When did major public transportation systems first begin using machine learning algorithms to predict passenger churn and optimize retention strategies?

The transportation industry has been transformed by data analytics and machine learning in recent years. Systems that predict passenger flow, optimize routes, and personalize marketing have revolutionized how transit companies operate. But when did transportation companies first begin implementing sophisticated AI-driven customer analytics at scale? Test your knowledge about the evolution of data science in public transportation!

This is a live public trivia poll on AIPolls.Net. Vote and see real-time results.

Late 1990s, with the Bay Area Rapid Transit (BART) system's pioneering PredictFlow program
2008, following Transport for London's implementation of the Oyster card system that generated enough data for ML applications
2013, after the MTA in New York partnered with IBM to develop RiderRetain, the first transport-specific churn prediction model
2017, when Japan Railways deployed the first comprehensive neural network for analyzing passenger behavior patterns