Container Terminal Workload Modeling Using Machine Learning Techniques

Üstün Atak*, Tolga Kaya, Yasin Arslanoğlu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


Container terminals are complex facilities which serve to maritime transportation. Effectivity and productivity of the terminals are very crucial for international transportation. Often, terminal managers deal with logistic problems with high costs. Reducing operation cost and time are the key elements for a smooth and secure process. To minimize the port stay time of the vessels, each part of the operation should be optimized. In order to serve container vessels efficiently, the port management should analyze the cargo handling process considering the ship and container characteristics. In this scope, the purpose of this study to model quay crane handling time in traditional container terminals based on port operations data using machine learning techniques. To conduct operational efficiency analysis, we have analyzed container terminal operations using alternative regression models based on operations data of more than 400.000 handling movements in a traditional terminal in Turkey. Results suggest that the efficiency of the terminal can be increased in planned rush hours such as the periods before the mealtime and the vessels’ operation completion.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques
Subtitle of host publicationSmart and Innovative Solutions - Proceedings of the INFUS 2020 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A. Cagri Tolga
Number of pages7
ISBN (Print)9783030511555
Publication statusPublished - 2021
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2020 - Istanbul, Turkey
Duration: 21 Jul 202023 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1197 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2020

Bibliographical note

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Container terminal
  • Machine learning
  • Operational efficiency analysis
  • Quay crane handling
  • Regression


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