Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

Abstract

Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.

A model for port throughput forecasting using Bayesian estimation

“…Due to the limitations of time series models, recent studies have used soft computing models including artificial neural networks (Gosasang et al 2011), transfer forecasting models (Xiao et al 2014), fuzzy logic, genetic algorithms , artificial bee colony (Gökkuş et al 2017), and ant colony algorithms (Nie and Zhao 2019). These models are used to simulate complex processes where a mathematical description is not performable due to random behavior and nonlinear characteristics of the process (Peng and Chu 2009).…”

“…They include national gross domestic product (GDP), average yearly consumer price index (CPI), world GDP, the volume of national export trade, the volume of national import trade, and the national population. These variables were also used in previous studies (Gökkuş et al 2017;Gosasang et al 2018). Of course, if more macroeconomic variables are available, they could naturally be used in mutual information analysis to discover those that influence port throughput the most.…”

“…Furthermore, the port authority can consider phasing of new development based on the changing demand in the volatile market environment. The results of this short-term forecast facilitate the port's operational decisions (i.e., port capacity utilization, cargo handling, and facilities development plan), resources allocation (Gökkuş et al 2017;Rashed et al 2017), port logistics, and terminal and hinterland connections capacity (Brooks et al 2014).…”

A Comparison of SVM and Traditional Methods for Demand Forecasting in a Seaport: A case study

“…For this purpose statistical forecasting methods, regression analysis and machine learning methods applied for general cargo, container and vehicle handling volumes. For multiple linear regression and SVM methods, gross domestic product (GDP), population, inflation and foreign trade of Turkey were selected as the model input (predictor) variables [1,7]. The monthly values of predictor variables are created by applying cubic spline interpolation to yearly data [12].…”

Empty container movements arising from cargo seasonality: Turkish terminals

“…Service networks of ship operators suffer from cargo seasonality (Polat and Gunther, 2016). But the most popular execution area of TSA in the literature of container transportation is forecasting (Schulze and Prinz, 2009;Ee et al, 2014;Rashed et al, 2016;Gokkus et al, 2017). Forecasting is highly important for terminal operators who plan investment on constructing new terminals or buying new terminal equipment and for ship operators who charter and/or purchase ships and containers and perform scheduling.…”