Title: Managing uncertainty in ferry terminals: a machine learning approach

Authors: Iñigo L. Ansorena; César López Ansorena

Addresses: Technical University of Madrid, C/Profesor Aranguren s/n, Madrid 28040, Spain ' Port Authority of Ceuta, C/Muelle de España s/n, Ceuta 51001, Spain

Abstract: Ferry service across the Gibraltar Strait usually faces with the congestion problem at ferry terminals. Recognising the need to manage this problem, port managers must be prepared in advance to reduce waiting times, give space in the car park, coordinate ferry departures, etc. With this aim, we propose a machine learning methodology based on a classification and regression tree (CART) model. Thus, by means of the CART model, port managers can predict (with a certain error) the number of vehicles (or passengers) that will use the ferry terminal in the future. The accurate prediction that the model provides is crucial not only for port managers, but also for ferry operators. Our CART gives the predicted value and the measure of the expected error. Both are presented in sunburst graphs.

Keywords: classification and regression tree; CART; ferry terminals; decision trees; traffic prediction; passengers; vehicles; port management; classification; regression; machine learning.

DOI: 10.1504/IJBIS.2020.105164

International Journal of Business Information Systems, 2020 Vol.33 No.2, pp.285 - 297

Received: 08 Jan 2018
Accepted: 20 Apr 2018
Published online: 14 Feb 2020 *

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