Managing uncertainty in ferry terminals: a machine learning approach

Iñigo L. Ansorena and César López Ansorena

International Journal of Business Information Systems, 2020, vol. 33, issue 2, 285-297

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. (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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