Abstract
Transport modeling, in general, and freight transport modeling, in particular, are becoming important tools for investigating the effects of investments and policies. Freight demand forecasting models are still in an experimentation and evolution stage. Nevertheless, some recent European projects, like Transtools or ETIS/ETIS Plus, have developed a unique modeling and data framework for freight forecast at large scale so to avoid data availability and modeling problems.
Despite this, projects that had multi-million Euros funding, using these modeling frameworks, have provided very different results for the same forecasting areas and years, giving rise to serious doubts about the results quality, especially in relation to their cost and development time. Moreover, many of these models are purely deterministic.
The project here developed tries to overcome the above-mentioned problems with a new easy-to-implement freight demand forecasting method based on Bayesian Networks using European official and available data. The method is applied to the Sixth European Freight Corridor.
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Petri, M., Fusco, G., Pratelli, A. (2014). A New Data-Driven Approach to Forecast Freight Transport Demand. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8582. Springer, Cham. https://doi.org/10.1007/978-3-319-09147-1_29
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DOI: https://doi.org/10.1007/978-3-319-09147-1_29
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