Forecasting Ferry Passenger Flow Using Long-Short Term Memory Neural Networks

TL;DR

This study applies LSTM neural networks to forecast ferry passenger traffic in the Philippines, demonstrating reasonable accuracy and highlighting the potential of deep learning for transportation demand prediction.

Contribution

It introduces an LSTM-based forecasting model for ferry passenger flow, evaluated on real data from two Philippine ports, expanding neural network applications in maritime transportation.

Findings

Achieved 72% accuracy for Batangas port

Achieved 74% accuracy for Mindoro port

Demonstrated LSTM's effectiveness in ferry passenger forecasting

Abstract

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.