Publication
Title
Prediction of late/early arrivals in container terminals : a qualitative approach
Author
Abstract
Vessel arrival uncertainty in ports has become a very common problem worldwide. Although ship operators have to notify the Estimated Time of Arrival (ETA) at predetermined time intervals, they frequently have to update the latest ETA due to unforeseen circumstances. This causes a series of inconveniences that often impact on the efficiency of terminal operations, especially in the daily planning scenario. Thus, for our study we adopted a machine learning approach in order to provide a qualitative estimate of the vessel delay/advance and to help mitigate the consequences of late/early arrivals in port. Using data on delays/advances at the individual vessel level, a comparative study between two transshipment container terminals is presented and the performance of three algorithmic models is evaluated. Results of the research indicate that when the distribution of the outcome is bimodal the performance of the discrete models is highly relevant for acquiring data characteristics. Therefore, the models are not flexible in representing data when the outcome distribution exhibits unimodal behavior. Moreover, graphical visualisation of the importance-plots made it possible to underline the most significant variables which might explain vessel arrival uncertainty at the two European ports.
Language
English
Source (journal)
European journal of transport and infrastructure research. - Delft
EUROPEAN JOURNAL OF TRANSPORT AND INFRASTRUCTURE RESEARCH
Publication
Delft : 2015
ISSN
1567-7141
Volume/pages
15:4(2015), p. 536-550
ISI
000366901000009
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 16.02.2016
Last edited 09.06.2017
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