ConDTC: Contrastive Deep Trajectory Clustering for Fine-Grained Mobility Pattern Mining

Abstract

Trajectory clustering is a cornerstone task in the field of trajectory mining. With the proliferation of deep learning, deep trajectory clustering has been widely researched to mine mobility patterns from massive unlabeled trajectories. Nevertheless, existing methods mostly ignore trajectories’ temporal regularities, which are essential for mining fine-grained mobility patterns for applications including traveling group identification, transportation mode discovering, social security emergency, etc. To fill this gap, we propose ConDTC, a contrastive deep trajectory clustering method targeting for fine-grained mobility pattern mining. Specifically, we first design a spatial-temporal trajectory representation learning method which can capture both spatial and temporal regularities of trajectories synchronously. The proposed trajectory representation model can be used as a pre-trained model to serve various downstream trajectory mining tasks. Then, we construct a contrastive trajectory clustering module which optimizes trajectory representations and clustering performance simultaneously. Experimental results on three datasets validate that ConDTC can identify fine-grained mobility patterns by clustering trajectories with similar spatial-temporal mobility patterns together while separating those with different mobility patterns apart. Actually, ConDTC outperforms all state-of-the-art competitors substantially in terms of effectiveness, efficiency and robustness.