A two-stage forecasting approach for short-term intermodal freight prediction

The forecasting of the freight transportation, especially the short-term case, is an important topic in the daily supply chain management. Intermodal freight transportation is subject to multiple complex calendar effects arising in the port environment. The use of prediction methods provides information that may be helpful as a decision-making tool in the management and planning of operations processes in ports. This work addresses the forecasting problem on a daily basis by a novel two-stage scheme combination to offer reliable predictions of fresh freight weight on Ro-Ro (roll-on/roll-off) transport for 7 and 14 days ahead. The study compares daily forecasting with a weekly forecasting approach. The applies database preprocessing and Bayesian regularization neural networks (BRNN) in Stage I. In Stage II, an ensemble framework of the best BRNN models is used to enhance the Stage I forecasting. The results show that the models assessed are a promising tool to predict freight time series for Ro-Ro transport.

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