  The ability to accurately predict public transit ridership demand benefits
passengers and transit agencies. Agencies will be able to reallocate buses to
handle under or over-utilized bus routes, improving resource utilization, and
passengers will be able to adjust and plan their schedules to avoid overcrowded
buses and maintain a certain level of comfort. However, accurately predicting
occupancy is a non-trivial task. Various reasons such as heterogeneity,
evolving ridership patterns, exogenous events like weather, and other
stochastic variables, make the task much more challenging. With the progress of
big data, transit authorities now have access to real-time passenger occupancy
information for their vehicles. The amount of data generated is staggering.
While there is no shortage in data, it must still be cleaned, processed,
augmented, and merged before any useful information can be generated. In this
paper, we propose the use and fusion of data from multiple sources, cleaned,
processed, and merged together, for use in training machine learning models to
predict transit ridership. We use data that spans a 2-year period (2020-2022)
incorporating transit, weather, traffic, and calendar data. The resulting data,
which equates to 17 million observations, is used to train separate models for
the trip and stop level prediction. We evaluate our approach on real-world
transit data provided by the public transit agency of Nashville, TN. We
demonstrate that the trip level model based on Xgboost and the stop level model
based on LSTM outperform the baseline statistical model across the entire
transit service day.
