A machine learning approach to predict timely ferry services using integrated meteorological datasets

Abstract

Ferry services that connect a huge number of islands and mainlands are vital transportation methods in several nations. However, a major disadvantage of ferry services is that they are crucially affected by weather conditions. Informing customers about regular ferry service operations is thus very important. With this in mind, the aim of this study was to predict whether ferry services can be provided in a timely manner through machine learning approaches with meteorological (6–48 h prior) and operation data sets. It was found that the random forest classifier achieved accuracy levels of 90.50% (6 h prior) and 88.78% (48 h prior) in predicting ferry services, which were greater than regulation-oriented determination. Both implications and limitations are presented based on the findings of this study.