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Smart card transactions capture rich information of human mobility and urban dynamics, therefore are of particular interest to urban planners and location-based service providers. This provides the opportunity to discover valuable knowledge from these transaction records. In recent years, research on measuring user similarity for behaviour 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.

Correlative Users travel Pattern Discovery from Smart card Data