Charles De Gaulle Airport Research Articles

Travelers' perception of smart airport facilities: An X (Twitter) sentiment analysis

This study investigates travelers' perceptions of smart airport facilities through sentiment analysis. It collected 39,616 comments from thirteen smart airports, and 25,572 tweets were analyzed. Most mentioned words about the smart airport included "technology" and "security". Comments also highlighted concerns about airport customer service, including "bag," "transit," and "disabled." The results also reveal that travelers are more concerned about the outcome of these facilities, such as the efficiency and time management of flights via smart facilities. The most mentioned words in the tweets related to smart airport operations are "flight," "waiting," and "time". Despite most smart facilities being equipped in the airports aiming to raise travelers' satisfaction, the results showed that 12 smart airports' tweets were generally neutral. Smart airport facilities might not add as much value to the airport impression as we expect. Brisbane International Airport was the only one with a positive perception of smart airport facilities. Most travelers mentioned that the airport had faster and better wifi and the airport is continuously improving. In contrast, Charles de Gaulle Airport and London Heathrow Airport had the highest percentage of negative sentiment, with 25.90% and 27.52% of tweets being negative, respectively. Travelers' complained that self-check-in kiosks were a mess, unhelpful staff and poor wifi. The results reveal travelers' concerns regarding smart airport facilities, and they let us know the importance of smart facility management. Smart airport facilities were initially designed to shorten travelers' time and enhance satisfaction, yet unhelpful staff and poor managed kiosks raise dissatisfaction. This study helps airport managers and operators to address the weakest part of smart airports as reflected in social media comments. It also fills the academic voids in examining travelers' satisfaction with smart facilities using social media and sentiment analysis via artificial intelligence.

Benefit of wind networking for aircraft arrival scheduling in terminal manoeuvring area

The effectiveness of air traffic management depends on the amount of obtained information and the accuracy of prediction, leading to a reduction in uncertainty disturbances, and an improvement in the robustness of decision-making results. Among all controlled areas, the Terminal Manoeuvring Area (TMA) is a complex and sophisticated airspace surrounding a coordinated airport in which there exists a high density of traffic, especially with arrivals converging from different directions. With the purpose of improving safety levels and operational efficiency, this paper addresses the aircraft arrival scheduling problem at the microscopic level, integrating the meteorological uncertainty based on wind networking into the optimization model. Wind uncertainty affects the flight speed, which in turn leads to uncertainty in the flight arrival time at one waypoint. Conflict detection and resolution are performed at waypoints and links in a predefined network, based on the predicted time information from the designated route. In order to optimize the time and speed for each aircraft entering TMA, this paper proposes a mathematical model that seeks to minimize the number of potential conflicts, flight delays and the number of flights deviating from the original plan under two assumptions. The first one considers regular weather forecasts for predicting the future positions of aircraft for conflict detection. The second one investigates the potential benefit for aircraft to share their local wind measure thanks to the ADSB-IN and ADSB-OUT channels. A resolution approach is proposed combining a simulated annealing algorithm and a simulation framework. Using data from a case study with 437 flights overflying the Paris Charles de Gaulle (CDG) airport TMA, we demonstrate the validity of the proposed scheduling strategy. A performance analysis is then conducted between standard forecast and wind networking.

A dynamic control method for extended arrival management using enroute speed adjustment and route change strategy

The continuous growth of air traffic leads to congestion in the surrounding area of airport. The optimization of the air traffic in this area requires the improvement of management strategies, especially for the arrival air traffic. SESAR has proposed the concept of Extended-Arrival MANagement (E-AMAN), which aims to plan the arrival streams from an earlier stage in order to achieve delay absorption and earlier planning in the en-route phases of flights. Based on this concept, we address a dynamic/on-line air traffic control problem to provide control decisions for flights in order to achieve safety and efficiency during arrival. The dynamic feature is realized by periodically updating the flight information for aircraft trajectory predictions. Sub-problems corresponding to each information update are established. The objective of this problem is to minimize the weighted sum of route congestion and the conflict resolution workload in the enroute segment, and the number of conflicts in the TMA (Terminal Maneuvering Area). A dynamic weight assignment approach is applied for ensuring that as an aircraft get closer to the TMA, its weight for TMA metrics increases accordingly. A case study based on the arrival traffic of the Paris Charles de Gaulle (CDG) airport is investigated by using a meta-heuristic simulated annealing algorithm combined with the rolling horizon approach. Final results are analyzed in terms of the reduction of concerned safety issues, final control decision distribution, and the time transferred through enroute decision change.

How do airlines react to slot displacements? Evidence from a major airport

The main strategic mechanism developed by the aviation industry to manage airport capacity is IATA's Slot Allocation Process. At airports where such mechanism is in place, airlines need to be assigned slots by an independent slot coordinator to schedule their flights. The slot allocation process involves multiple steps spanning several months of negotiations between airlines and slot coordinators. The most critical step is the initial slot allocation during which the coordinators communicate to airlines whether their slot requests have been accepted or need to be displaced (i.e., moved to a different time of the day). In this article, we use logistic regression models to analyze the reactions of airlines to slot displacement decisions by coordinators and the subsequent need to change their flight schedules. We focus our analysis on Paris Charles de Gaulle (CDG) Airport, one of the busiest worldwide. Our results indicate that even a small displacement (of less than 30 min) can decrease substantially the odds of a flight being scheduled, but that these odds vary widely with the type of airline involved, the time of the day and several other factors. This information could be used by coordinators to make initial slot allocation decisions that take into account their expected flight scheduling implications. Ultimately, this would contribute to more efficient use of the scarce capacity available at congested airports and increased responsiveness to the mobility needs of air passengers.

Clustering-based Iterative Optimization Method for Aircraft Landing Sequence under Crowded Conditon

This paper proposes the clustering-based optimization method for landing sequence of aircraft, which partitions all the aircraft into several clusters and optimizes the schedules of these parted aircraft in parallel. We conducted the computer simulation of the Charles de Gaulle Airport in France and revealed that (1) our proposed method obtains the feasible optimization solution without conflicts among aircraft for landing sequence and (2) the optimized schedule of the proposed method is the better than that of the conventional method based on the fixed time window (corresponding to the fixed size of clusters of aircraft).

Rapid Scale Wind Profiling With Autoregressive Modeling and L-Band Doppler Radar

Radar Wind Profilers (RWP) are well-established instruments for the probing of the atmospheric boundary layer, with the immense advantage of long-range and all-weather operation capability. One of their main limitations, however, is a relatively long integration time compared to other instruments such as lidars. In the context of L-band RWP we show that the use of Autoregressive (AR) modeling for the antenna signals combined with the Maximum Entropy Method (MEM) allows for a correct estimation of radial wind velocity profiles even with very short time samples. A systematical analysis of performance is made with the help of synthetic data. These numerical results are further confirmed by an experimental dataset acquired near the landing runways of Paris Charles de Gaulle (CDG) Airport, France, and validated using a colocated optical lidar at the Aerological station of Payerne, Switzerland. It is found that the AR-MEM approach can successfully derive wind estimates using integration times as short as 2.5 s where the classical spectral approach can barely provide any measurement.

Comparison of Cabin Noise of Airport Express Rail Systems

In this paper, the cabin noise of four airport express rail systems, namely the Taiwan Taoyuan International Airport MRT, the Hong Kong Airport Express, RER B service from Paris Gare du Nord to Paris Charles de Gaulle Airport, and the Shanghai Maglev, have been measured. These four airport express rail systems have different specifications and maximum speeds, ranging from 100 to 300 km/h. The results show a significant low-frequency noise content below 100 Hz, which would not be captured if the measurements were conducted in dB(A). The difference between Leq in terms of dB(C) and dB(A) ranges from 11.3 to 17.0 dB. The maximum speed of the Taoyuan Airport MRT was found to be the lowest at 100 km/h and with the lowest Leq in terms of 66.4 dB(A) and 81.4 dB(C). The Shanghai Maglev has a maximum speed of 300 km/h but a relatively low Leq of 69.7 dB(A), although its top speed is almost three times the maximum of the other airport rail systems. It also has the lowest Lmax of 73.1 dB(A) among the four rail systems. Moreover, the Paris RER B railway system, with its top speed of 120 km/h, was measured to have the highest Leq and Lmax values of 72.8 dB(A) and 83.8 dB(A), respectively.

Analysis of Small-Scale Spatial Variability of Fog at Paris Charles de Gaulle Airport

The small-scale variability of fog over Paris-CdG airport was explored by using the twelve visibility measurements installed on runways. The variability inside this sub-kilometre area is significant for fog, whatever the studied fog characteristics. These results confirm that about 50% of fog is thin and heterogeneous at the airport scale. This study indicated that one point of measurement may be far from the representativeness of sub-kilometre scale area and can create problems when used for verifying numerical models. The fog characteristics cannot be captured by a local measurement of visibility, and it is not possible to deduce fog extension at the local scale from a local measurement, even for low visibility such as for LVP cases (Low Visibility Procedures). This work demonstrated with a perfect forecast framework that a large dispersion of forecast scores could be obtained when using one observation at the local-scale due to representativeness errors of visibility measurements. This dispersion has the same order of magnitude as the current NWP forecast quality of fog. An attempt to quantify the scale heterogeneity of fog was made using the Gini index. This index has allowed highlighting the appearance of waves during the dissipation phase of fog.

A probabilistic model based optimization for aircraft scheduling in terminal area under uncertainty

Air traffic management relies strongly on the accuracy of prediction, while uncertainties such as weather, navigation accuracy, pilot operations, etc. may weaken the performance of predictive tools and cause potential safety issues or reduced capacity. Among all controlled airspace, the Terminal Maneuvering Area (TMA) is one of the most complex areas in which flight safety can be easily affected by unpredictable disturbances. This paper addresses an aircraft scheduling problem under uncertainty with the aim of providing a robust schedule for arrival flights. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models - the deterministic model and a model that incorporates separation buffers - are presented as benchmarks. A meta-heuristic simulated annealing algorithm combined with a time decomposition sliding window is proposed for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented so as to evaluate the optimized solutions obtained from the three models. Statistical comparison among the results shows instability of the model that incorporates the separation buffers, in contrast, the proposed model has absolute advantages in both stability and the conflict absorbing ability when uncertainty arises.

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.

The potential of residential area – A practice design at Roissypole

AbstractThe Roissypole is a complex residential area within Charles de Gaulle airport in Paris. There are already accommodations of different levels with bars, cafes and more in this area, but there is a lack of simple connection between existing buildings, which help brings life to the outside atmosphere. The proposed concept is not a simple public facility renew instead of a new social and functional design, which is to reinvent the airport region design to transform the Roissypole district into an appealing enjoyable and lively living environment. While enhancing the life quality for both long and short-term residents, for people to share their life, live in an active way, it also provides passive life and initiative social activities, which form a new lifestyle.

Flight safety during Covid-19: A study of Charles de Gaulle airport atypical energy approaches

During the COVID-19 period and particularly during lockdown, deviations from nominal operations have shown to become more frequent. To confirm this observation this paper proposes to evaluate the impact of COVID-19, and more generally of crises that lead to a sharp drop in traffic, on the pilot/controller system, especially during the critical approach and landing phases. To study the influence of this type of crisis on flight operations at Charles De Gaulle airport, an existing energy atypicality metric is applied on a reference period before COVID-19 and compared to the COVID-19 period. Whereas the traffic at Charles De Gaulle airport has decreased by around 90% on April 2020, the obtained statistics underlined an increase in the atypical flight ratio of around 50%. This trend can be explained in part by the appearance of glide interceptions from above as a result of trajectory shortenings, and an increase in the proportion of high speed approaches.

An optimization–simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for driving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.

The Stateless Novel

This essay explores the various ways in which W. G. Sebald’s Austerlitz upends traditional understandings of the novel as a form. Specifically, it situates this “prose book of an undetermined kind” against the rise of the steel container as the dominant mode of commodity transportation. The novel today is best understood as a shipping container giving refuge to virtually any kind of aesthetic or narrative content. The “stateless” (as opposed to global) novel requires a new model of individualism, a subjectivity embodied in the tragic life of Mehran Karimi Nasseri, who lingered sans papiers for almost two decades in Paris’s Charles de Gaulle Airport.

Comparative Analysis of Departure Metering at United States and European Airports

Departure metering has the potential to mitigate airport surface congestion and decrease flight delays. This paper considers several candidate departure metering techniques, including a trajectory-based optimization approach using a node-link model and three aggregate queue-based approaches (a scheduler meant to represent NASA’s Airspace Technology Demonstration-2 logic, an optimal control approach, and a robust control approach). The outcomes of these different approaches are compared for two major airports: Paris Charles De Gaulle Airport (CDG) in Europe and Charlotte Douglas International Airport (CLT) in the United States. These two airports have similar aggregate demand and capacity, but the banking of demand at CLT leads to congestion, resulting in higher taxi-out delays and more potential for departure metering at CLT. Stochastic simulations are used to show that departure metering with the robust control approach best accommodates operational uncertainties, and that departure metering yields higher taxi-out time savings at CLT compared to CDG, irrespective of the approach.

Calculation of Airplane Wake Turbulence Re-Categorisation Effects

All aircraft generate wake turbulence that significantly affects flight operations and safety of following airplanes that might fly into it. In order to reduce the risk of wake turbulence related incidents and accidents, airplanes are divided into categories regarding the strength of the vortices they can generate and are separated depending on the category they belong to. Safety is a priority in aviation, but there are other factors that are being taken into consideration both in planning and execution of flight operations. For instance, efficiency in flight operations, airspace and airport capacity and cost-effectiveness are also factors that are to be considered. In order to optimise those factors without jeopardising the level of safety, a new aircraft categorisation has been proposed that divides them into more groups and reduces mandatory standard separation in some cases. This paper analyses possible impacts of re-categorisation. The data being used is randomly generated figures for a simulated airport as well as traffic information from Paris Charles de Gaulle airport where re-categorisation has been introduced. A comparison was made between these two groups of data and conclusions were drawn on that basis.

Flight and passenger efficiency-fairness trade-off for ATFM delay assignment

This paper studies trade-offs between efficiency (performance) and fairness (equity), when assigning ATFM delay pre-tactically (on-ground at origin airport) due to reduced airport capacity at destination. Delay is assigned as the result of the optimisation of a deterministic multi-objective problem considering flight and passenger perspectives when defining objectives of performance and fairness. Two optimisation cases are presented: one where objectives are based on flight metrics, and another one where they are based on passenger metrics. The paper defines and analyses efficiency-fairness trade-offs: the concepts of price of fairness for flights and passengers are defined as the percentage of efficiency loss due to the consideration in the optimisation of fairness; whereas the price of efficiency is considered as the fairness loss relative to the maximum value of the fairness metric, when considering flight or passenger delay in the optimisation. The optimisation model is based on the ground holding problem and uses various objective functions. For performance, total delay for flights (considering reactionary delay), and total delay for passengers (considering outbound connections) are defined. For fairness, the deviation of flight arrivals from a Ration By Schedule solution, and the deviation of delay experienced by passengers with respect to the one obtained in an RBS situation are used. An illustrative application on traffic at Paris Charles de Gaulle airport, a busy European hub airport, and including realistic values of traffic is modelled. A comprehensive trade-off analysis is presented. Results show, how in some cases, gains on one stakeholder can be achieved without implying any detriment on the other one. Passengers are more sensitive to the optimisation and hence, their consideration when assigning delay is recommended. Further research should explore how to combine flight and passenger indicators in the optimisation and consider how the lack of data availability could be mitigated.

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Airport capacity has become a constraint in the air transportation networks due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum potential conflicts. In this framework, airspace together with ground airport operations is considered. The conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways and as capacity overloads for taxiway network and terminals. The methodology proposed in this paper consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution, while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, and this is supported by a case study of Paris Charles de Gaulle Airport.

Optimization of departure runway scheduling incorporating arrival crossings

AbstractThe runway is a key airport resource and an efficient runway operation is critical to enhance the airport efficiency and to reduce delays. This paper addresses the problem of scheduling aircraft departures incorporating arrival crossings. Constraints for wake turbulence separations, flight time window restrictions, and holding queue capacity at runway threshold are explicitly considered. We present two integer linear programming models and a simulated annealing (SA) algorithm. Comparison tests are conducted for the south side of Paris Charles De Gaulle Airport and show that the three proposed methods can significantly improve the solutions based on the simple first‐come‐first‐served rule. Moreover, SA has sufficiently small computation time for real‐time deployment.

Explanation of the collapse of Terminal 2E at Roissy–CDG Airport by nonlinear deterministic and reliability analyses

The collapse of Terminal 2E Roissy at Charles de Gaulle Airport that failed eleven months after its inauguration in 2004 and left four casualties is investigated in this paper by performing deterministic and reliability analyses using the finite element software Ansys. The aim of this study is to assess the accuracy of the terminal design by comparing the calculated reinforcement ratio required with the existing ratio and to evaluate the reliability of Terminal 2E to know if the collapse could be predicted during the design phase. Reliability analysis is performed using a combination of Response Surface Method (RSM) and Monte Carlo Simulation (MCS) and taking into account the nonlinear properties of material and the complexity of geometry to investigate the nonlinear behavior of the structure in order to reduce computational costs and obtain reliable results. Moreover, sensitivity analysis is performed to determine the effects of the uncertainties on the long term deflection in order to investigate the causes of the failure.