajectories into vector representations, i.e., embeddings, enabling the application of simpler (and indexable) similarity metrics such as Euclidean distance. Existing research either involves embedding trajectories independent of the downstream tasks, or tailors the embedding specifically for a designated similarity metric. While the former offers versatility and allows for easy fine-tuning to accommodate various metrics, the latter typically yields more effective results but necessitates reconfiguration for different, yet similar metrics. Moreover, both approaches neglect the intrinsic spatiotemporal continuity in trajectory data, resulting in suboptimal trajectory modeling. Our objective is to address the limitations in modeling and have the best of the two worlds. Initially, we generate an embedding through pre-training, decoupled from any particular similarity metric. Subsequently, through a meticulous yet less complex fine-tuning process, we enhance the embedding to encapsulate the nuances of a designated similarity metric. Moreover, a significant aspect of our approach lies in our trajectory modeling that captures spatiotemporal continuity, which mainly consists of a trajectory-oriented road segment embedding and a Transformer encoder enhanced by spatiotemporal semantics inherent in road network-constrained trajectories. Our experimental results demonstrate the superiority of our approach in approximating multiple trajectory similarity metrics over existing state-of-the-art models from both categories of approaches.