Airport Surface Research Articles

Towards Greener Airport Surface Operations: A Reinforcement Learning Approach for Autonomous Taxiing

Towards Greener Airport Surface Operations: A Reinforcement Learning Approach for Autonomous Taxiing

Feasibility Study of GBAS/INS and RRAIM for Airport Surface Movement Under Low-Visibility Conditions

Feasibility Study of GBAS/INS and RRAIM for Airport Surface Movement Under Low-Visibility Conditions

Modeling Multi-Timescale Dynamics for Airport Surface Congestion and Recovery

Modeling Multi-Timescale Dynamics for Airport Surface Congestion and Recovery

Airport Surface Arrival and Departure Scheduling Using Extended First-Come, First-Served Scheduler

This paper demonstrates the effectiveness of the Extended First-Come, First-Served (EFCFS) scheduler for integrated arrival and departure scheduling by comparing the scheduling results with the recorded operational data at Incheon International Airport (ICN), Republic of Korea. The EFCFS scheduler can handle multiple capacity- or flow-rate-related constraints along the path of each flight, which is represented by a node–link graph structure, and can solve large-scale problems with low computational cost. However, few studies have attempted a systematic verification of the EFCFS scheduler by comparing the scheduling results with historical operational data. In this paper, flights are scheduled between gates and runways on the airport surface with detailed constraints such as runway wake turbulence separation minima and conflict-free taxiing. The scheduler is tested using historical flight data from 15 August 2022 at ICN. The input schedule is generated based on the flight plan data extracted from the Flight Operation Information System (FOIS) and airport surface detection equipment data, and the results are compared with the times extracted from the FOIS data. The scheduling results for 500 aircraft show that the average takeoff delay is reduced by about 19 min, while the average landing delay is increased by less than one minute when the gate occupancy constraint is not considered. The results also confirm that the EFCFS effectively utilizes the available time slots to reduce delays by switching the original departure or arrival orders for a small number of flights.

Delay in the Air or Detour on the Ground?—A Case Study in Guangzhou Baiyun International Airport

Collaboration between terminal airspace and airport surface operation shows an increasing significance for the best efficiency of both parts of the air traffic management domain. Runways play a critical role in connecting the two parts for departure and arrival aircraft. Suppose the gate and the entry fix of an aircraft are predetermined according to the flight plan, and they are on the opposite side of the airport terminal. The aircraft will either spend more time (i.e., delay in the air) landing on a runway close to its gate or take a longer distance (i.e., detour on the ground) taxiing to its gate if a runway close to its entry fix is assigned. This paper proposes a runway assignment model considering terminal airspace operation and airport surface movement simultaneously to discover how runway assignments can affect integrated operations. Four different runway assignment schemes are applied in this model. Subsequently, a metaheuristic method is proposed to solve the model. Furthermore, the historical taxiing and flight time data are analyzed to demonstrate the potential benefits of runway reassignment. Finally, the results show that the free assignment of the runway stands out among the four schemes, not only in the performance of terminal airspace operation (lower flight time) but also in airport surface movement (lower pushback delay, taxi time).

Background concentrations and spatial distribution of PFAS in surface waters and sediments of the greater Melbourne area, Australia

A survey of per- and polyfluoroalkyl substances (PFAS) was conducted in Melbourne, Australia to determine background concentrations in residential, industrial, municipal wastewater treatment plants, and rural land uses. Surface water and sediment samples collected from 65 sites with different catchment land uses were analysed for thirty-three PFAS. Twenty-two out of thirty-three targeted PFAS were detected, with at least one PFAS species was detected in 98% water samples and 8% sediment samples. One site was determined to have point-source pollution from an airport (surface water Σ33PFAS = 4261 ng/L) and was excluded from statistical analyses. The median Σ33PFAS concentration in surface water was 63.5 ng/L and the average was 78.6 ng/L (range < DL-526 ng/L). PFAS species with the highest median concentrations were PFBA (11.3 ng/L), PFHxA (9.2 ng/L), PFOA (8.3 ng/L), PFOS (8.0 ng/L), PFPeA (7.5 ng/L), PFHpA (3.2 ng/L), and PFHxS (2.9 ng/L).The average Σ4PFAS in sediments was 0.35 ng/g d.w. (range = <DL-6.7 ng/g) and the congeners detected were PFOS, N-MeFOSAA, N-EtFOSAA, and N-EtFOSE. Significantly higher concentrations of all PFAS were found in urban areas than rural, except for PFBA which was evenly dispersed throughout all areas. Short-chain perfluoroalkyl carboxylic acids (PFCAs), PFBA, PFPeA, PFHxA, PFHpA, and the PFSA, PFBS were evenly dispersed throughout urban and industrial areas. PFOA, PFNA, and PFDA were higher, but not significantly, in residential catchments than industrial catchments. Industrial areas had significantly higher concentrations of perfluoroalkyl sulfonic acids (PFSAs), PFPeS, PFHxS, and PFOS, and 6:2 FTS than other urbanised areas. The distribution of PFAS throughout catchment types in this study displayed similar trends to other studies including findings of PFBA in background areas. Ongoing monitoring of defined catchments is recommended to improve understanding of PFAS sources and changes in environmental profiles, and to assess the implications of its occurrence throughout the environment.

ASSD-YOLO: a small object detection method based on improved YOLOv7 for airport surface surveillance

ASSD-YOLO: a small object detection method based on improved YOLOv7 for airport surface surveillance

Hybrid Electromagnetic Simulation Using 2D-FDTD and Ray-Tracing Methods for Airport Surfaces

In this paper, a hybrid electromagnetic simulation method of two-dimensional FDTD and ray-tracing methods suitable for an airport surface was proposed. The power variation due to ground reflection, refraction and creeping is calculated by two-dimensional FDTD method and ray-tracing method is used to calculate the reflecting and diffracted powers from buildings. The proposed approach was validated by measurement using a 1/50 scale-model of an airport model with a building model in various positions at 5 GHz. The proposed method allowed measured power distributions to correlate with simulated figures to within 4.8 dB and their null positions were also estimated to an error tolerance of within 0.01 m.

A Conflict Resolution Strategy at a Taxiway Intersection by Combining a Monte Carlo Tree Search with Prior Knowledge

With the escalating complexity of surface operations at large airports, the conflict risk for aircraft taxiing has correspondingly increased. Usually, the Air Traffic Controllers (ATCOs) generate route, speed and holding instructions to resolve conflicts. In this paper, we introduce a conflict resolution framework that incorporates prior knowledge by integrating a Multi-Layer Perceptron (MLP) neural network into the Monte Carlo Tree Search (MCTS) approach. The neural network is trained to learn the allocation strategy for waiting time extracted from actual aircraft taxiing trajectory data. Subsequently, the action probability distribution generated with the neural network is embedded into the MCTS algorithm as a heuristic evaluation function to guide the search process in finding the optimal conflict resolution strategy. Experimental results show that the average conflict resolution rate is 96.8% in different conflict scenarios, and the taxiing time required to resolve conflicts is reduced by an average of 42.77% compared to the taxiing time in actual airport surface operations.

Investigating Runway Incursion Incidents at United States Airports

According to the Federal Aviation Administration (FAA), the number of runway incursions is increasing. Over the last two decades, the number of runway incursions at U.S. airports has increased from 987 in 2002 to 25,036 in 2020. Runway incursions are a major threat to aviation safety, causing major delays and financial consequences for airlines, as well as injury or death through incidents such as aircraft collisions. The FAA promotes the implementation of runway safety technology, infrastructure, procedural methods, alterations to airport layouts, and training practices to reduce the frequency of runway incursions. In this paper, the relationship between airport geometry factors, mitigating technologies, and the number of runway incursions at large hub airports in the United States was investigated using a random effects Poisson model for analyses of panel data. Airport operations data from the FAA Air Traffic Activity System, runway incursion data from the FAA Aviation Safety Information Analysis and Sharing System from 2002 to 2020, and airport geometry data created using airport geometry features from the FAA airport diagrams were collected. Thirty large hub airports with FAA-installed mitigating technologies were investigated. The model identified significant variables that correlate with runway incursions for large hub airport categories defined by the National Plan of Integrated Airport Systems (NPIAS). The model results indicate that airports with significant numbers of runway-to-runway intersection points increase runway incursion rates and mitigating technologies Runway Status Lights (RWSLs) and Airport Surface Detection Equipment, Model X (ASDE-X), can help reduce runway incursions at severity levels A and B.

Bilevel spatial–temporal aircraft taxiing optimization considering carbon emissions

Air transportation pollution is mainly caused by aircraft carbon emissions. One of the key methods for reducing aircraft carbon emissions is to optimize the taxiing route on the airport surface. In view of this, a bilevel spatial–temporal taxiing optimization model considering carbon emissions is proposed in this paper. A bilevel programming method is deployed in the model to minimize the carbon emissions, taxiing waiting time, and conflicts of aircraft, which not only facilitates environmentally friendly airport establishment but also improve airport operation efficiency. Particularly, the influence of taxiing conflicts and turning action are considered when calculating the carbon emissions in the paper, which provides a more effective subjective for the model. An iterative neighborhood search algorithm is proposed to solve the model. The operation data of Baiyun Airport in China are utilized to validate the model. The results indicate that, compared with the first-come first-serve (FCFS), the proposed model reduces the total carbon emissions of aircraft by 814 kg on the premise of no taxiing conflict or 2% of the total. Compared with tabu search, it can reduce carbon emissions by 375 kg. This study can provide a more environmentally friendly taxiing strategy for aircraft and reduce carbon emissions.

Aircraft taxiing conflict avoidance approach based on Gaussian spatial–temporal prediction

To detect and avoid taxiing conflict on the airport surface and ensure the safety of airfield operation, constructing a Gaussian spatial–temporal prediction is very important. This paper summarizes the types of taxiing conflicts on the airport surface and the dynamic process of aircraft conflicts, and defines the taxiing conflict area based on the characteristics of aircraft types. Based on Gaussian spatial–temporal regression prediction and real-time taxiing speed, the taxiing conflict risk is quantified, and a dynamic interval taxiing speed feedback adjustment method is established. The proposed quantization method and feedback adjustment method are verified by simulation calculation and historical taxi data, respectively. Results show that the proposed method can quantitatively evaluate the aircraft conflict risk of cross-taxiing and dynamically adjust the taxiing speed of the aircraft through the conflict risk quantification value to control the aircraft conflict risk within a safe range and avoid taxiing conflicts.

Ground-Air Traffic Congestion Propagation Model Based on Hierarchical Control Interdependent Network

A multilayer network approach to model and analyze air traffic networks is proposed. These networks are viewed as complex systems with interactions between airports, airspaces, procedures, and air traffic flows (ATFs). A topology-based airport-airspace network and a flight trajectory network are developed to represent critical physical and operational characteristics. A multilayer traffic flow network and an interrelated traffic congestion propagation network are also formulated to represent the ATF connection and congestion propagation dynamics, respectively. Furthermore, a set of analytical metrics, including those of airport surface (AS), terminal controlled airspace (TCA), and area-controlled airspace (ACA), is introduced and applied to a case study in central and south-eastern China. The empirical results show the existence of a fundamental diagram of the airport, terminal, and intersections of air routes. Moreover, the dynamics and underlying mechanisms of congestion propagation through the AS-TCA-ACA network are revealed and interpreted using the classical susceptible-infectious-removed model in a hierarchical network. Finally, a high propagation probability among adjacent terminals and a high recovery probability are identified at the network system level. This study provides analytical tools for comprehending the complex interactions among air traffic systems and identifies future developments and automation of layered coupled air traffic management systems.

Spatial Modeling of Airport Surface Fuel Burn for Environmental Impact Analyses

The assessment of the fuel burn and emissions impact of airport surface operations is a key part of understanding the environmental impacts of aviation. These assessments are needed at two levels: the analysis of inventories (the total amount of fuel burned and emissions discharged over some period of time), and the analysis of spatial distributions (the amount of emissions experienced at a particular location within or near the airport). Although the availability of taxi times for the operations of interest is sufficient for inventory analysis, the analysis of spatial distributions requires estimates of where on the airport surface an aircraft is located as it consumes fuel. In this paper, we show how a data-driven queuing network model can be developed in order to estimate the time that an aircraft spends at different congested locations on the airport surface. These models are useful both in spatial distribution analysis and in accurately predicting taxi times in the absence of measurements (for example, for projected demand sets). We use measurements of ultrafine particles at Los Angeles International Airport to demonstrate that the proposed model can help predict the measured emissions at different monitoring sites located in the vicinity of the airport. In the process, we show how one could develop a machine learning model of the spatial distribution of airport surface emissions given the pollutant measurements, air traffic demand, and prevailing weather conditions. Finally, we develop a clustering-based method to evaluate the generalizability of our surface operations modeling framework.

Safety and Efficiency Evaluation Model for Converging Operation of Aircraft and Vehicles

To explore the mixed traffic characteristics of vehicles and aircraft on the airport surface and solve the problem of real-time conflict detection at key intersections. According to the actual taxiing procedures and airport control rules in China, this paper focus on abstracting the mixed motion process of aircraft and vehicles in the maneuvering area, defining the convergent cross-safety operation scenario. To quantify the driver’s attention to safety separation and the degree of conservatism in adjusting speed, the vehicle deceleration rate and acceleration rate are defined with α as the exponent. Under the same spacing, the vehicle deceleration rate is directly proportional to α, and α is named the vehicle safety sensitivity. At the same time, the rules of speed change of vehicles and aircraft will be designed, and a convergent operation model of aircraft and vehicles will be proposed. Based on real-time speed and separation dynamic assessment of safety and efficiency under different traffic strategies, the computer simulation results show that vehicle safety sensitivity and deceleration rules determine the sequence of vehicles and aircraft when they are passing through in the short term and can affect the proportion of mixed traffic flow in the long run. The safety probability of vehicles passing the intersection first is negatively correlated with vehicle safety sensitivity, while the safety probability of aircraft passing the intersection first is correlated positively with vehicle safety sensitivity. The efficiency of passing without conflict with mixed traffic has an inverse relationship with vehicle safety sensitivity. When the vehicle safety sensitivity takes the value in the interval [0.4, 0.6], a mixed traffic flow with higher safety and efficiency, better stability, and a balanced ratio of locomotives can be obtained.

Multiobjective Gate Assignment Model Considering Carbon Emissions

It has been a main concern for governments to reduce the carbon emission of the aviation industry. The paper proposes a multiobjective gate assignment model that considers the carbon emission at the airport surface to facilitate environmental-friendly airport construction. Three objectives are considered in the model to reduce carbon emissions, including the proportion of flights assigned to the contact gate, aircraft taxiing fuel consumption, and gate assignment robustness. In order to achieve better performance on all objectives, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to obtain the optimal results. The operation data from a domestic airport is deployed to validate the model. The optimal results of the gate assignment model are compared with the original scheme. It indicates that the proposed model can effectively reduce carbon emissions. The study can provide a strategy for gate assignment to reduce carbon emissions and improve the management of the airport.

LRF-SRNet: Large-Scale Super-Resolution Network for Estimating Aircraft Pose on the Airport Surface.

The introduction of various deep neural network architectures has greatly advanced aircraft pose estimation using high-resolution images. However, realistic airport surface monitors typically take low-resolution (LR) images, and the results of the aircraft pose estimation are far from being accurate enough to be considered acceptable because of long-range capture. To fill this gap, we propose a brand-new, end-to-end low-resolution aircraft pose estimate network (LRF-SRNet) to address the problem of estimating the pose of poor-quality airport surface surveillance aircraft images. The method successfully combines the pose estimation method with the super-resolution (SR) technique. Specifically, to reconstruct high-resolution aircraft images, a super-resolution network (SRNet) is created. In addition, an essential component termed the large receptive field block (LRF block) helps estimate the aircraft's pose. By broadening the neural network's receptive field, it enables the perception of the aircraft's structure. Experimental results demonstrate that, on the airport surface surveillance dataset, our method performs significantly better than the most widely used baseline methods, with AP exceeding Baseline and HRNet by 3.1% and 4.5%.

Data-driven trajectory-based analysis and optimization of airport surface movement

Driven by the recent deployment of equipment and the availability of data, aircraft trajectories on the surface can be tracked continuously, and the detailed analysis of surface operations is enabled to measure airport operational performance and prepare for collaborative decision making. This paper proposes a hybrid approach combining traffic analysis and optimization to address the airport surface movement problem. Important metrics of surface management such as runway utilization, taxi routes, taxi times are extracted and characterized based on Automatic Dependent Surveillance-Broadcast (ADS-B) messages. After recognizing the congestion bottleneck, a trajectory-based optimization model is proposed, and an adapted simulated annealing heuristic is applied to solve the related problem. Two concepts for optimizing surface operations: arrival taxi reroute and departure controlled pushback are evaluated. The proposed approach is illustrated for Beijing Capital International airport. Computational experiments show that a mean taxi-in time reduction of 5.1 min is achieved for the rerouting arrivals, and the mean taxi-out time is reduced by 3.7 min with gate holding strategy. This data-driven approach allows advanced characterization of complex surface operations and enables collaborative decision assistance tools for managing surface movement efficiently.

A quick method of improving the load-bearing capacity of natural airport pavements

Abstract: The load-bearing capacity of natural airport surfaces directly affects the safety of air operations. Natural airport surfaces at military airports are front seat belts, work belts and side seat belts. The load-bearing capacity of the natural airport pavements must be high enough to prevent damage to the aircraft from the runway, damage to the underground infrastructure of the airport, and to enable quick restoration of the airport's operational capacity by efficient removal of the aircraft from the natural surface by airport services. That is why it is so important to have a quick and effective method of strengthening natural surfaces. The article proposes to improve the load-bearing capacity of natural airport pavements by using geogrids (airport gratings) pressed into the existing pavement. Keywords: Carrying capacity; Natural airport pavement; Safety Conducting air operations

INFLUENCE OF SODIUM AND POTASSIUM FORMATE ON THE ASR REACTIVITY OF GRANITE AGGREGATE

Use of de-icing agents is necessary in winter to maintain appropriate quality of road and airport surfaces. Formate or acetate de-icing agents are safer for aircraft and the environment than the commonly used sodium chloride, but may cause an alkali-silica reaction in concrete. The study investigated the influence of sodium formate and potassium formate on the occurrence of ASR (alkali-silica reaction) in mortars with granite aggregate. Accelerated mortar-bar tests at 80°C using various de-icing agents were performed and detailed studies of the SEM-EDS microstructure were carried out.