Busy Airports Research Articles

Pavement Management: Key to Sustainable Concrete Pavement at the World’s Busiest Airport

Hartsfield-Jackson Atlanta International Airport (ATL) is the busiest airport in the world in terms of passengers and aircraft operations. In 2010, it served more than 89 million passengers with 950,119 aircraft operations. Reliable performance with little down-time for its airfield pavements is critical for efficient, safe, and economical travel. Concrete pavements have long been the keystone of this capability at ATL. This paper presents the design, construction, maintenance, and evaluation procedures and activities that comprise the Pavement Management System (PMS) used at ATL to provide sustainable concrete pavements. The sustainability issues include long life pavements that have impacts far beyond those of the concrete pavement itself and the airport. Sustainable concrete pavements result in reduced down-time, fewer construction delays, less fuel used waiting for takeoff, and fewer materials consumed due to reduced frequency of construction/reconstruction and maintenance. These results help reduce the overall carbon footprint of the airport and its impact on the carbon footprint of other airports and aircraft operations throughout the worldwide air travel system.

Towards net zero aviation: Exploring safe hydrogen refuelling at airports

Hydrogen flight is one important part of the way to net zero aviation. However, safety challenges around refuelling are not well understood but are paramount to enable airports to be more comfortable with using hydrogen in the airport environment. This study investigates safety considerations of hydrogen aircraft refuelling at airports. Technical and human factor risks are explored as well as risk assessment models. Two focus groups were conducted in 2022. Data was analysed using NVivo, revealing major themes including the mental and physical performance of refuellers, technical aspects of refuelling stations, environmental factors, and the use of risk assessment models. These findings contribute significantly to an understanding of hydrogen refuelling challenges in busy airport environments. Recommendations help airports preparing for hydrogen as a fuel source, further supporting the transition towards net zero aviation. Future research could focus on carrying out experiments analysing chemical reactions between kerosene and hydrogen vapours and testing the identified risk assessment tools in different airport environments.

Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information

The taxi-out time of an airport scene can be categorized into the unimpeded taxi-out time and the additional taxi-out time. Usually, additional taxi-out time is used as a key index to monitor taxi-out performance, and its accurate prediction plays an important role in optimizing the allocation of time slots at an airport and improving scene operation efficiency. Taking Shanghai Pudong International Airport as the research object, we first analyze its layout and construct the origin–destination pairs (ODPs) based on the stand groups and runways. Then, we develop a multiple linear regression model based on the arrival and departure flows to calculate the unimpeded taxi-out times for all ODPs. The actual taxi-out time is then subtracted from the unimpeded taxi-out time to obtain the historical additional taxi-out time of each flight. We propose three new flow features related to the structure: the corridor departure flow, the corridor arrival flow, and the departure flow proportion of ODPs, based on which we construct a dataset for training the prediction model. We then propose an additional taxi-out time prediction model based on the nutcracker optimization algorithm (NOA) and XGBoost and run comparison experiments on the operation data of our target airport. The results show that the optimized prediction model we proposed has the best performance compared with the traditional XGBoost model and other commonly used prediction models, and the proposed structure-related features have high correlations with additional taxi-out time.

Research on Safety Separation Distance in Paired Approach Based on Monte Carlo Simulation

Faced with the increasing number of flights, efficient and orderly operations have become a critical issue for busy airports. The closely spaced parallel runway paired approach technology has been proven to be an effective solution for increasing runway capacity and operational efficiency. This research focuses on determining the safety separation between paired aircraft, particularly the collision safety limit. This study refines the kinematic modeling process by constructing a three-dimensional kinematic model and advancing the starting point of paired approach, making the simulation process more aligned with reality. Subsequently, the Monte Carlo simulation method is applied, integrating the kinematic model and considering collision risks to simulate the entire paired approach process. Multiple scenarios are created, and simulation experiments are conducted to determine the safety separation between the two paired aircraft. Finally, by adjusting parameter settings and taking the example of the two closely spaced parallel runways at Chongqing Jiangbei International Airport, the correlation between various influencing factors and the safety separation is analyzed. Different scenarios are simulated 100,000 times each, and the results show that the speed difference between the two paired aircraft has the most significant impact on the safety separation, followed by the paired approach mode and runway centerline spacing. The wake turbulence category matching has a minimal effect on the safety separation. The results provide a theoretical basis for airports with closely spaced parallel runways to further reduce the separation between paired aircraft, thereby enhancing airport capacity and runway operational efficiency.

The operational dynamics of end-around taxiways: Insights from Istanbul Airport

The rapid rise in global air traffic poses significant challenges for airports, particularly in maintaining operational safety and efficiency. One of the main issues is the risk of runway crossings during ground movements, which can lead to runway incursions. This is particularly problematic in airports with parallel runway operations, where managing multiple runways and ground movements is more complex. End-Around Taxiways (EATs) have been introduced as a strategic solution to mitigate these risks. However, their implementation also introduces new operational challenges and complexities. This study evaluates the impact of EATs on Istanbul Airport (LTFM), one of Europe’s busiest airports. Using Automatic Dependent Surveillance-Broadcast (ADS-B) data, the research examines the use of EATs in taxi times, acceleration counts and durations, stop-and-go counts, level-off times, hot spots, gate and stand usages by developing new catchment algorithms. The findings show that EATs present operational challenges such as increased taxi times, number of acceleration movements, and level off times. The study emphasizes the need to consider local airport configurations when implementing EATs and suggests new constraints to optimize their usage.

Airport runway FOD detection model based on improved YOLOv10m

Abstract Foreign Object Debris (FOD) poses a significant safety risk to aircraft operations, making efficient and accurate detection methods crucial. Existing detection techniques often struggle to meet the demands of busy airports due to limitations in real-time performance and detection accuracy. To address these challenges, we proposed a lightweight FOD detection model based on YOLOv10. By incorporating PConv and EMA attention mechanisms, our model enhances detection accuracy, achieving a 0.4% increase in mAP@50 and a 0.7% improvement in mAP@50-95 compared to the original YOLOv10m. Additionally, our model reduces the number of parameters by 19.8% and decreases computational complexity by 20.9%, thanks to the integration of Feature Pyramid Shared Convolution (FPSConv) and a lightweight shared detection head (Detect-LSCD). These improvements make our model more suitable for real-time FOD detection on airport runways, offering a more efficient and accurate solution than the original YOLOv10m.

Runway assignment optimisation model for Istanbul Airport considering multiple parallel runway operations

Abstract The aviation industry has rapidly developed in recent years. Due to the increased number of flight operations, managing air traffic has become essential. The air traffic management system aims to reduce the air traffic control workload and use existing resources more efficiently. This study proposed a new mixed integer linear programming model to minimise the total fuel consumption during taxi operations for the runway assignment problem, comparing the actual Istanbul Airport runway assignment data. The average taxi times are calculated using the 30,000-flight operations data for each arrival and departure taxi route. Also, 47 different aircraft types are obtained using the data for the fuel consumption calculation. The International Civil. Aviation Organisation (IACO) aircraft engine emissions databank provides the fuel consumption values for each aircraft according to engine type. This approach allows our model to calculate more realistic fuel consumption for taxi operations, as each aircraft engine type has a different fuel consumption value. The proposed model is implemented at Istanbul Airport, the busiest airport in Turkey, where multiple parallel runway operations are applied. The results showed that the proposed model reduced total fuel consumption for taxi operations between 6.6% and 14.4% compared to the actual Istanbul Airport runway assignment data.

Airport time profile construction driven by flight delay prediction

Slot structure is the equilibrium result of market demand side and slot resource supply side, while slot parameters reflect the operational support capacity of the aviation system. Time parameters reflect the operational support capability of the aviation system. Time structure should not only reflect changes in market demand, but also meet the constraints of operational efficiency. Constructing a reasonable 18–24 h timetable profile for busy airports that meets normal expectations for declared capacity and seasonal scheduling is a challenge in civil aviation slot management. This study utilizes historical data on airport flights and weather conditions to establish a regression prediction model for the time structure using K-means clustering and partial least squares regression. Additionally, ensemble learning is employed to forecast flight delay levels. The findings demonstrate that random forest yields favorable results in regression and prediction tasks, allowing for the integration of upper (good weather) and lower (severse weather) limits of the time profile with delay predictions as time parameter intervals. Consequently, the flights falling within these intervals achieve an average delay level of less than 15 min which meets the expectations of normal flight.

Assessing the impacts of videoconferences on domestic business flights in Brazil’s post-pandemic era

During the COVID-19 pandemic, society realized that many activities could be conducted remotely, achieving similar or superior results. Several trends were anticipated and consolidated, such as remote work and hybrid activities. In this context, the air transport market was also profoundly affected, and we are still trying to understand the size and depth of the ongoing changes. It has become crucial for airline market strategies to evaluate how the behavior of business passenger demand has changed, considering the new conditions created and the intensified use of information and communication technology (ICT) tools, particularly videoconferencing. The article aims to identify and analyze the factors that have influenced the number of domestic business flights in Braziĺs post-pandemic era, focusing on the increased use of ICT. It applied a logit model to a database collected at Congonhas Airport in São Paulo, the second busiest airport in the country and the seventh in Latin America, which operates only domestic flights and is used mainly for business trips. Findings showed that the most significant variables influencing reduced travel for work were the number of trips made, the company’s market segment, and travelers’ perceptions about how the company dealt with topics such as productivity, employee well-being, videoconference use, and airline ticket purchases. With this understanding, airlines can develop more effective advertising strategies, enhance ticket sales, and prospect customers in more promising market niches.

A Novel Method for Modeling and Predicting Transportation Data Via Multideep Assessment Methodology and Fractional Calculus

Abstract Aviation is one of the most global industries, and if we can model and predict a country’s air transportation flow and indicators ahead of time, we may be able to use it as a key decision-making tool for the management and operation process. This study proposes a new modeling, and prediction method that employs both fractional calculus and Multi Deep Assessment Methodology (MDAM) techniques. For the application, air passengers carried, air freight, available seat kilometers, number of flights, destination points, international travelers, international destination points, and international flight data between 2011 and 2019 for eight countries with the busiest airports were chosen. As a result, the highest modeling error was discovered to be Germany’s air transport freight factor expressed as a percentage of 1,59E-02. The percentage of predictions with errors less than 10% was 90.278. We also compared the performance of two different MDAM methodologies. The novel MDAM wd methodology proposed in this paper has a higher accuracy in aviation factors prediction and modeling.

Mechanism Analysis of the Impact of COVID-19 on the Whole Process of Aircraft Turnaround Operations

Efficient aircraft turnaround operations at airports are vital to ensure overall air traffic network performance. After the outbreak of COVID-19, the traditional aircraft ground handling process has changed significantly due to new requirements put forward by the pandemic prevention and control policy. To better understand how COVID-19 has affected ground handling operations, a discrete-event simulation model of turnaround is established to analyze the change in the whole turnaround process before and after the pandemic. The critical path of turnaround operations was used to identify the significantly affected subprocesses to which airports should pay attention. For a case study on the two busiest airports in China, the aircraft turnaround time increased by about 18% after COVID-19. Cabin cleaning, catering, and passenger embarking were the main processes in causing this increase. By evaluating the impact mechanism of COVID-19 on turnaround operations, the study sheds light on strategic, tactical, and operational approaches for relevant authorities.

Planes, trains, and automobiles: Three case studies of building envelope design addressing transportation noise

This presentation will focus on three case studies of designs to limit transportation noise through the building shell to support the successful use of the building. The first project focuses on an office building located close to beautiful water views that desired a building shell made primarily of glass. However, it will be located directly under the flight path of a nearby US Airforce base. The second project considers a hotel conference center located directly adjacent to a heavy rail line. The third project address movie sound stages located adjacent to a major highway and directly under the flight path of Hartsfield Jackson International Airport, known as the busiest airport in the world. Site measurements were taken for each of these locations and a detailed review of the data used to determine which noise sources and frequencies of concern would be the focus of the design. The presentation will present examples of the collected data, the design strategies used, and discuss the challenges faced by the design team.

Air Traffic Flow Management Delay Prediction Based on Feature Extraction and an Optimization Algorithm

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency.

Determination of Limited Security Area at the Indonesian Flight Academy, Banyuwangi: A Case Study at Banyuwangi Airport

The Security Restricted Area at airports plays a crucial role in preventing unauthorized access and criminal activities around the airport. This research aims to evaluate the design of the Security Restricted Area and access control at Banyuwangi Airport, operated by the Indonesian Civil Pilot Academy Banyuwangi. The research adopts a qualitative approach with data collection techniques such as participant observation, interviews with relevant stakeholders, and analysis of documents related to airport security regulations and standards. The findings reveal that the Airport Business Entity (BUBU) or airport operator is responsible for security in the airside area, but through a Memorandum of Understanding (MoU), this responsibility is delegated to the Indonesian Civil Pilot Academy Banyuwangi. Weaknesses in access control and security measures were identified in the airside area operated by the academy, including inadequate guarding at certain access points and the absence of mapping for the Security Restricted Area. The research provides recommendations to enhance access control and security at Banyuwangi Airport, aiming to improve operational safety and the airport's reputation.

Unraveling passengers’ preferences: investigating airport access mode choice in Tehran, Iran

Airports are imperative elements in fueling the economic growth of a country by reducing the distances between origins and destinations for the movement of both passengers and goods. Managing ground access modes of travel has become essential as the majority of trips are made by private cars, leading to increased environmental issues and congestion. To alleviate these problems, it is vital to identify the factors that shape air passengers' choice of ground access mode. In this work, we utilized inclusive Revealed Preference data (RP) collected through face-to-face interviews and applied a Multinomial Logit (MNL) model to study passengers' ground access mode choice behavior at Mehrabad International Airport, the busiest airport in Iran. The results showed that different factors such as socioeconomic status, travel characteristics, and distance to the airport affect the mode choice behavior of passengers in reaching the airport.

Enhancing customer loyalty in the airline industry: Critical personal values and switching barriers

The current study aims to explore how customers’ personal values influence customer-perceived switching barriers and drive customer loyalty in the airline industry. Notably, it investigates switching barriers as a moderator between three dimensions of personal values and customer loyalty. Data were collected from passengers at Tan Son Nhat International Airport, Ho Chi Minh City, the busiest airport in Vietnam, using the convenience sampling method. Data analysis was conducted through structural equation modelling. The moderating effects were tested by PROCESS 3.4 macro in SPSS. The analysis shows that different facets of personal values explicate different impacts on switching barriers and customer loyalty. Further, the heterogeneous moderating role of the switching barriers in the relationship between dimensions of personal values and consumer loyalty is highlighted. These results suggest effective strategies for airlines to successfully retain their customer base.

Enhanced Model for Terminal Airspace Capacity Estimation

Terminal Airspace comprises the area around an airport in which various aircraft in its vicinity are in ascending or descending phases. This paper describes an enhanced model for estimation of Ultimate Capacity of Terminal Airspace, given its geometric layout, traffic data, and the separation minima maintained. This model can be used to determine the number of aircraft in the Terminal airspace of an airport that can be safely handled by the Air Traffic Controllers. An existing probabilistic model for Terminal Capacity assessment was enhanced to include operations on intersecting and parallel runways. An important out-come of this methodology is the minimum time gap between successive operation at the runway, which is utilized to draw a time space diagram of runway operation, and thus obtain an estimate of Delay under various operational scenarios. The model was first utilized to estimate the Terminal airspace capacity of the two most busy airports in India, viz., Mumbai and Delhi, and later for other main Airports viz., Kolkata, Chennai, Ahmadabad, Guwahati, Hyderabad, Kochi and Thiruvananthapuram. A critical analysis of the results lead to suggestions like slot distribution, rescheduling of aircraft, restructuring of spacio-geometrical conditions of airspace and airfield etc. that may be considered to increase capacity and reduce delays at Indian Airports.

Security Professionals’ Perceptions of Crime at the Oliver Reginald Tambo International Airport: Towards Sustainable Solutions

Abstract Across the globe, airports are regarded as essential infrastructure needing protection. One of the major goals of an airport, as encouraged by the regulatory bodies and evoked through public expectation, is safety. However, a public environment, such as an airport, is vulnerable to crime. The Oliver Reginald Tambo International Airport (ORTIA) is Africa’s biggest and busiest airport, servicing approximately 19 million passengers annually. Moreover, tourism is a key component of a healthy economy. Therefore, safety and security are significant for sustainable tourism in South Africa. Thus, this paper used a quantitative inquiry to determine security professionals’ perceptions of crime at ORTIA. A non-experimental and randomized cross-sectional research design was used. Through random sampling, a total of 100 participants contributed to the study and yielded a representative percentage of 36%. Data were collected through online self-administered questionnaires from security personnel employed at ORTIA and analysed through descriptive analysis. The findings revealed that the most problematic crimes taking place at ORTIA were reported to be theft, mishandled baggage, automated teller machine (ATM) crimes, burglary, robbery, illegal immigrants and smuggling of drugs. Furthermore, most participants had witnessed a crime taking place at the airport. As informed by the participants, sustainable solutions are proposed in terms of crime prevention and intervention.

Improving the management of air traffic congestion during the approach phase

AbstractNowadays most busy international airports and their corresponding terminal areas are suffering from huge congestion issues due to the simultaneity of their arrival aircraft. The aim of this paper is to establish a new separation method using time- based-separation, speed modification during approach phases and Point Merge System (PMS) so as to ensure efficiently the traffic flow. This work took as a case study the busiest airport of Morocco, The Mohammed V International airport of Casablanca. The proposed management model offers very good results when compared with other models such as the first-come first-served (FCFS) model.

Curb Allocation and Pick-Up Drop-Off Aggregation for a Shared Autonomous Vehicle Fleet

Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region’s 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD.