Among the major public transportation systems in cities, bus transit has its
problems, including more accuracy and reliability when estimating the bus
arrival time for riders. This can lead to delays and decreased ridership,
especially in cities where public transportation is heavily relied upon. A
common issue is that the arrival times of buses do not match the schedules,
resulting in latency for fixed schedules. According to the study in this paper
on New York City bus data, there is an average delay of around eight minutes or
491 seconds mismatch between the bus arrivals and the actual scheduled time.
This research paper presents a novel AI-based data-driven approach for
estimating the arrival times of buses at each transit point (station). Our
approach is based on a fully connected neural network and can predict the
arrival time collectively across all bus lines in large metropolitan areas. Our
neural-net data-driven approach provides a new way to estimate the arrival time
of the buses, which can lead to a more efficient and smarter way to bring the
bus transit to the general public. Our evaluation of the network bus system
with more than 200 bus lines, and 2 million data points, demonstrates less than
40 seconds of estimated error for arrival times. The inference time per each
validation set data point is less than 0.006 ms