Automated fare collection (AFC) systems have been widely implemented around the world. Their main goal is to gather revenue in a more efficient way. hi addition to this purpose, they have multiple indirect benefits to transport researchers. This rich data is obtained from daily transactions that mainly occur when a passenger boards or alights a public transit vehicle. They have some features that are investigated in recent studies. Considering volume, longitudinal nature, level of details, and accuracy of the AFC data, this data creates new opportunities for researchers to study public transit systems and travel behaviour of passengers in order to develop various applications, such as origin-destination (OD) estimation, travel pattern mining, trip purpose detection, route choice modelling, transit performance indicators, and identifying policy alternatives. This paper reviews the previous studies on the AFC data focusing on studies after 2010. It will also make suggestions for the direction future studies may wish to take.
