A model for port throughput forecasting using Bayesian estimation

Abstract

Capacity plays a crucial role in a port’s competitive position and the growth of its market share. An investment decision to provide new port capacity should be supported by a growing demand for port services. However, port demand is volatile and uncertain in an increasingly competitive market environment. Also, forecasting models themselves are associated with epistemic uncertainty due to model and parameter uncertainties. This paper applies a Bayesian statistical method to forecast the annual throughput of the multipurpose Port of Isafjordur in Iceland. Model uncertainties are thus taken into account, while parameter uncertainties are handled by selecting influencing macroeconomic variables based on mutual information analysis. The presented model has an adaptive capability as new information becomes available. Our method results in a range of port throughput forecasts, in addition to a point estimate, and it also accounts for epistemic uncertainty, thus increasing the reliability of forecasts. Our results provide support for informed decision-making in capacity planning and management. Our forecasts show a constant linear growth of containerized throughput the period 2020–2025. Noncontainerized throughput declines rapidly over the same period.