Understanding the dynamics of human daily mobility patterns is essential for the management and planning of urban facilities and services. Travel smart cards, which record users' public transporting histories, capture rich information of users' mobility pattern. This provides the opportunity to discover valuable knowledge from these transaction records. In recent years, research on measuring user similarity for behavior analysis has attracted a lot of attention in applications such as recommendation systems, crowd behavior analysis applications, and numerous data mining tasks. In this paper, our goal is to estimate the similarity between users' travel patterns according to their travel smart card data. The core of our proposal is a novel user similarity measurement, namely, Travel Spatial-Temporal Similarity (TST), which measures the spatial range and temporal similarity between users. Moreover, we also propose a hybrid index structure, which integrates inverted files and cluster-based partitioning, to allow for efficient retrieval of the top-K most similar users. Through experimental evaluation, our proposed approach is shown to deliver scalable performance.