ARMA Model-Based Prediction of the Number of Vessels Navigating the Istanbul Strait Unassisted by Maritime Pilots

Authors

  • Pelin Bolat Istanbul Technical University, Maritime Faculty, Istanbul, Turkey
  • Gizem Kayisoglu Istanbul Technical University, Maritime Faculty, Istanbul, Turkey

DOI:

https://doi.org/10.7225/toms.v10.n01.001

Keywords:

Istanbul strait, ARIMA, Forecasting, Time series, VTS

Abstract

The Istanbul Strait is one of the busiest and riskiest trade routes, with the annual traffic of 50,000 ships. Such high traffic density is managed by the enforcement of a passage regimen by the Vessel Traffic Service (VTS) and maritime pilots of the Directorate General of Coastal Safety of the Republic of Turkey. VTS operations and maritime pilot actions are assumed to complement each other. Accordingly, a vessel unaccompanied by a maritime pilot is expected to interact with the VTS to a greater extent than a vessel assisted by a maritime pilot. Thus, estimating the number of ships that pass through the Istanbul Strait, especially those that do not use maritime pilot assistance, will be an effective tool for the Istanbul Strait traffic scheme management, as it will allow the authorities to balance and integrate VTS and maritime pilot operations. The predictive model based on Autoregressive Moving Average (ARMA) described in this paper has been developed to estimate the number of ships that navigate through the Istanbul Strait without pilot assistance. The best ARMA model was identified through the use of historical data on 100-150 meter and 150-200-meter-long ships that passed through the Istanbul Strait unaccompanied by pilots in 2012-2019. The ARMA model obtained has also been validated through the comparison of real and estimated data.

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Published

2021-04-20

How to Cite

Bolat, P. and Kayisoglu, G. (2021) “ARMA Model-Based Prediction of the Number of Vessels Navigating the Istanbul Strait Unassisted by Maritime Pilots”, Transactions on Maritime Science. Split, Croatia, 10(1), pp. 6–19. doi: 10.7225/toms.v10.n01.001.

Issue

Section

Regular Paper
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