Analysis of port waiting time due to congestion by applying Markov chain analysis

Jeroen Pruijn, Austin Kana, William Groeneveld

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

6 Citations (Scopus)


This chapter discusses the use of Markov chain analysis (MCA) to better understand and probabilistically predict port waiting times due to congestion using historical data. Port congestion makes ships to wait before making use of the port's infrastructure. These congestions are often shortlived and uncertain at the start of a trip. Predicting these waiting times may improve planning and increase the efficiency of the transportation of goods. Analysis of existing port waiting times due to port congestion has been studied in the past using queuing theory. This research investigates using MCA for predicting port waiting time in the bulk shipping industry due to port congestion. Currently, MCA has not been widely used within shipping and shipbuilding. In other industries, MCA is widely applied, for example, in weather models, stock pricing, and modeling maintenance and degradation. The results obtained from this research aim to predict port congestion and finding trends that allow shippers to better predict their services.
Original languageEnglish
Title of host publicationMaritime Supply Chains
EditorsThierry Vanelslander, Christa Sys
ISBN (Print)978-0-12-818421-9
Publication statusPublished - 2020


  • Port congestion
  • Markov chain
  • Dry-bulk transportation
  • Waiting time
  • Short-term predictions


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