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Public transport has become an essential part of urban existence withincreased population densities and environmental awareness. Large quantities ofdata are currently generated, allowing for more robust methods to understandtravel behavior by harvesting smart card usage. However, public transportdatasets suffer from data integrity problems; boarding stop information may bemissing due to imperfect acquirement processes or inadequate reporting. Wedeveloped a supervised machine learning method to impute missing boarding stopsbased on ordinal classification using GTFS timetable, smart card, andgeospatial datasets. A new metric, Pareto Accuracy, is suggested to evaluatealgorithms where classes have an ordinal nature. Results are based on a casestudy in the city of Beer Sheva, Israel, consisting of one month of smart carddata. We show that our proposed method is robust to irregular travelers andsignificantly outperforms well-known imputation methods without the need tomine any additional datasets. Validation of data from another Israeli cityusing transfer learning shows the presented model is general and context-free.The implications for transportation planning and travel behavior research arefurther discussed.

A Supervised Machine Learning Model For Imputing Missing Boarding Stops In Smart Card Data