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This paper contributes to the emerging literature analysing public transit fare card data for a better understanding of passengers’ mobility patterns and path choices. A new heuristic is proposed to estimate the stop-level origins and destinations by detecting the traveller activities in the observed transactions in a fare card dataset. The main focus in this research is estimating the actual passenger trajectories for multi-leg journeys. If the fare card dataset includes both boarding and alighting information of each transaction, the main challenge is the estimation of origins and destinations by distinguishing the transfer interchanges from the activity locations. Built on commonly used criteria for identifying transfers, this paper proposes a new method to improve the accuracy of short activity detection to estimate the passengers’ true origins and destinations. The set of criteria in this research is based on the proposed …