Forecasting of short-term flow freight congestiona study case of Algeciras Bay Port (Spain)
- Juan Jesús Ruiz-Aguilar 1
- Ignacio Turias 1
- José Antonio Moscoso-López 1
- María Jesús Jiménez-Come 1
- Mar Cerbán 2
- 1 Intelligent Modelling of Systems Research Group, University of Cádiz, Algeciras, Spain
- 2 Research Group Transport and Innovation Economic, University of Cádiz, Algeciras, Spain
ISSN: 0012-7353
Année de publication: 2016
Volumen: 83
Número: 195
Pages: 163-172
Type: Article
D'autres publications dans: DYNA: revista de la Facultad de Minas. Universidad Nacional de Colombia. Sede Medellín
Résumé
The prediction of freight congestion (cargo peaks) is an important tool for decision making and it is this paper’s main object of study. Forecasting freight flows can be a useful tool for the whole logistics chain. In this work, a complete methodology is presented in order to obtain the best model to predict freight congestion situations at ports. The prediction is modeled as a classification problem and different approaches are tested (k-Nearest Neighbors, Bayes classifier and Artificial Neural Networks). A panel of different experts (post–hoc methods of Friedman test) has been developed in order to select the best model. The proposed methodology is applied in the Strait of Gibraltar’s logistics hub with a study case being undertaken in Port of Algeciras Bay. The results obtained reveal the efficiency of the presented models that can be applied to improve daily operations planning.