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 consecutive boarding and alighting. The findings reveal many unanswered critical research questions which need to be addressed for ztOD estimation using smartcard data. The research questions are primarily related to the conversion of stop level OD (stOD) to ztOD, transfer detection, and a few miscellaneous problems.

在公共交通中，智能卡主要用于自动收费，目的是生成大量数据。在过去的二十年中，已经进行了大量研究，以将这种大数据用于从运输计划到实时操作和控制的各种运输应用中。智能卡数据应用程序之一是对公共交通始发地-目的地矩阵（tOD）的估计。本文的主要重点是对tOD估算过程中涉及的基本步骤的最新文献进行批判性分析。这些步骤包括数据清理，未知量估计，传输检测，已开发算法的验证以及最终估计区域级别传输OD（ztOD）的过程。未知数的估计包括乘客登机和下车的信息估计。转移检测算法区分两个连续的登机和下车之间的转移或活动。研究结果揭示了许多尚未解决的关键研究问题，使用智能卡数据进行ztOD评估需要解决这些问题。研究问题主要涉及停止级别OD（stOD）到ztOD的转换，转移检测以及一些其他问题。