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