  Public transportation has been essential in people's lives in recent years.
Bus ridership is a factor in people's choice to board the bus. Therefore, from
the perspective of improving service quality, it is important to inform
passengers who have not boarded the bus yet about future bus ridership.
However, there is a concern that providing inaccurate information may cause a
negative experience. Against this backdrop, there is a need to provide bus
passengers who have not boarded yet with highly accurate predictions. Many
researchers are working on studies on this. However, two issues summarize
related studies. The first is that the correlation of bus ridership between
consecutive bus stops should be considered for the prediction. The second is
that the prediction has yet to be made using all of the features shown to be
useful in each related study. This study proposes a prediction method that
addresses both of these issues. We solve the first issue by designing an
LSTM-based architecture for each bus stop and a single model for the entire bus
stop. We solve the second issue by inputting all useful data, the past bus
ridership, day of the week, time section, weather, and precipitation, as
features. Bus ridership at each bus stop collected from buses operated by
Minato Kanko Bus Inc, in Kobe city, Hyogo, Japan, from October 1, 2021, to
September 30, 2022, were used to compare accuracy. The proposed method improved
RMSE by 23% on average and up to 27% compared to existing methods.
