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Transit OD matrix estimation using smartcard data: Recent developments and future research challenges

Highlights•A comprehensive review of recent development on transit OD estimation is presented.•Transit OD matrix estimation process is divided into four components.•The current algorithms and errors associated with them are discussed in detail.•Sketch of solutions of current issues are also included.AbstractIn 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.


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