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The ability to accurately predict public transit ridership demand benefitspassengers and transit agencies. Agencies will be able to reallocate buses tohandle under or over-utilized bus routes, improving resource utilization, andpassengers will be able to adjust and plan their schedules to avoid overcrowdedbuses and maintain a certain level of comfort. However, accurately predictingoccupancy is a non-trivial task. Various reasons such as heterogeneity,evolving ridership patterns, exogenous events like weather, and otherstochastic variables, make the task much more challenging. With the progress ofbig data, transit authorities now have access to real-time passenger occupancyinformation 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 thispaper, we propose the use and fusion of data from multiple sources, cleaned,processed, and merged together, for use in training machine learning models topredict 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 forthe trip and stop level prediction. We evaluate our approach on real-worldtransit data provided by the public transit agency of Nashville, TN. Wedemonstrate that the trip level model based on Xgboost and the stop level modelbased on LSTM outperform the baseline statistical model across the entiretransit service day.

On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data