A supervised machine learning model for imputing missing boarding stops in smart card data

Document Type

Journal Article

Publication Date


Subject Area

place - asia, place - urban, technology - ticketing systems, technology - passenger information, technology - intelligent transport systems, technology - geographic information systems, ridership - behaviour


Machine learning, Smart card, Boarding stop imputation, Public transport, Missing data, Pareto accuracy


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. This study introduces 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. The 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. The data validation 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.


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