Model of short-term forecasting liner maritime transport in the port system: A case study for Split City port

Abstract

One of the preconditions for good quality management of seaports is forecasting the traffic according to the number of passengers and the number of vehicles; in this way it is possible to plan and prepare activities for the smooth operation of the ports. This paper researches the port system as part of the coastal liner maritime transport. The set hypothesis is that the model of forecasting the traffic could be presented as a function of two variables. The Principal Component Analysis (PCA) method is used to select the forecasting parameters. Based on the choice of parameters, using the Least Squares Method (LSM), the trend analysis is performed to choose the forecasting functions for maritime liner transport on the example of the Split City port. The statistical analysis on the choosed forecasting model using the coefficient of determination r2 and adjusted r2 model is performed to confirm the choice.