SimPRL: A Simple Contrastive Learning for Path Representation Learning by Joint GPS Trajectories and Road Paths

Path representations are widely applied in smart-city tasks, such as travel time estimation, road classification, and trajectory search. Existing approaches effectively learn these representations by jointly utilizing multi-mode data, including GPS trajectories, road paths, and road networks. Although these methods achieve state-of-the-art performance, the theoretical understanding of the importance and effectiveness of key designs in path representation pre-training remains unclear. Moreover, these methods often rely on hard-constrained labeled data and manual design, resulting in limited performance gains and high annotation costs. In this paper, we propose SimPRL, a simple yet powerful contrastive learning framework that capitalizes on unannotated data. Building on the exploration of key factors contributing to successful path representation learning, SimPRL integrates unannotated GPS trajectories and road paths directly through contrastive self-supervised learning. Specifically, we introduce a pre-training task that predicts correspondences between trajectories from different modes using a shared encoder, without requiring labeled data. We also design an auxiliary segment imputation task and a minimal trajectory sampling technique with random offsets for data augmentation. Additionally, we employ any trajectory within the mini-batch, except for the anchor itself, as the negative sample. Extensive experiments on two large-scale, real-world datasets from Chengdu and Porto demonstrate the effectiveness of SimPRL in two downstream tasks: travel time estimation and road classification. The results show that SimPRL not only surpasses state-of-the-art methods but also achieves more stable generalization and greater efficiency in terms of runtime and memory usage.