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Journals / Marine Science and Technology Bulletin / 2026 / Volume: 15 Issue: 2

Statistical analysis of vessel waiting times in the Istanbul Strait: Evidence from operational data

Emin Deniz Özkan(Dokuz Eylül University, Maritime Faculty, Department of Marine Transportation Engineering, 35390, İzmir, Türkiye)0000-0001-8489-4707
Pages
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Publication Date
2026-06-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results, the clearest difference among the binary variables was observed for pilotage, with piloted vessels experiencing longer waiting times.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Vessel waiting time is a crucial indicator of operational efficiency in maritime transport.
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Key Findings & Data:This study statistically examined vessel waiting times for transit passage in the Istanbul Strait, one of the world’s busiest and most strategic waterways, using operational records from 2023 and 2024.
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Conclusion & Impact:According to the results, the clearest difference among the binary variables was observed for pilotage, with piloted vessels experiencing longer waiting times.
🔬Design:Modeling & Simulation
📍Scope:2023 Verileri
⚙️Variables:Istanbul Strait · Operational data · Statistical analysis

Abstract

Vessel waiting time is a crucial indicator of operational efficiency in maritime transport. This study statistically examined vessel waiting times for transit passage in the Istanbul Strait, one of the world’s busiest and most strategic waterways, using operational records from 2023 and 2024. After data cleaning, 76,916 transit records were analysed. Since the waiting time distribution was markedly right skewed, the analysis focused on median, interquartile range (IQR), and upper percentiles. Mann-Whitney U and Kruskal-Wallis tests were used for group comparisons, and Spearman’s rank correlation was used to examine the relationship between waiting time and vessel length. A general linear model was also used to evaluate the explanatory variables jointly using log-transformed waiting time. According to the results, the clearest difference among the binary variables was observed for pilotage, with piloted vessels experiencing longer waiting times. Weaker differences were found for tug assistance, dangerous cargo status, and transit direction. Waiting times also differed across vessel type groups and showed a positive but weak relationship with vessel length. In addition, waiting times varied by season, with higher values in winter and spring and lower values in summer. These findings may provide an empirical reference for decision makers and stakeholders in evaluating waiting time patterns and identifying vessel, operational, and seasonal conditions associated with longer waiting times in the Istanbul Strait.

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Cite This Article

Özkan, E. D. (2026). Statistical analysis of vessel waiting times in the Istanbul Strait: Evidence from operational data. Marine Science and Technology Bulletin, Volume: 15 Issue: 2. https://doi.org/10.33714/masteb.1930690

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