  With recent studies related to Neural Networks being used on different
forecasting and time series investigations, this study aims to expand these
contexts to ferry passenger traffic. The primary objective of the study is to
investigate and evaluate an LSTM-based Neural Networks' capability to forecast
ferry passengers of two ports in the Philippines. The proposed model's fitting
and evaluation of the passenger flow forecasting of the two ports is based on
monthly passenger traffic from 2016 to 2022 data that was acquired from the
Philippine Ports Authority (PPA). This work uses Mean Absolute Percentage Error
(MAPE) as its primary metric to evaluate the model's forecasting capability.
The proposed LSTM-based Neural Networks model achieved 72% forecasting accuracy
to the Batangas port ferry passenger data and 74% forecasting accuracy to the
Mindoro port ferry passenger data. Using Keras and Scikit-learn Python
libraries, this work concludes a reasonable forecasting performance of the
presented LSTM model. Aside from these notable findings, this study also
recommends further investigation and studies on employing other statistical,
machine learning, and deep learning methods on forecasting ferry passenger
flows.
