Quantitative analysis and forecast of traffic for north Adriatic ports

Osnovna svrha ovog rada je kvantitativna analiza prometa različitih vrsta tereta sjevernojadranskih luka Rijeka, Kopar i Trst te izrada prognoze za pojedine vrste tereta. Navedeno je ostvareno sa primjenom metoda analize vremenskih nizova i regresijske analize. Dana je usporedba prometa za pojedine vrste tereta s osvrtom na dosadašnje kretanje prometa i to kroz analizu prometa tekućeg i suhog tereta, promet kontejnera te putnički promet za navedene luke. Nadalje, odabrane su reprezentativne jednadžbe trenda s kojima je dobivena procijenjena količina prometa pojedinih vrsta tereta u sljedećem petogodišnjem razdoblju. Korelacijska analiza pokazala je postojanje ovisnosti između prometa određenih tereta pojedinih sjevernojadranskih luka. Regresijska analiza na primjeru luke Rijeka, nakon prikupljenih podataka i odabira zavisne i nezavisne varijable, rezultirala je regresijskim modelom za koji je zaključeno da je reprezentativan i pouzdan za daljnje prognoze.The main purpose of this paper is a quantitative analysis of the traffic of different types of cargo in the northern Adriatic ports of Rijeka, Koper and Trieste and then give a forecast for certain types of cargo. This was achieved by applying the methods of time series analysis and regression analysis. A comparison of traffic for certain types of cargo is given with reference to the current movement of traffic through the analysis of liquid and dry cargo traffic, container traffic and passenger traffic for these ports. Furthermore, representative trend equations were selected with which the estimated amount of traffic of certain types of cargo in the next five-year period was obtained. Correlation analysis showed the existence of dependence between the traffic of certain cargoes of individual northern Adriatic ports. Regression analysis on the example of the port of Rijeka, after collecting data and selecting dependent and independent variables, resulted in a regression model that was concluded to be representative and reliable for further forecasts