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ThinkMind // DATA ANALYTICS 2016, The Fifth International Conference on Data Analytics // View article data_analytics_2016_5_30_60094


Datamining and Big Freight Transport Database

Authors:
Massimiliano Petri
Antonio Pratelli
Giovanni Fusco

Keywords: Freight Demand Model; Bayesian Networks; European Freight Corridor; Demand Forecasting

Abstract:
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, important projects 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 described in this article 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 Transport Market study of the Sixth European Rail Freight Corridor.

Pages: 100 to 108

Copyright: Copyright (c) IARIA, 2016

Publication date: October 9, 2016

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-510-4

Location: Venice, Italy

Dates: from October 9, 2016 to October 13, 2016

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