Machine Learning Forecasts of Public Transport Demand: A Comparative Analysis of Supervised Algorithms Using Smart Card Data

Abstract

Public transport smart cards are widely used around the world. However, while they provide information about various aspects of passenger behavior, they have not been properly exploited to predict demand. Indeed, traditional methods in economics employ linear unbiased estimators that pay little attention to accuracy, which is the main problem faced by the sector's regulators. This paper reports the application of various supervised machine learning (SML) techniques to smart card data in order to forecast demand, and it compares these outcomes with traditional linear model estimates. We conclude that the forecasts obtained from these algorithms are much more accurate.