With the increase in population densities and environmental awareness, public transport has become an important aspect of urban life. Consequently, large quantities of transportation data are generated, and mining data from smart card use has become a standardized method to understand the travel habits of passengers. Increase in available data and computation power demands more sophisticated methods to analyze big data. Public transport datasets, however, often lack data integrity. Boarding stop information may be missing either due to imperfect acquirement processes or inadequate reporting. As a result, large quantities of observations and even complete sections of cities might be absent from the smart card database. We have developed a machine (supervised) learning method to impute missing boarding stops based on ordinal classification. In addition, we present a new metric, Pareto Accuracy, to evaluate algorithms where classes have an ordinal nature. Results are based on a case study in the city of Beer Sheva utilizing one month of data. We show that our proposed method significantly outperforms schedule-based imputation methods and can improve the accuracy and usefulness of large-scale transportation data. The implications for data imputation of smart card information is further discussed.