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Forecasting of future intermodal traffic demand is very important for decision making in ports operations management. The use of accurate prediction tools is an issue that awakens a lot of interest among transport researchers. Intermodal freight forecasting plays an important role in ports management and in the planning of the principal port activities. Hence, the study is carried out under the motivation of knowing that modeling the freight transport flows could facilitate the management of the infrastructure and optimize the resources of the ports facilities. The use of advanced models for freight forecasting is essential to improve the port level-service and competitiveness. In this paper, two forecasting-models are presented and compared to predict the freight volume. The models developed and tested are based on Artificial Neural Networks and Support Vector Machines. Both techniques are based in a historical data and these methods forecast the daily weight of the freight with one week in advance. The performance of the models is evaluated on real data from Ro-Ro freight transport in the Port of Algeciras Bay. This work proposes and compares different approaches to determine the best prediction. In order to select the best model a multicomparison procedure is developed using several statistical test. The results of the assessed models show a promising tool to predict Ro-Ro transport flows with accuracy.

Short-term Forecasting of Intermodal Freight Using ANNs and SVR: Case of the Port of Algeciras Bay