Jointly Trajectory Representation Learning on Road Network and Semantics using On-road IoT Data
Abstract: Internet of Things (IoT) data provides rich data sources and application scenarios for trajectory representation learning. Trajectory representation learning aims to transform the original trajectory information into a general low-dimensional vector representation for many different downstream tasks (trajectory similarity calculation, anomaly detection, etc.). Current road network-based trajectory learning methods mainly focus on the spatial structure of the road network and often ignore the semantic information and complex feature information embedded in IOT data, in addition, the spatial and semantic properties of trajectories cannot be adequately preserved simultaneously. To this end, we propose a Trajectory Representation Learning framework based on Road network-TRLR. It first uses a graph attention network to learn the topological and semantic properties of road network segments, then it fuses node vectors and segment vectors as representations of node units, this is to support more data input types and enhanced node characteristics. Lastly, it learns the travel semantics of the trajectories through an information-enhanced transformer model, which captures the sequence information in the trajectory and generates the trajectory representation vector. In addition, we also propose four data augmentation methods to ensure that the trajectories can maintain both their spatial and semantic properties. To validate the effectiveness of our modeling approach, we conduct experiments on real datasets for similar trajectory search and mask prediction tasks. The experimental results demonstrate the performance improvement of our model.
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