No abstract

A combination of linear and non-linear models results in a more accurate prediction in comparison with using linear or non-linear models individually to forecast time series data. This paper utilizes the linear autoregressive integrated moving average (ARIMA) model and non-linear artificial neural network (ANN) model to develop a new hybrid ARIMA-ANN model for prediction of container vessel traffic volume. The suggested hybrid method consists of an optimized feed-forward, back-propagation model with a hybrid training algorithm. The database of monthly traffic of Rajaee Port for thirteen years from 2005-2018 is taken into account. The performance of the developed model in forecasting short-term traffic volume is evaluated using various performance criteria such as correlation coefficient (R), mean absolute deviation (MAD), mean squared error (MSE) and mean absolute percentage error (MAPE). The developed model provides useful insights into container traffic behavior. Comparing the results with the real data-sets demonstrates the superior performance of the hybrid models than using models individually in forecasting traffic data.

Long-term insight into maritime traffic is critical for port authorities, logistics companies, and port operators to proactively formulate suitable policies, develop strategic plans, allocate budget, and preserve and improve competitiveness. Forecasting freight rate is a spotlight in port traffic literature, but relatively little research has been directed at forecasting long-term vessel traffic trends. Based on forecast long-term freight rate input provided by the recent 10-year strategic planning of the port of Rajaee, the largest port of Iran, the paper implements seasonal autoregressive integrated moving average (SARIMA) and neural network (NN) models to forecast its container vessel traffic between 2020 and 2025. A database consisting of monthly container traffic data for this port from 1999 to 2019 is utilized. The comparison between the two forecasting models is fulfilled by benchmarking the naïve method. The results reveal the superiority of the NN model over SARIMA in this practice. Considering NN model outputs, the port should expect a significant increase in Panamax and Over-Panamax vessels in the future, and, if not timely addressed, this would result in a systemic queue in the port of Rajaee. That said, the approach can be implemented in port planning and design to avoid under- or over-estimations in such capital-intensive projects.