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- W3129983003 endingPage "103044" @default.
- W3129983003 startingPage "103044" @default.
- W3129983003 abstract "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." @default.
- W3129983003 created "2021-03-01" @default.
- W3129983003 creator A5039809980 @default.
- W3129983003 creator A5044341587 @default.
- W3129983003 creator A5054402843 @default.
- W3129983003 date "2021-04-01" @default.
- W3129983003 modified "2023-10-17" @default.
- W3129983003 title "Transit OD matrix estimation using smartcard data: Recent developments and future research challenges" @default.
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- W3129983003 doi "https://doi.org/10.1016/j.trc.2021.103044" @default.
- W3129983003 hasPublicationYear "2021" @default.
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