A Supervised Machine Learning Model For Imputing Missing Boarding Stops In Smart Card Data
This addresses data integrity issues in transportation planning and travel behavior research, but it is incremental as it applies known machine learning techniques to a specific domain problem.
The paper tackles the problem of missing boarding stops in public transport smart card data by developing a supervised machine learning method for imputation, showing it significantly outperforms existing methods and is generalizable to other cities.
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.