Assessment of airside aerodrome infrastructure by SAW method with weights from Shannon's interval entropy

Multi-criteria decision support (MCDM) methods are widely used in many areas of science. This applies to economic, social and technical sciences. Implementing activities at the strategic, tactical or operational level requires appropriate tools to support decision-makers. The use of these tools requires the preparation of a decision model along with the formalization of the goal and the acquisition and preparation of data to make the decision accurate. Due to the wide application of MCDM in engineering practice, the article presents their application in air transport. It is an area that is constantly evolving, and all decisions at the strategic level have long-term effects and must be adequately justified. In the paper a compartmental extension of the classical SAW method with weights obtained using the compartmental Shannon entropy was proposed. This paper presents issues concerning the choice of airport layout and describes the problems that occur in determining the cost and capacity of airports. This paper reviews the literature on airport capacity and operations and airside air transport processes and the application of various multi-criteria decision support methods to airport problems. The main part of the article contains an optimization mathematical model aimed at determining the parameters of the elements comprising the airport, on the basis of which a simulation model was developed and a modified method of multi-criteria evaluation of SAW taking into account the interval numbers was presented, in which the set of weights was estimated by the Shannon entropy method. In the application part for 3 variants of the airport arrangement, the parameters were determined in the form of interval numbers and then evaluated using the presented method. The presented numerical example shows that the proposed method is an excellent tool to assist in solving complex decision problems where the data are imprecise and represented by interval numbers.

The impact of seasonality on efficient airport capacity utilization

The major cause of under-capacity or overcapacity at smaller airports is seasonality. Such airports are finding it difficult to determine the capacity to meet the demand and adequately handle passengers in both high and low season. If the capacity is not optimally defined, excessive congestions and waiting times occur, resulting in lower service quality. Airports greatly benefit from capacity utilization analysis in terms of more accurate planning, designing, and adjusting capacity to the current demand in order to encourage further development as well as to reduce additional costs. Using queuing theory, this paper aims to answer the following question: is the passenger capacity at Rijeka International Airport (Croatia) optimally determined to meet the demand promptly, both in high and low season, without causing excessive congestions and waiting times. The results obtained indicate the occurrence of overcapacity since high season demand can be well served, even with reduced capacity used in the low season when demand is significantly lower.

Airport Passenger Flow

Airports are essential for intercontinental trade and a key to the success of the global aviation industry. The common objective of all stakeholders in the airport chain is to satisfy the passenger while facing a significant number of challenges, including the continued growth of air traffic and its influence on airport capacity, the problem of congestion in peak hours, and all that result from queuing and boarding delays. This situation forces the managers of the airport to set up a better circuit of passengers, both at departure and on arrival. In fact, to achieve a better flow of passenger within the terminal, modeling is the first step towards developing and solving this problem. The present work aims to present the various models and approaches of simulation and optimization applied to the study of passenger flows and highlight their contributions to the good management and operation in a field as complex as that of airport operations.

Airspace Capacity Assessment with Augmented Cell Transmission Model

Air traffic congestion is caused by the unbalance between increasing traffic demand and saturating capacity. Flight delay not only causes huge economical lost, but also has very negative environmental impact in the whole air transportation system. In order to identify the impact of extended TMA on airport capacity, an airspace capacity assessment method based on augmented cell transmission model was proposed. Firstly, the airspace structure was modeled with points, segments, layers, and cells. Secondly, mixed integer linear programming model was built up with maximum throughput or capacity as the objective function. Finally, genetic algorithm was used to find the optimal result, and the results were validated by comparing with the fast-time simulation results generated by total airspace and airport modeler (TAAM) software. It is found that the proposed method could achieve a relatively accurate result in a much affordable and fast way. The numerical results could be very helpful for air traffic controllers to analyze the dynamic traffic flow entering and exiting TMA, so as to make decisions via reasonable analysis and do planning in advance by referring to the airport capacity.

Post-COVID-19 Scenarios of Global Airline Traffic until 2040 That Reflect Airport Capacity Constraints and Mitigation Strategies

Although there has been an unprecedented decline in traffic volume due to the COVID-19 crisis, robust growth in global demand for air transport services in the past means that air traffic is expected to recover in the long term. While capacity constraints are currently not a major topic at airports due to the extremely low levels of traffic, there is growing evidence to suggest that important nodes of the worldwide airport network will struggle to deal with capacity constraints after the recovery. The objectives of this research were therefore as follows: to elaborate long-term global passenger and flight volume scenarios in a post-COVID-19 world; to conduct an empirical and model-based analysis of the impact of limited airport capacity on the future development of air traffic in these scenarios; and to derive general strategies for mitigating capacity constraints at certain international airports. Thus, the main aim of this paper is to present a model-based scenario analysis of the long-term impact of the COVID-19 crisis on the capacity situation for airports. Our results indicate that once the pandemic is over, the capacity crunch will remain on the airports’ agenda for some time.

The Resident Subsidy Impact on Regional Air Carrier Route Development: A Case Study

Subsidizing air mobility for Canary Island residents may have an unforeseen impact on regional air carrier route development and this could exacerbate congestion in airports that operate near to their maximum threshold. Reginal routes often require the use of small aircraft such as those from the ATR (Avions de Transport Régional) family. These aircraft types have [some specific characteristics related to its time performance. If airports manage a large proportion of ATR aircrafts, one of their main activities, such as landing and taking off operations (LTO), may become congested, and affect airport capacity. Air carrier economies might be negatively affected because of delays in airport operations. For instance, air carriers’ fuel costs might rise due to aircraft’s increased LTO time. This paper seeks to analyze the impact of regional aviation route development for the Canary airport network; specifically, the effect that it has on airport capacity and air carrier economies.

Air Traffic Management during Rare Events Such as a Pandemic: Paris Charles de Gaulle Case Study

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways.