Seaport Rijeka is located and connected to the strategic EU TEN-T transport routes (Mediterranean and Baltic–Adriatic Corridor). Seaport Rijeka represents the shortest connection between Central and Central-Eastern Europe, and overseas destinations, by land and sea, and is in an excellent position to take advantage of its location. Being the largest and busiest seaport in Croatia, with constant increase in cargo traffic, especially container traffic, with inadequate and incomplete transport infrastructure that creates congestion, Seaport Rijeka will soon reach its capacity limits. One of the possible solutions that would satisfy the increasing demand and mitigate existing problems is establishing a dry port. Establishing a dry port serving Seaport Rijeka on the EU transport routes would greatly contribute to the strategic and operational plans of the EU and Croatia. The focus of this paper is to determine the optimal dry port location for Seaport Rijeka. The AHP methodology was used to determine the optimal dry port location of the Seaport Rijeka, by analyzing a large set of influential factors. The analysis was performed for three groups of possible dry port locations (close, medium distance and distant). Results suggest that optimal dry port locations for Seaport Rijeka are in Miklavlje, Velika Gorica and Vinkovci.

A predictive safety management methodology implies steps and tools of predictive safety management in aviation, i.e., use of predictive (forecasting) and causal modeling methods to identify potential and possible hazards in the future, as well as their causal factors which can help define timely and efficient mitigation measures to prevent or restrain emerging hazards turning into adverse events. The focus of this paper is to show how predictive analysis of an organization’s safety performance can be conducted, on the sample airport. A case study regarding implementation of predictive analysis of an organization’s safety performance, was performed at Split Airport. The predictive analysis of an airport’s safety performance was conducted through the analysis of Split Airport safety database, causal modeling of Split Airport organizational and safety performance indicators, outlier root cause analysis of Split Airport safety performance indicators, predictive analysis of safety performance (forecasting of Split Airport organizational and safety performance indicators), and scenario cases that simulate future behavior of Split Airport safety performance indicators. Based on detected future hazards, and their causal factors, the appropriate mitigation measures are proposed for the purpose of improving and maintaining an acceptable level of safety at the airport.

Due to the continuous growth of air traffic and the development of aviation systems, the current safety management methodologies should be improved and upgraded. Safety management systems help aviation organizations to manage, maintain and increase safety efficiently. The focus of the research is on the development of the predictive safety management methodology to upgrade current reactive and proactive safety management methodologies and to improve the overall safety level in aviation organizations. Predictive methods are used in various aviation sectors (air navigation services, airport operations, airline operations) for planning purposes but not in the segment of safety management. Available examples of predictive methods were tested and analyzed. Time series decomposition methods were selected as most suited for implementation in aviation safety management. The paper explicitly emphasizes correlations between safety management methodologies in the sample aviation organization. The paper also shows how causal links among organizational and safety performance indicators can be detected, by developing causal models of mutual influences using causal modeling methods, on the sample organization. This research defined steps and tools of the conceptual model of predictive safety management methodology, which enables an organization to identify and mitigate future adverse events.

While airlines can directly quantify carbon emissions based on flight-specific fuel burn data, such data, along with data on other gaseous emissions that do not scale linearly with fuel consumption, are often unavailable to external stakeholders, necessitating the reliance on estimation models. Emissions are thus approximated from known quantities, with most usually from the fuel burned and distance travelled. Emission approximators developed for the aviation industry thus involve some degree of approximation and assumptions, as well as different exogenous and endogenous factors. As a result, such solutions differ primarily due to the significant methodological variations they incorporate. This paper assesses carbon emission approximators developed to valorize emissions generated by flight operations. It reveals the significance and sources of the misestimation of emissions by focusing on the ICAO Carbon Emission Calculator (ICEC), ICAO CORSIA CO₂ Estimation and Reporting Tool (CERT) and EUROCONTROL’ Advanced Emission Model (AEM) and Small Emitters Tool (SET). Thereby, the main research findings indicate considerable estimation uncertainty among the reviewed solutions, ranging from 1.77% to 27.95% on average compared to the baseline, which translates to statistical confidence levels ranging from 15% to 77.50% on average with respect to a 95% confidence threshold.

Conducting flight operations at the pace of air traffic relies on shift work, overtime work, work at night, work in different and numerous time zones, and unbalanced flight crew schedules. Such working hours and workload settings can cause disturbances of the circadian rhythm and sleep disorders among flight crew members; this can result in fatigue and can have an impact on the safety of flight operations. Fatigue impacts many cognitive abilities such as vigilance, memory, spatial orientation, learning, problem solving, and decision making. In aviation, fatigue has been identified as a hazard to the safety of flight operations. This paper describes objectivation methods for data collecting processes regarding flight crew fatigue, using an electronic system of standardized chronometric cognitive tests and subjective self-assessment surveys on the subjective perception of fatigue. The data collected were analyzed using statistical methods to identify and quantify elements that affect the appearance of fatigue. Finally, causal modeling methods were used to determine correlations among the measured flight crew fatigue indicators, the subjective perception of fatigue, and the defined workload settings. The results of this research reveal which elements strongly impact flight crew fatigue. The detected correlations can help define improved measures for the mitigation of fatigue risk in future flight operations.