Increasing interest on intermodal transport both by the shippers and carriers has led logistics service providers and transport operators to develop new intermodal transport

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Seaborne transport forecasting has attracted substantial interest over the years because of providing a useful policy tool for decision-makers. Although various forecasting methods have been widely studied, there is still broad debate on accurate forecasting models and preprocessing. The current paper aims to point out these issues, as well as to establish the forecasting model of the domestic cargo volumes using SARIMAX, MLP, LSTM and NARX and SARIMAX-ANN hybrid models. Based on the domestic cargo volumes of Turkey, findings suggest that SARIMA-MLP models can be considered as an appropriate alternative, at least for time series forecasting of shipping. Pre-processed data provides a significant improvement over those obtained with unpreprocessed data, with the accuracy of the models found to be significantly boosted with the Fourier term of decomposition. The results indicate that SARIMAX-MLP, with a mean absolute percentage error (MAPE) of 4.81, outperforms the closest models of SARIMAX, with a MAPE of 6.14 and LSTM with Fourier decomposition with a MAPE of 6.52. Findings have implications for shipping policymakers to plan infrastructure development, and useful for shipowners in accurately formulating shipping demand.

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Domestic dry cargo shipping is a dynamic market influenced by many variables. When shipping demand and shipping demand forecast studies are reviewed, it is seen that macroeconomic factors are prominent. Therefore, this research aims to explain the determinant variables of domestic dry cargo shipping demand forecast in terms of industrial marketing. In order to determine these factors, face-to-face semi-structured interviews have been conducted with a total of 17 domestic maritime dry cargo shipping experts. The findings of the study have been examined under es These findings have enabled a unique contribution toward finding out industry specific variables and dynamics.