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

arXiv:2405.02098v1 Announce Type: new
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 …

abstract arxiv capability cs.lg expand flow forecast forecasting investigations lstm memory networks neural networks series studies study time series traffic type