Machine learning for public transport demand forecasting: methods, data and deployment challenges

Machine learning has become the dominant approach to public transport demand forecasting, yet the literature remains scattered across transport and computing communities. This narrative review synthesizes studies published between 2013 and 2025 on the methods, data sources and deployment challenges of machine learning for transit demand prediction. Searches were run in Scopus, OpenAlex and Crossref, and the synthesis was organized around four thematic axes. The evidence shows a progression from statistical time series models to recurrent, convolutional, graph and transformer architectures, with hybrid and ensemble models generally reporting the lowest errors. Smart card and automatic fare collection records remain the principal data source, complemented by weather, calendar and built environment variables and by emerging sources such as trip planners, Wi-Fi and video. Reported performance is difficult to compare because datasets, horizons and metrics differ across studies, and uncertainty quantification is still rare. The main deployment constraints are real time operation, generalization to new stations and networks, interpretability and model maintenance. Closing these gaps will require shared benchmarks, probabilistic outputs and validation with transit agencies. The review identifies a research agenda connecting accuracy, uncertainty and operational usability.