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In public transport, smartcards are primarily used for automatic fare collection purpose, which in turn generate massive data. During the last two decades, a tremendous amount of research has been done to employ this big data for various transport applications from transit planning to real-time operation and control. One of the smart card data applications is the estimation of the public transit origin–destination matrix (tOD). The primary focus of this article is to critically analyse the current literature on essential steps involved in the tOD estimation process. The steps include processes of data cleansing, estimation of unknowns, transfer detection, validation of developed algorithms, and ultimately estimation of zone level transit OD (ztOD). Estimation of unknowns includes boarding and alighting information estimation of passengers. Transfer detection algorithms distinguish between a transfer or an activity between two …