An Approach for the In-Depth Data Analysis of the Marine Traffic of Independent Nearby Ports
y | 09 dic 2023
Publicado en línea: 09 dic 2023
Páginas: 402 - 426
Recibido: 22 abr 2023
Aceptado: 15 sept 2023
DOI: https://doi.org/10.2478/foli-2023-0038
Palabras clave
© 2023 Julian Vasilev et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
Research background
Maritime transport helps the development of the economy of countries. Improving the current situation in this type of transport requires the application of modern software tools for assessment, analysis and forecasting.
Purpose
The aim of this paper is to suggest an approach for an in-depth analysis of marine traffic near to independent ports. This approach is tested and validated for the Varna and Constanta ports for the period 2004–2021. Data from Eurostat are used.
Research methodology
This paper proposes a new methodology for an in-depth analysis and forecasting of marine traffic of independent nearby ports using public data. Correlations, multiple regression, graphical methods, seasonality and trendlines are used to test and validate the proposed methodology.
Results
The results show that the proposed methodology may be applied for other independent ports and periods. The results show some interesting facts about the analyzed ports of Varna and Constanta. Our initial assumptions that these two independent ports have similar seasonality is rejected.
Novelty
The novelty of the paper refers to a new methodology for the in-depth analysis and forecasting of marine traffic of independent nearby ports using public data. Using the methodology in this paper (for an in-depth analysis of marine traffic of independent nearby ports) similar research may be done for other nearby ports and periods. Other research may focus on finding the specific types of cargo for each port influencing the differences in seasonality. Nearby ports with separate management may use the proposed methodology for better cargo planning and investment planning.