  In urban settings, bus transit stands as a significant mode of public
transportation, yet faces hurdles in delivering accurate and reliable arrival
times. This discrepancy often culminates in delays and a decline in ridership,
particularly in areas with a heavy reliance on bus transit. A prevalent
challenge is the mismatch between actual bus arrival times and their scheduled
counterparts, leading to disruptions in fixed schedules. Our study, utilizing
New York City bus data, reveals an average delay of approximately eight minutes
between scheduled and actual bus arrival times. This research introduces an
innovative, AI-based, data-driven methodology for predicting bus arrival times
at various transit points (stations), offering a collective prediction for all
bus lines within large metropolitan areas. Through the deployment of a fully
connected neural network, our method elevates the accuracy and efficiency of
public bus transit systems. Our comprehensive evaluation encompasses over 200
bus lines and 2 million data points, showcasing an error margin of under 40
seconds for arrival time estimates. Additionally, the inference time for each
data point in the validation set is recorded at below 0.006 ms, demonstrating
the potential of our Neural-Net-based approach in substantially enhancing the
punctuality and reliability of bus transit systems.
