Major European Airports Research Articles

Size distribution, sources and chemistry of ultrafine particles at Barcelona-El Prat Airport, Spain

The rapid expansion of the aviation sector raises concerns about air quality impacts within and around airports. Ultrafine particles (UFP, diameter < 100 nm) are of particular concern due to their potential adverse health effects. In this study, particle number concentrations (PNC), particle number size distribution (PNSD), and other ancillary pollutants such as particulate matter (PM), nitrogen oxides (NOX), black carbon (BC), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO) and benzene, as well as organic markers and trace elements (in quasi-UFP) were measured at Barcelona-El Prat Airport (80 m and 250 m from the main taxiway and runway). Comparisons were made with an urban background (UB) location, and source apportionment of PNSD was performed using Positive Matrix Factorization (PMF). PNC inside the airport was nine-fold higher than the UB, and fifteen-fold higher when considering only nucleation mode particles (< 25 nm). Six sources contributing to PNC were identified inside the airport: Taxiing (48.7 %; mode diameter = 17 nm), Industrial/Shipping (7.4 %; mode diameter = 35 nm), Diesel (3.9 %; mode diameter = 64 nm), Regional recirculation (1.1 %; mode diameter = 100 nm), Photonucleation (16.6 %; mode diameter = 13 nm) and Takeoff (18.5 %; mode diameter = 23 nm). Due to the measurement location and prevailing wind patterns, no significant contributions from landings were detected. Chemical analysis of quasi-UFP collected on Electrical Low-Pressure Impactor (ELPI + ) filters (stages 2 to 6: 17–165 nm) revealed higher concentrations (> 2-fold) of Fe, Al, Cr, Cu, Mo, Mn, Pb, Ti, and Sb at the airport compared to the UB, with Al exhibiting the most pronounced disparity. Generally, PAH levels were low at both sites, although concentrations were higher at the airport relative to the UB. Overall, this study provides a comprehensive understanding of UFP within a major European airport, identifying the different sources contributing to PNC and PNSD.

Reviews and Responses for On the Causes and Environmental Impact of Airborne Holdings at Major European Airports

See detailed reviews and responses in the PDF file.&#x0D; DOI for the original paper: https://doi.org/10.59490/joas.2023.7198

Air–rail timetable synchronisation: Improving passenger connections in Europe within and across transportation modes

This study addresses the integration of the railway and airline scheduling problems, in order to offer passengers smooth transfers between rail and air. This paper focuses on optimising the air and rail timetables at 18 major European airports including three hubs and their associated train stations. A multimodal passenger demand simulation, using constraint programming and based on real data, is proposed. A typical week, from Monday to Saturday, of December 2019 is analysed. Ten passenger demand simulations are run for each day, resulting in 60 test instances that are publicly released. The air–rail timetable synchronisation is applied to these 60 instances. Three scenarios are tested in which each operator agrees to change its schedule or not. Results show that changing the schedule of only 13% of European flights by 11 min, and half of trains scheduled to stop at the three hubs of 17 min, on average, could increase the number of suitable connections for passengers by 60%. In addition, if both airlines and railway operators adapt their schedules, passenger comfort is improved and operator costs are reduced, even more so than with unilateral changes.

On the Causes and Environmental Impact of Airborne Holdings at Major European Airports

This paper introduces a data-driven technique for labelling airborne holdings based on their underlying causes, specifically distinguishing between adverse weather conditions and other causes, such as airport capacity. Utilising a dataset comprised of flight trajectories arriving at 45 European airports over a nine-month period, extracted from automatic dependent surveillance-broadcast data, this paper provides valuable insights into the causes behind airborne holdings and their relative environmental impact. The proposed approach involves employing an existing neural network to identify airborne holdings. Subsequently, these holdings are cross-referenced with actual weather observations obtained from meteorological aerodrome reports. Following this, a subset of the holdings is labelled as either weather-related or attributed to other causes, based on historical air traffic flow management regulations. Finally, the cause of the majority of unlabelled holdings is determined using semi-supervised learning. The findings indicate that at least one-quarter of the 30-minute time periods with airborne holdings identified by the neural network can be attributed to weather-related factors, with reduced visibility, strong winds, and convective weather, emerging as the primary contributing events. Intriguingly, weather-related causes account for approximately 40% of the total fuel consumption associated with these procedures.

Analysing the Impact of Go-Around Occurrences at Large European Airports

Go-arounds (GoA) or missed approaches are standard flight procedures initiated when an approach is aborted for safety reasons, requiring pilots to reposition the aircraft for a subsequent landing attempt. This study leverages ADS-B data sourced from the OpenSky Network, collected at 20 major European airports between January 2019 and July 2023. Out of 6.7 million retrieved landing trajectories, 20,196 GoA were identified and analyzed. We conducted statistical evaluations on these GoA instances to compare the rates of GoA at different airports, market segments, and aircraft types. We also looked at the distributions of distance, duration, and fuel consumption of GoA for the different airports. Of particular note, we quantified the impact of a GoA on the surrounding arrival traffic by analyzing how GoA events affect Arrival Sequencing and Metering Area (ASMA) timings. Our results show that the rate of GoA at the assessed airports ranged from 1.5 to 6 occurrences per 1,000 landings. The median duration and distance of a GoA varied depending on the airport, falling between 11.5 and 16.5 minutes, and 36.5 and 58.2 NM respectively. During a GoA, an Airbus A320 typically consumes between 350 and 600 kg of fuel. Importantly, our findings demonstrate that a GoA occurrence can significantly impact the efficiency of arriving traffic at an airport, causing disruptions lasting up to an hour. ASMA timings tend to increase directly after a GoA occurs and peak for landings occurring 10 to 25 minutes after a GoA, with flight time increases ranging from 30 to 100 seconds, depending on the airport. These timings gradually return to their pre-GoA levels within the following hour, though certain airports may experience longer recovery periods. This comprehensive study on GoA provides a deeper understanding of their impact, offering valuable insights that can support data-driven decision-making in the aviation industry.

Environmental inefficiencies for arrival flights at European airports.

In this paper, we analyze two months of trajectory data for aircraft landing in five major European airports. Based on open ADS-B data from the OpenSky Network and open performance models, we enrich all trajectories with automatically detected procedure information, fuel consumption, and emissions for supported aircraft types. To assess the inefficiencies associated with holding patterns, point merges, and continuous descent operations across different airports, we propose methodologies to quantify and compare these environmental inefficiencies. Holding patterns are found to have a higher negative impact on the environment than point merge and continuous descent operations. Furthermore, the paper provides recommendations for procedure evaluations of future airports, which could help policymakers and relevant stakeholders to evaluate the environmental performances of arrival procedures based on open data and open models.

Formation and Routing of Worker Teams for Airport Ground Handling Operations: A Branch-and-Price-and-Check Approach

We address workforce optimization for ground handling operations at the airport, focusing on baggage loading and unloading. Teams of skilled workers have to be formed and routed across the apron to unload the baggage from the aircraft after a landing and to load it before takeoff. Such tasks must be performed within time windows and require a team of workers with different skill levels. The goal is to find a feasible plan that minimizes the sum of the tasks completion times. We formalize a variation of the workforce scheduling and routing problem, integrating team formation, hierarchical skills with downgrading, multiple trips, and different execution modes. We propose a solution approach based on branch-and-price-and-check and test it on real-world instances from a major European hub airport. We propose a model based on the Dantzig–Wolfe decomposition. In the pricing problem, we generate tours of teams as shortest paths with constrained resources in a network. In the master problem, we select an optimal set of tours that do not exceed the workforce availability. Our experiments show that the proposed algorithm can produce optimal solutions for small- and medium-sized instances and good or optimal solutions for large instances. The results also show that our approach outperforms the current airport dispatching policy. Funding: G. Dall’Olio was funded by the Deutsche Forschungsgemeinschaft [Grant Advanced Optimization in a Networked Economy Graduiertenkolleg 2201, Project 277991500]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0110 .

Noise points as an instrument to measure (and limit) noise exposure – empirical findings from Europe 2015-2020

Aircraft noise is the biggest environmental problem around airports, as it has a negative impact on the health of local residents. Therefore, aircraft noise is an important constraint in airport development. Residents' concerns can lead to restrictions on airport capacity, as in the recent case of Amsterdam Schiphol. At this airport, a reduction/limitation of aircraft movements was chosen as a policy instrument to reduce noise impact. Such an approach can be criticized from the point of views of economic efficiency and noise impact among others, as only the number of aircraft movements is taken into account and neither different noise levels nor departure/approach times are not considered. However, the latter parameters also have a significant noise impact, e.g. the different noise levels caused by different types of aircraft. Furthermore, airlines have no advantage in using modern, quieter aircraft or avoiding evening or night for operations if only the number of movements are considered in a regulatory policy. In the worst case such simple restrictions can even have negative impacts on the health of local residents. In this paper, the authors present an alternative approach in which the noise impact at an airport is quantified by different noise point systems. The idea of such systems is similar to the quota count used for London airports at night. Two different options for calculating noise points are presented. The noise point systems are applied to available data of aircraft movements at European airports for the period 2015-2020. Due to the current relevance for aviation policy, a special focus is placed on Amsterdam Schiphol in the analysis and discussion. Based on empirical data, it can be shown that decoupling noise impact from the number of aircraft movements is possible for major European airports, even in a relatively limited time frame. Furthermore, noise point systems could not only be used for noise monitoring, but also as a regulatory tool that ensures the limitation (or even reduction) of noise impact while allowing for a more sustainable growth of the air transport system. From the point of view of noise impact and economic efficiency, such an approach is preferable to a restriction of aircraft movements.

Measuring landing independence and interactions using statistical physics

Benchmarking different aspects of air transport, and especially airport operations, is a research topic both important and complex, as proposed solutions are usually limited by data availability and underlying hypotheses. We here propose the use of two well-known statistical physics concepts, the Hurst exponent and the irreversibility, to measure the presence of interactions between landing flights from a macro-scale perspective. We firstly present a synthetic model of landings at an airport, showing how the two metrics are able to detect interactions arising from high volumes of traffic. Real landing data for twelve major European airports and ten Chinese ones are then analysed, showing that more interactions are present than what expected for the corresponding traffic. Results are nevertheless not homogeneous, with some airports (most notably Milano Malpensa, Madrid and Paris Charles de Gaulle) yielding statistically significant high values; and with Frankfurt airport having been especially impacted by the COVID-19 pandemics, which resulted in an increase in interactions in spite of a reduction in traffic. Some possible reasons behind these results, and their operational significance in terms of efficiency and safety, are finally discussed.

METHODOLOGY OF THE STATUS ASSESSMENT OF AIR NETWORK

The air transport network is one of the most important and critical infrastructures in today's global economy. Air transport would not be able to operate without the air network. This fact significantly contributed to interest in a closer study of the issue of air network status assessment. The aim of this article is to evaluate the European air network by identifying and evaluating major European airports based on scheduled flights. Robustness of this air network was determined with regard to its evaluation. According to research, a suitable methodology was chosen which is describing the organization of data, specification of the research area and selection of method. Civil international airports which are located in European countries recognized by their entire size and handled at least 1 million passengers in 2018, were chosen as the basis for the creation of the European air network model and following evaluation. For these airports, individual scheduled flights were searched including their frequency during the selected period on 11-17 November 2019. The method of centrality used in the field of graph theory was chosen to evaluate the air network of these selected airports with regard to their connectivity. The obtained results identified the most important airports of the European air network and thus represent status assessment of air network during the winter flight schedule 2019/2020. These results were used to monitor the robustness of the European air network as a whole. The robustness of the air network was monitored based on a simulation of the sequent closure of the most important airports in the network with regard to the change of average path length and the size of the giant component. The European air network becomes inoperable when 19-20% of the best evaluated airports in the network are closed.

Corrupted bifractal features in finite uncorrelated power-law distributed data

Multifractal Detrended Fluctuation Analysis stands out as one of the most reliable methods for unveiling multifractal properties, specially when real-world time series are under analysis. However, little is known about how several aspects, like artefacts during the data acquisition process, affect its results. In this work we have numerically investigated the performance of Multifractal Detrended Fluctuation Analysis applied to synthetic finite uncorrelated data following a power-law distribution in the presence of additive noise, and periodic and randomly-placed outliers. We have found that, on one hand, spurious multifractality is observed as a result of data finiteness, while additive noise leads to an underestimation of the exponents hq for q<0 even for low noise levels. On the other hand, additive periodic and randomly-located outliers result in a corrupted inverse multifractality around q=0. Moreover, the presence of randomly-placed outliers corrupts the entire multifractal spectrum, in a way proportional to their density. As an application, the multifractal properties of the time intervals between successive aircraft landings at three major European airports are investigated.

Implementing halal logistics in a non-Muslim-dominant environment: a proposal for reengineering the business processes in two stages

PurposeThe purpose of this paper, positioned in the halal logistics research domain, is to bridge the gap between the theory of halal logistics and halal logistics' empirical implementation through a business process reengineering (BPR) project in the context of a major European airport (i.e. Frankfurt Airport, Fraport AG).Design/methodology/approachAn in-depth case study approach has been adopted in the authors' business processes reengineering proposal. Prior investigations on the topic maintained that “business process analysis and activity elimination” (BP and AE) and “problem analysis” (PA) are the most widespread approaches when the prominent business reengineering dimension under analysis is the specific process/task. Consistently, the authors adopted a six-step BP&amp;AE-based model in order to implement the halal logistics requirements in the context of an air cargo supply chain.FindingsThis paper addresses fundamental issues about the analysis and the redesign of air cargo processes when halal shipments are taken into account. Conceptual breakthroughs of new processes are suggested. The paper sheds light on potential issues which may arise when adapting the extant air cargo processes to halal logistics guidelines. In addition, the paper suggests an appropriate resolution scheme articulated in two stages of progressively higher compliance to halal logistics according to the Malaysian standards (MS).Research limitations/implicationsThe outcome of this work has implications for practitioners, researchers, and transport associations. For practitioners, the study offers an immediately applicable implementation plan which is ready to be discussed with all agents involved in the business reengineering (BR) process. For researchers, the study offers a basis for future halal logistics reengineering projects, both from a theoretical and from an empirical standpoint. Finally, the collaboration of transport associations will become mandatory due to an update of the International Air Transport Association (IATA) code system that includes a dedicated halal code, “HAL”, for halal air cargo shipments.Originality/valueThe concept of halal logistics is still in the infant stage and there is a complete lack of academic publications, especially empirical implementations of halal logistics principles. The authors' project provides detailed guidelines to help air cargo operators operating in non-Muslim-dominant countries to reengineer their internal processes and, in doing so, to comply with halal logistics and principles.

A Data-Driven Approach for Baggage Handling Operations at Airports

Before each flight departs, baggage has to be loaded into containers, which are then forwarded to the airplane. Planning the loading process consists of setting the start times for the loading process and depletion of the baggage storage as well as assigning handling facilities and workers. Flight delays and uncertain arrival times of passengers at the check-in counters require plans that are adjusted dynamically every few minutes and, hence, an efficient planning procedure. We propose a model formulation and a solution procedure that utilize historical flight data to generate reliable plans in a rolling planning fashion, allowing problem parameters to be updated in each reoptimization. To increase the tractability of the problem, we employ a column generation–based heuristic in which new schedules and work profiles are generated in subproblems, which are solved as dynamic programs. In a computational study, we demonstrate the robust performance of the proposed procedure based on real-world data from a major European airport. The results show that (i) the procedure outperforms both a constructive heuristic that mimics human decision making and a meta heuristic (tabu search) and (ii) being able to dynamically (re)allocate baggage handlers leads to improved solutions with considerably fewer left bags.

Multi-period efficiency analysis of major European and Asian airports under fixed proportion technologies

The airport production system includes non-substitutable inputs and non-transformable outputs in most cases, hence subjected to fixed proportion technologies. In this study, we proposed a combined CRITIC-FPR approach to analyze the efficiencies of major European and Asian airports. Fixed Proportion Ratio (FPR) is an efficiency analysis technique for cases where the inputs are not substitutable and the outputs are not transformable. Criteria Importance Through Inter-criteria Correlation (CRITIC) was used here to define the priority weights of each criterion in the FPR calculation and examine the heterogeneity of the airport production system. Using the proposed approach, both the individual performances of the airports and the performance differences between Europe and Asia were measured. Impacts of capacity expansion decisions on airport efficiency were also examined. Findings indicate that cargo is the most distinctive output parameter in the airport industry, while the segregation between passenger volumes of airports has been increasing since 2000. The average efficiency of Asian airports is increasing faster than their European counterparts. Finally, the large-scale investments caused dramatic decreases in airport efficiency while small-scale investments have a positive impact.

Optimization of Transfer Baggage Handling in a Major Transit Airport

We consider the handling of baggage from passengers changing aircraft at an airport. The transfer baggage problem is to assign the bags from each arriving aircraft to an infeed area, from where a network of conveyor belts will bring them to the corresponding outbound flight. The main objective is to minimize the number of missed bags, but in order to reach this goal, we introduce some auxiliary objectives that minimize the transportation time of bags, while ensuring robustness and avoiding overload of the handling facilities. We first present a static mixed integer programming model for the transfer baggage problem. However, the transfer baggage process is subject to uncertainty related to aircraft arrival time, transportation time from aircraft to baggage handling facility, and capacity use in the baggage handling system. In order to handle this uncertainty, a stochastic model is developed, optimizing over a finite set of scenarios. Although the model in theory should lead to more balanced and stable decisions, it suffers of long solution times. Therefore, a semi-stochastic model is presented, where short-term decisions are stochastic, while long-term decisions are deterministic. Computational experiments on real-life data from a major European hub airport are reported, demonstrating that each of the three models has its advantages. In particular, the semi-stochastic model shows promising results both with respect to robustness and solution times.

London Heathrow Airport Uses Real-Time Analytics for Improving Operations

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. In this paper, we describe the process of how we chose the subject of the pilot, namely the improvement of transfer-passenger flows through the airport, and how we helped Heathrow move from its existing legacy system for managing passenger flows to an advanced machine learning–based approach using real-time inputs. The system, which is now in operation at Heathrow, can predict which passengers are likely to miss their connecting flights, reducing the likelihood that departures will incur delays while waiting for delayed passengers. This can be done by off-loading passengers in advance, by expediting passengers through the airport, or by modifying the departure times of aircraft in advance. By aggregating estimated passenger arrival time at various points throughout the airport, the system also improves passenger experiences at the immigration and security desks by enabling modifications to staffing levels in advance of expected surges in arrivals. The nine-stage framework we present here can support the development and implementation of other real-time, data-driven systems. To the best of our knowledge, the proposed system is the first to use machine learning to model passenger flows in an airport.

London Heathrow Airport Uses Real-Time Analytics for Improving Operations

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs), recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the co-location and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data sharing and collaborative decision-making process, selected to improve the effciency of Heathrow’s operations. In Guo et al. (2019), we describe the system that we developed for Heathrow to better estimate passenger connection times, which takes advantage of the integration and data sharing that Heathrow’s APOC offers. In this paper, we describe the process of how we chose the subject of the pilot, namely the improvement of transfer-passenger flows through the airport, and how we helped Heathrow move from its existing legacy system for managing passenger flows to an advanced machine learning-based approach using real-time inputs. The system, which is now in operation at Heathrow, can predict which passengers are likely to miss their connecting flights, reducing the likelihood that departures will incur delays while waiting for delayed passengers. This can be done by offloading passengers in advance, by expediting passengers through the airport, or by modifying the departure times of aircraft in advance. By aggregating estimated passenger arrival time at various points throughout the airport, the system also improves passenger experiences at immigration and security desks, by enabling modifications to staffing levels in advance of expected surges in arrivals. The nine-stage framework we present here can support the development and implementation of other real-time data-driven systems. To the best of our knowledge, the proposed system is the first to use machine learning to model passenger flows in an airport.

Predictive modeling of inbound demand at major European airports with Poisson and Pre-Scheduled Random Arrivals

This paper presents an exhaustive study of the arrivals process at eight major European airports. Using inbound traffic data, we define, compare, and contrast a data-driven in-homogeneous Poisson and Pre-Scheduled Random Arrivals (PSRA) point process with respect to their ability to predict future demand. As part of this analysis, we show the weaknesses and difficulties of using a non-homogeneous Poisson process to model the arrivals stream. On the other hand, our novel and simple data-driven (PSRA) model captures and predicts the main properties of the typical arrivals stream with good accuracy. These results have important implication for the modeling and simulation-based analyses of inbound traffic and can improve the use of available capacity, thus reducing air traffic delays. In a nutshell, the results lead to the conclusion that, in the European context, the (PSRA) model provides more accurate predictions.

Improving aircraft approach operations taking into account noise and fuel consumption

While air transport brings very significant economic and social benefits to the cities and regions served by airports, aircraft noise is the single major cause of community opposition to airport operations, becoming a critical issue that affects the sustainability of future traffic growth. However, planning operations exclusively focusing on noise impact may result in an increase of fuel consumption or delays. This paper develops a suitable bi-objective model for landing aircraft, which finds a schedule that minimises noise impact, total fuel consumption and delays, under wake vortex separation and Constrained Position Shifting restrictions. The results of this model are compared with real operations in a major European airport to assess the potential level of improvements. By comparing with real data from Madrid-Barajas airport, the research shows potential improvements of up to 4.5% reduction of total fuel consumption (without increasing noise levels) only by modifying the sequence of arrivals, and up to 43% (without extra fuel consumption) of reduction in noise impact over the populations under study.

Planning efficient 4D trajectories in Air Traffic Flow Management.

In this paper, we focus on designing efficient 4D trajectories for the planning phase of Air Traffic Flow Management (ATFM). A key feature of the proposed approach is the inclusion of stakeholders’ preferences and priorities. In particular, we have implemented two priority mechanisms recently developed by Eurocontrol, namely the Fleet Delay Reordering and the Margins.For this purpose, we have customized a multi-objective binary program for the ATFM problem taking into account the specific assumptions required for the ATFM planning phase. To compute the Pareto frontier in a reasonable computational time, we have developed a simulated annealing algorithm. The algorithm has been tested on an instance resembling real world conditions using data extracted from the Eurocontrol data repository. This instance involves four major European airports and their air traffic in one of the busiest days of year 2016, and precisely, October 3rd. The simulated annealing algorithm has shown good computational performances and has provided a good approximation of the Pareto optimal frontier. The results have been validated using Eurocontrol tools and have demonstrated the viability of the proposed approach. Practitioners and stakeholders’ representatives have provided positive feedback on the proposed modeling approach and on the inclusion of ATM stakeholders’ preferences and priorities.