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The gradual commercialization of entirely autonomous vehicles is expected to bring numerous benefits, such as structural transformation in the industry. Specifically, in maritime transportation, automobile terminals that import and export finished autos are seen to transform their current loading system into a CAV (connected automated vehicle)-loading system to accommodate autonomous vehicles. In this study, the impact of introducing a CAV-loading system to a roll-on, roll-off (RORO) ports was investigated. Simulation models were developed to test the performance of the terminal with the CAV-loading system. Then, a cost model was developed to determine the economic benefits of the CAV-loading system. The results in this study revealed that operating costs were reduced by 90%, while terminal operations were significantly improved. In addition, the study revealed that using the CAV-loading system resulted in a 12% reduction in CO2 emissions compared to that using the current loading system. The originality of this study lies in its transformative potential for an industry that heavily relies on human labor and has limited mechanization and automation. This study provides significant implications for incorporating autonomous vehicles in planned automobile terminal operations. [ABSTRACT FROM AUTHOR]

The goal of this paper is to show that the implementation of the queuing theory could set an optimal capacity of the port container terminal, or a combination of the number of berths and cranes per berth with the minimum costs for the given traffic. The container terminal is a system composed of a specific number of mutually depending subsystems so the optimal capacity of the terminal is reduced to the calculation of the optimal number of berths, since the capacity of the berth determines the necessary capacity of the other subsystems, and thereby a throughput of the entire container terminal. The optimal solution was received by applying a model of total costs that is based on the queuing theory. This model can be used for any container terminal and for terminals in present and future conditions. The results are a basis for bringing adequate business decisions related to the operating of the terminal. The model is tested on an example of container terminal Brajdica in Port of Rijeka. [ABSTRACT FROM AUTHOR]