Airport Operational Efficiency Research Articles

Aircraft Taxi Path Optimization Considering Environmental Impacts Based on a Bilevel Spatial–Temporal Optimization Model

Aircraft taxiing emissions are the main source of carbon dioxide and other pollutant gas emissions during airport ground operations. It is crucial to optimize aircraft taxiing from both spatial and temporal perspectives to improve airport operation efficiency and reduce aviation emissions. In this paper, a bilevel spatial and temporal optimization model of aircraft taxiing is constructed. The upper-level model optimizes the aircraft taxiing path, and the lower-level model optimizes the taxiing start time of the aircraft. By the iterative optimization of the upper- and lower-level interactions, the aviation fuel consumption, flight waiting time, and number of taxiing conflicts are reduced. To improve the calculation accuracy, the depth-first search algorithm is utilized to generate the set of available paths for aircraft during the model solution process, and a model solution method based on the genetic algorithm is constructed. Simulation experiments using Tianjin Binhai International Airport as the research object show that adopting the waiting taxiing strategy can effectively avoid taxiing conflicts and reduce aviation fuel consumption by 753.18 kg and 188.84 kg compared to the available path sets generated using Dijkstra’s algorithm and those created manually based on experience, respectively. Conversely, adopting an immediate taxi-out strategy caused 54 taxiing conflicts and increased aviation fuel consumption by 49.44 kg. These results can provide safe and environmentally friendly taxiing strategies for the sustainable development of the air transportation industry.

Multi-Task Dynamic Spatio-Temporal Graph Attention Network: A Variable Taxi Time Prediction Model for Airport Surface Operation

Variable taxi time prediction is the core of the Airport Collaborative Decision Making (A-CDM) system. An accurate taxi time prediction contributes to enhancing airport operational efficiency, safety and predictability. The deep dynamic spatio-temporal correlation inherent in airport traffic data is critical for taxi time prediction. However, existing machine learning (deep learning) methods have been unable to thoroughly exploit these correlations. To address this issue, we propose a deep learning-based model called the multi-task dynamic spatio-temporal graph attention network (MT-DSTGAN). Our model also predicts future entire airport traffic flow and taxiing segment traffic flow as auxiliary tasks, with the goal of enhancing the accuracy of aircrafts’ taxi time prediction. The proposed MT-DSTGAN model is implemented and assessed through a case study of Beijing Capital International Airport with a real-world dataset. The advantage of the proposed model, which shows better performance in various evaluation metrics, is demonstrated in a comparative study with other baseline works. In summary, the proposed MT-DSTGAN exhibits promising capabilities in perceiving the dynamic changes in the taxiing process of aircraft and demonstrates the ability to capture complex spatio-temporal correlations in airport traffic data.

A CNN-GRU Hybrid Model for Predicting Airport Departure Taxiing Time

Scene taxiing time is an important indicator for assessing the operational efficiency of airports as well as green airports, and it is also a fundamental parameter in flight regularity statistics. The accurate prediction of taxiing time can help decision makers to further optimize flight pushback sequences and improve airport operational efficiency while increasing flight punctuality. In this paper, we propose a hybrid deep learning model for departure taxiing time prediction based on the new influence factors of taxiing time. Taking Pudong International Airport as the research object, after analyzing the scene operation mode, we construct the origin–destination pairs (ODPs) with stand groups and runways and then propose two structure-related factors, corridor departure flow and departure flow proportion of ODP, as the new features. Based on the new feature set, we construct a departure taxiing dataset for training the prediction model. Then, a departure taxiing time prediction model based on convolutional neural networks (CNNs) and gated recurrent units (GRUs) is proposed, which uses a CNN model to extract the high-dimensional features from the taxiing data and then inputs them to a GRU model for taxiing time prediction. Finally, we conduct a series of comparison experiments on the historical taxiing dataset of Pudong Airport. The prediction results show that the proposed hybrid prediction model has the best performances compared with other deep learning models, and the proposed structure-related features have high correlations with departure taxiing time. The prediction results of taxiing time for different ODPs also verify the generalizability of the proposed model.

Reliable and robust scheduling of airport operation resources by simulation optimization feedback and conflict resolution

Reliable and robust airport flight ground service resources collaborative scheduling has emerged as an issue of major concern at congested airports, which has great significance on airport service quality and operation efficiency. Due to the uncertainty of the service process of multiple vehicles in airport ground operations, the combination of simulation and optimization (sim-opt) techniques has been proven to be an effective means to solve the collaborative scheduling problem. However, existing models still have limitations on the reliability of feasible solutions and the robustness of evaluations. In this work, we developed a conflict resolution based sim-opt framework that combines the search capability of mathematical optimization models with the ability of simulation models to describe uncertainty. The dynamic window is introduced to divide the flight zone into multiple sub-intervals, which allows for iterative search and evaluation of the optimal schedule solution while reducing model calculation time. Additionally, a matching algorithm based on slack and tight constraints is proposed to optimize the possible resource requirements of each vehicle, thereby improving the initial schedule solution’s search depth and reliability. Furthermore, a conflict resolution feedback mechanism between adjacent windows is constructed to optimize the scheduling plan, reducing misjudgment and omission of the optimal solution and enhancing the sim-opt framework’s robustness. Finally, experiments on real airport datasets of three different scales demonstrate that our proposed method efficiently allocates resources with a smaller flight delay time as well as reducing total vehicle cost time during the flight ground service process.

Design Automatic Air Conditioning System Control with Passive Infrared Receiver/motion sensor

Dr. Ferdinand Lumbantobing Airport faces serious challenges related to the waste of electrical energy in its operations. The quarterly report shows an inconsistent increase in electricity usage, which is largely due to human factors and some technical aspects. The biggest problem is the use of air conditioning and lights that do not need to be constantly active. This article takes a close look at the airport's efforts to address this waste of energy, focusing on the use of air conditioning and constantly burning lights in unused rooms. Measures such as monitoring, automation, sensors, employee extension, and smart appliance development are proposed to address these issues and achieve significant energy savings. This solution will help reduce airport operational costs, increase efficiency, and support environmental conservation.

Performance optimization design of wide-body aircraft pressure refueling system

In order to reduce the wide body aircraft refueling process time-cost and improve the airport operation efficiency, pressure refueling system design strategy was innovated. The shutoff valves’ operation sequence in refueling process was optimized. The diameter of the orifices on the pressure refueling system fuel line was designed. Pressure refueling performance was simulated and analyzed at different scenarios. The results show that it can improve the pressure refueling system performance at different scenarios by optimizing the pressure refueling process strategy design including optimizing the fuel line orifice design and optimizing the wing tank inner side shut-off valve operation sequence.

Design of an aircraft autonomous traction taxiing system based on hydraulic secondary control

At present, aircraft taxiing at ground airports needs to be provided with a thrust by the main engine. The taxiing process is inefficient, has high fuel consumption and serious pollution, and is prone to safety risks. In this paper, a new configuration of aircraft autonomous traction taxiing system is proposed based on the principle of hydraulic secondary control, in which a hydraulic motor drive device is installed at the front wheels of the aircraft to drive the wheels to rotate forward or backward. Based on this, autonomous taxiing can be realized without relying on the main engines, thus greatly improving airport operation efficiency. Meanwhile, this paper analyzes the influencing factors of the autonomous traction taxiing process, and investigates the parameter matching design of the new configuration system. Besides, this paper develops the ground principle prototype, designs the aircraft longitudinal bonding force observer and the aircraft wheel disturbance moment observer, and proposes the speed control method of the aircraft front wheel autonomous traction taxiing by considering the ground bonding force saturation characteristics. Finally, the ground taxiing test is conducted, and the results show that the new configuration proposed in this paper presents a new solution for aircraft autonomous traction taxiing.

Simulation as a tool for calculating the airports’ runway capacity

The basic parameter for evaluating the efficiency of airport operations is capacity. Investment planning in the aviation industry is a complex process, requiring consideration of many factors and a combination of different aspects. The aim of the study is to present the possibility of using discrete event simulation as a tool for determining the capacity of runways at airports. The main research problem of the article is: How to build a simulation model for determining the capacity of an airport runway using FlexSim software? An attempt was made to build simulation model, the purpose of which is to carry out simulation analyzes of airport capacity, and in particular of the runway. Simulation analyzes were performed using the FlexSim 3D Simulation software. The article discusses in detail the operating principle of the model and presents the results of numerical tests carried out to calculate the capacity of runways and taxiways at airports. The constructed simulation model of the airport capacity in the FlexSim environment enables to determine the airport’s runway capacity, constituting valuable information to support modernization decisions. The model may contribute to the efficient adjustment of the runway capacity. It should be noted that the creation of an effective system dedicated to a given airport will improve the air traffic control carried out by the system based on calculated maximum hourly runway capacity.

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.

Fast response flight multi-service nodes prediction based on one hidden layer neural network

Airport scene multi-service process(ASMP) state prediction is one of the important links to optimize airport ground resource allocation, improve collaborative release scheduling, and improve airport operation efficiency. In order to solve the problem of the slow response of existing prediction models, a Gaussian kernel probability density model of multi-service nodes in airport scenes is established. Then the one hidden layer neural network(OHLNN) is used to predict the nodes of ASMP. A study on the data of more than 20,000 flights at a large airport in China shows that: The mean absolute error (MAE) of the mixed flight prediction result is only 2.62911min, the mean root mean square error (RMSE) is only 3.24564min, and the response speed of the prediction model in the verification set is about 0.48s. When the results of MAE and RMSE are good, the response speed of the prediction model is greatly improved.

Exploring Delay Propagation Causality in Various Airport Networks with Attention-Weighted Recurrent Graph Convolution Method

Exploring the delay causality between airports and comparing the delay propagation patterns across different airport networks is critical to better understand delay propagation mechanisms and provide effective delay mitigation strategies. A novel attention-based recurrent graph convolutional neural network is proposed to identify the hidden delay causality relationship among airports in three different airport networks of China. The selected three airport networks show great heterogeneities in topological characteristics, such as average intensity, modularity and eigenvector centrality. The modeling results indicate that the identified delay causality networks of three airport networks are greatly varied in terms of complexity, delay propagation distance and efficiency. Moreover, the delay state of each airport is categorized into three levels, and the delay state transition of the three networks is explored. The results indicate that delay state transition in the North China Control Area exhibits an obvious bidirectional transition form that mainly propagates between the large-degree airports and small-degree airports, while severe delays of some hub airports account for a relatively large proportion in the other two networks. The results of this study could better reveal the delay propagation mechanism among airports and help airport operators develop effective strategies to alleviate flight delays and improve airport operation efficiency.

Comparison of logistic regression and artificial neural network model for apron allocation assignment

Management of the parking apron is one of the most essential airport ground service operations for flight operations to run smoothly. Effective airport ground service management will have a direct effect on the cost and duration of flights. Therefore, in this paper, we address the issue of using machine learning techniques, such as logistic regression analysis and artificial neural network (ANNs) models, for classified targets of stand locations assignment of an arriving flight. Also, this could assist ground controllers to assign apron allocation and improve the efficiency and predictability of airport operations which reduce the time required for airport ground processing to increase flight capacity. In order to evaluate the performance of the proposed method, simulation results reveal that ANN has the lowest error rate and the highest accuracy. Therefore, ANN is the effective classification technique for this data set.

Cost–Benefit Analysis of Investments in Air Traffic Management Infrastructures: A Behavioral Economics Approach

An important and challenging question for airport operators is the management of airport capacity and demand. Airport capacity depends on the available infrastructure, external factors, and operating procedures. Investments in Air Traffic Management (ATM) infrastructures mainly affect airside operations and include operational enhancements to improve the efficiency, reliability, and sustainability of airport operations. Therefore, they help increase capacity while limiting the impact on the airport infrastructure itself. By reviewing the neoclassical valuation principles for Cost–Benefit Analysis (CBA), we find that it does not consider relevant behavioral economic challenges to conventional analysis, particularly: failure of the expected utility hypotheses, dependence of valuations on reference points, and time inconsistency. These challenges are then incorporated through practical guidelines into the traditional welfare model to achieve a new methodology. We propose a novel CBA behavioral framework for investments in ATM infrastructures to help policy makers and airport operators when faced with a capacity development decision. This is complemented with a practical example to illustrate and test the applicability of the proposed model. The case study evaluates the deployment of Automatic Dependent Surveillance–Broadcast (ADS–B) as an investment aimed at improving ATM operational procedures in the airport environment by providing advanced ground surveillance data. This allows airport operators to discover the causes of taxi congestion and safety hotspots on the airport airside. The benefits of ADS–B are related to enhanced flight efficiency, reduced environmental impact, increased airport throughput, and improved operational predictability and flexibility, thus reducing waiting times. At the airport level, reducing the waiting times of aircraft on the ground would lead to a capacity release and a reduction in delays. The results show that, following a traditional CBA, the investment is clearly viable, with a strong economic return. Including behavioral notions allows us to propose a new evaluation framework that complements this conclusion with a model that also considers inconsistencies in time and risk perception. A positive Net Present Value can turn into a negative prospect valuation, if diminishing sensitivity and loss aversion are considered. This explains the reticent behavior of decision makers toward projects that require robust investments in the short-term, yet are slow to generate positive cash flows. Finally, we draw conclusions to inform policy makers about the effects of adopting a behavioral approach when evaluating ATM investments.

Assessing the dynamic efficiency and technology gap of airports under different ownerships: A union dynamic NDEA approach

Airport performance assessment is important to a variety of stakeholders. It is particularly relevant to measure airport operational efficiency and the ways in which it can be improved through measurement can be undertaken by examining the relationship between airport ownership and operational efficiency. In the present study, a new model called the union dynamic network data envelopment analysis model has been propounded and has been employed to analyse the dynamic efficiency and changes of technology gaps of 50 European airports with different ownerships in the period 2011–2017 within the meta-frontier framework. The results demonstrate that over the years, even though the dynamic meta efficiency of private majority airports has been the best in both production and sales stages of operation, public corporation airports have been technology leaders amongst other groups of airports. The findings give direction as to what aspects the airport managers and practitioners need to focus on so as to undertake airport performance improvement.

Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network

The occurrence of wake can pose a threat to the flight safety of aircraft and affect the runway capacity and airport operation efficiency. To effectively identify aircraft wake, this paper proposes a novel convolutional neural network (CNN) method of aircraft wake recognition based on the improved parallel network (ParNet). Depthwise separable convolution (DSC) was introduced into the ParNet to make the wake recognition model lightweight. In addition, the convolutional block attention module (CBAM) was introduced into the wake recognition model to improve the capacity of the model to extract the spatial features of the wind field. The proposed aircraft wake recognition method was used to identify the aircraft wake based on the lidar wind field scanning image of Hong Kong International Airport. The best wake recognition effect was obtained with a recognition accuracy of 98.91% and an F1 value of 98.90%. As the number of parameters of the model was only 0.46 M, the aircraft wake could be identified on an ordinary computer. Thus, the proposed method can effectively identify aircraft wake.

Comprehensive operating efficiency measurement of 28 Chinese airports using a two-stage DEA-Tobit method

<abstract> <p>This paper presents a two-stage method combining data envelopment analysis (DEA) and a Tobit model to analyze the comprehensive operating efficiency of 28 airports in China in 2016. At the first stage, the DEA-BCC (Banker-Charnes-Cooper) model was employed to obtain the comprehensive operating efficiency of the combination of flight departure punctuality, non-cancellations, landing bridge rates from the perspective of airport infrastructure, surrounding airspace, route layouts, flight volume and weather. At the second stage, a Tobit model was used to analyze the influence of nine input variables from four aspects on obtained comprehensive operating efficiency, ultimately providing a clear and straightforward basis for formulating and testing policies. The comprehensive operating efficiency with this combination was further compared with each of the three efficiencies respectively. The important findings included the following: (1) The comprehensive operation efficiencies of most airports were greater than the individual efficiency; (2) These four types of operation efficiencies for most airports did not achieved DEA validity (100% efficiency), except for six airports (i.e., Haikou, Dalian, Jinan, Fuzhou, Nanning and Lanzhou); (3) These factors affecting each of the four types of operation efficiencies were different in that the number of terminals, duration of impact and average daily inbound and outbound flights had a negative impact on airport operational efficiency, while the average number of overnight aircraft per day and peak hour sorties had positive effects.</p> </abstract>

Corruption and airport efficiency: A study on Asian airports

This paper evaluates the relationship between corruption and operational efficiency of airports in Asia, by analyzing a panel dataset of 26 Asian airports over the 2003–2019 period. Our estimation results for Asia confirm the findings for other regions that there exists a negative association between the operational efficiencies of airports and the corruption level in the country. Airports owned, operated and/or managed by government corporations (100% government) are the most affected, in that they experience significant loss of efficiency when located in a high corruption country. The residual variable factor productivity (RVFP) from the Air Transport Research Society (ATRS) airport benchmarking reports is used as measure of airport operational efficiency. Regression analyses are applied to evaluate the relationship between airport efficiency and corruption. The results are robust to alternative corruption indexes. The findings of this paper have important policy implications for the management of large public infrastructure projects and airport governance reforms in Asia.

Approach and Landing Aircraft Wake Encounter Risk Based on Reynolds-Averaged Navier-Stokes Numerical Simulation

In order to shorten the wake safe separation, numerical simulation technology with aerodynamic response models and strip models have been combined to calculate wake hazard zone. As a realistic case, a medium aircraft ARJ21 following a heavy aircraft A330-200 is considered, and the Reynolds-averaged Navier-Stokes (RANS) method is used to explore the wake vortex evolution process of the leading aircraft at the decision height. A strip algorithm is proposed to calculate the rolling moment coefficient and overload increment of the ARJ21 after encountering the wake of the front aircraft in the three-dimensional space. The proposed algorithm identifies the area where the wake of the front aircraft can cause risks to the following aircraft and analyzes the evolution process of the hazard zone of the section where the decision height is located. The minimum safe separation of the ARJ21 following the A330-200 is 1.32 nmile, which is 26.4% of the ICAO separation standard of 5 nmile. When the average runway occupancy time (ROT) is reduced to match the separation of 1.32 nmile, the capacity of runway 02R/20L in Tianfu International Airport could theoretically reach 102.37 sorties/h under this aircraft pair combination. Compared to original 27 sorties/h, the runway capacity can be improved up to 279.14%, which will increase the airport operation efficiency.

Study on non-aeronautical efficiency of Chinese listed airports based on two-stage network DEA

This study evaluates airport non-aeronautical efficiency from 2016 to 2019 with statistical data to help promote airport sustainable development. Seven Chinese listed airports (PEK, PVG, CAN, SZX, XMN, HAK, and HKG, HKG is quasi-listed) are studied. Airport efficiency is evaluated from process level (i.e., operational process providing aeronautical services and financial process providing non-aeronautical services) to airport level by the network DEA (NDEA) method and the key influencing factors of overall efficiency are identified by employing the Panel Data model. The first-stage NDEA results indicate that HKG is the most efficient among the sample airports for the entire study period. There was an obvious observed increasing trend of average airport operational efficiency (except HKG) from 2016 to 2019, and the scores (average from 0.8724, 0.8928, 0.8953 to 0.8999) were relatively high. This study classifies relatively operationally efficient airports (HAK, XMN, PEK, HKG, and SZX) and financially efficient airports (HKG, and HAK). The operational efficiency of mainland Chinese airports is higher than the financial efficiency, but most airports' non-aeronautical efficiency improves year by year, such as PEK, PVG. Proportion of non-aeronautical revenue of PEK in 2019 has achieved 62.18%, PVG 62.69%, both are similar with HKG 63.70% in 2017. The second-stage regression analysis implies that the airport service quality, airport economic development level, and airport airside level have a significant positive impact on airport efficiency, while the distance from the airport to the city center has a negative impact on airport efficiency. This study helps airport management to evaluate airport financial development strategies by assessing airport non-aeronautical revenue.

Estimate Spatial Spillover of Airport Operational Efficiency in the YRD Region

Accurate measurement of airport operational efficiency and the analysis of key influencing factors can provide theoretical references for regional airport planning and air traffic management. Many studies are conducted on the operational efficiency of airports in the region, but little attention is paid to their interactions. To fill this gap, this paper measures the operational efficiency of airports in four major cities in the Yangtze River Delta (YRD) region, and on this basis, the spatial Durbin model is used to explore the influencing factors and spillover effects of airport operational efficiency based on two aspects: airport physical characteristics and regional characteristics. The study demonstrates that the efficiency of airport operations has a significant negative spillover effect, indicating that the efficiency of neighboring airports evolves in a competitive interaction. In terms of direct effects, the number of flights, the number of destinations, airport capacity utilization and GDP are important factors affecting airport efficiency. In terms of spillover effects, this paper found that the population and income positively affect the efficiency of local airport operations, while the number of flights and airport capacity utilization effects have negative effects.