Baiyun International Airport Research Articles

Optimization of multi-path approach flight sequencing based on rolling horizon control

Arrival flight sequencing optimization is an effective method to improve the landing efficiency of incoming flights and reduce flight delays. Based on this, with the goal of maximizing landing efficiency, combined with multi-runway and multi-route selection, and considering the actual waypoint restrictions, a mixed integer programming model for multi-path and multi-runway integrated arrival flight sequencing optimization is proposed. In order to solve the real-time problem of large-scale flight sequencing calculation, a multi-waypoint rolling horizon control algorithm is proposed. The terminal area of Guangzhou Baiyun International Airport is used as an example for verification, and the actual arrival flight data is used to carry out calculation experiments. In terms of wake turbulence safety interval, the RECAT-CN operation standard is used. The calculation results show that when the number of small-scale flights (23 flights) is small, the maximum landing time of the proposed model is 55 s earlier than the first-come-first-served method and 271 s earlier than the non-optimized one; when the number of large-scale flights (104 flights) is large, the solver alone cannot find a feasible solution within 3600 s, and the proposed algorithm finds a solution within 128.65 s. The proposed model and algorithm are effective and can be applied to actual flight scheduling optimization.

Research on the Green Transition Path of Airport Development under the Mechanism of Tripartite Evolutionary Game Model

Since existing studies primarily explore green development measures from the static perspective of a single airport stakeholder, this paper constructs an evolutionary game model to analyze the strategic choices of three key stakeholders: airport authorities, third-party organizations, and government departments, based on evolutionary game theory. By solving the stable strategy of the tripartite evolution using the Jacobian matrix, the green transition of airport development can be divided into three stages: “initiation”, “development”, and “maturity”, allowing for the exploration of key factors influencing the green transition of airport development. A simulation analysis is conducted based on real Guangzhou Baiyun International Airport data. The results indicate that the tripartite evolutionary game strategy is stable at E4(0,0,1) and the green transition of Baiyun Airport remains in the development stage. By improving the reward and punishment mechanisms of government departments, the evolutionary game strategy can be stabilized at E8(1,1,1), promoting the green transition of airport development toward the mature stage. By adjusting the game parameters, the dynamic process of green transition in airports at different levels of development and under varying regulatory environments can be effectively captured, supporting the precise formulation of corresponding policies.

A Novel Aircraft Trajectory Generation Method Embedded with Data Mining

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability.

Improved Data-Driven Trajectory Optimization Method Utilizing Deep Trajectory Generation

The imbalance between air traffic capacity and demand, especially in the terminal maneuvering area, constrains the development of the civil aviation industry. To enhance the capacity, providing aircraft with optimal trajectories is the way forward. The key challenge of building a trajectory optimization model lies in embedding air traffic controllers’ operation experience into the optimization process. Previous efforts proposed a data-driven trajectory optimization method to learn operation experience from historical data for trajectory optimization use, which is limited by the issues of insufficient quantity and diversity of trajectory data. To solve those issues, an improved model is proposed to further improve the trajectory optimization performance. Firstly, this paper exploited a connecting-based trajectory generation model to generate massive synthetic trajectories. Then, this paper used a data-mining-based valuable trajectory identification technique to find valuable trajectories that can be used to enrich its operation experience. A case study on Guangzhou Baiyun International Airport was conducted to verify the proposed method. The results show that the proposed model successfully incorporates more operation experience during the optimization process. Compared with the benchmark model, the proposed model can adapt to more complex air traffic and provide optimal trajectories for more incoming aircraft.

A Decision Support Framework for Aircraft Arrival Scheduling and Trajectory Optimization in Terminal Maneuvering Areas

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft.

Spatial modelling of street-level carbon emissions with multi-source open data: A case study of Guangzhou

A refined spatial understanding of carbon emissions is crucial for advancing low-carbon development. This study aims to develop a comprehensive, open data-based approach for spatial modelling of carbon emissions at street level, covering five sectors: industry, transportation, residential & public service, commerce, and agriculture in Guangzhou. Two sets of open data, including statistical yearbook data and urban morphology data, were analyzed using a comprehensive methodology that integrates both bottom-up and top-down approaches to map the spatial distribution of carbon emissions. The findings delineate the carbon emission hierarchy across five distinct sectors as follows: Industrial (37.9%), Transportation (31.3%), Residential (18.6%), Commercial and Public Services (11.6%), and Agriculture (0.6%). The industrial sector emerges as the largest contributor, emitting 61.59 million tons, chiefly situated in suburban industrial zones like Huangpu and Panyu. Following closely is transportation, emitting 50.87 million tons, concentrated around Baiyun International Airport, ports, and urban areas with heavy traffic. Commercial and residential sectors emit 18.94 million tons, primarily within densely populated areas such as Tianhe and Haizhu. Agricultural emissions total 1.02 million tons, predominantly located on the city's outskirts, notably in Nansha. The findings of this study could provide information support for identifying carbon emission hotspots and developing sector-specific low-carbon urban planning strategies.

The synergistic evolution of supply-demand composite system for airport green development: A case study in Guangzhou Baiyun International Airport, China.

Given the pressing requirements for sustainable development in civil aviation, conducting a synergistic evolution analysis of the supply and demand aspects in the airport green development holds great significance. This analysis helps achieve sustainable airport development and facilitates the green transformation of civil aviation development. Taking a collaborative learning approach and utilizing historical data from Guangzhou Baiyun International Airport spanning 2008 to 2019, the supply-demand composite system for airport green development was deconstructed into two subsystems-demand and supply-and relevant evaluation index systems were established in this paper. A screening and optimization model of supply and demand synergy indicators for airport green development was constructed, and it was solved using a simulated annealing genetic algorithm. The Haken model was constructed to analyze the synergistic evolutionary relationship of the composite system of supply and demand for green airport development in two stages. The results indicate a shift in the order parameter of the co-evolution of the supply-demand composite system at Guangzhou Baiyun International Airport, moving from the demand subsystem in the first stage (2008-2015) to the supply subsystem in the second stage (2016-2019). The co-evolution of the airport supply-demand composite system has entered a new stage, but has not reached a high level of synergy. The study not only contributes theoretically by explaining the interaction mechanism between supply and demand for airport green development, but also offers targeted suggestions for achieving high-quality synergistic evolution of supply and demand for airport green development.

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).

Medium- and Long-Term Prediction of Airport Carbon Emissions under Uncertain Conditions Based on the LEAP Model

As important nodes in the air transport system, it is of great significance for airports to achieve the carbon-peaking goal before 2030 under the target of peaking carbon emissions in China’s civil aviation industry. However, it remains unknown whether airports will be able to realize this ambitious goal due to a variety of uncertain factors, such as the social economy, epidemic impact, and emission reduction measures. According to the possibilities of uncertain factors, 12 uncertain scenarios were constructed. Using the case of Guangzhou Baiyun International Airport (CAN), this study predicted medium- and long-term carbon emission trends under 12 uncertain scenarios based on the Long-range Energy Alternatives Planning System (LEAP) model. Furthermore, the effects of carbon abatement measures and emission reduction responsibilities were analyzed. The results show that CAN cannot guarantee that it will realize the goal under the established abatement policy. If socioeconomic development is rapid, carbon emissions will peak at about 90 kt tons in 2030, and if socioeconomic development is slow, it will plateau at about 1 million tons between 2030 and 2035. What is more, airlines bear the greatest responsibility for reducing emissions, and technological progress measures have the highest abatement potential. This study provides decision support for airport stakeholders in abatement work so as to ensure that airports can achieve the carbon-peaking goal.

Emerging and legacy perfluoroalkyl and polyfluoroalkyl substances (PFAS) in surface water around three international airports in China

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a large category of crucial environmental contaminants of global concerns. There are limited data on PFAS in surface water around international airports in China. The present study investigated the concentrations, distributions, and sources of emerging and legacy PFAS in surface waters around Beijing Capital International Airport (BC), Shanghai Pudong International Airport (SP), and Guangzhou Baiyun International Airport (GB) in China. Twenty-seven target compounds were quantified. The Σ27PFAS concentrations ranged from 19.0 to 62.8 ng/L (mean 36.1 ng/L) in BC, 25.6–342 ng/L (mean 76.0 ng/L) in SP, 7.35–72.7 ng/L (mean 21.6 ng/L) in GB. The dominant compound was perfluorooctanoic acid (PFOA), which accounted for an average of 27% (5%–65%) of the Σ27PFAS concentrations. The alternatives with –C6F12- group had detection frequencies ranging from 72% to 100%. The partition coefficient results indicate that the longer chain PFAS (C > 8) tend to be more distributed in the particle phase. Fifty suspect and nontarget PFAS were identified. In GB, 44 PFAS were identified, more than SP of 39 and BC of 38. An ultra short-chain (C = 2) precursor, N-methylperfluoroethanesulfonamido acetic acid (MeFEtSAA), was identified and semi-quantified. Domestic wastewater discharges might be the main sources around BC, while industrial and aviation activities might be the main sources around SP and GB.

Disease Simulation in Airport Scenario Based on Individual Mobility Model

As the rapid-spreading disease COVID-19 occupies the world, most governments adopt strict control policies to alleviate the impact of the virus. These policies successfully reduced the prevalence and delayed the epidemic peak, while they are also associated with high economic and social costs. To bridge the microscopic epidemic transmission patterns and control policies, simulation systems play an important role. In this work, we propose an agent-based disease simulator for indoor public spaces, which contribute to most of the transmission in cities. As an example, we study Guangzhou Baiyun International Airport, which is one of the most bustling aviation hubs in China. Specifically, we design a high-efficiency mobility generation module to reconstruct the individual trajectories considering both lingering behavior and crowd mobility, which greatly enhances the credibility of the simulated mobility and ensures real-time performance. Based on the individual trajectories, we propose a multi-path disease transmission module optimized for indoor public spaces, which includes three main transmission paths as close contact transmission, aerosol transmission, and object surface transmission. We design a novel convolution-based algorithm to mimic the diffusion process, which can leverage the high concurrent capability of the graphics processing unit to accelerate the simulation process. Leveraging our simulation paradigm, the effectiveness of common policy interventions can be quantitatively evaluated. For mobility interventions, we find that lingering control is the most effective mobility intervention with 32.35% fewer infections, while increasing social distance and increasing walking speed have a similar effect with 15.15% and 18.02% fewer infections. It demonstrates the importance of introducing crowd mobility into disease transmission simulation. For transmission processes, we find the aerosol transmission involves in 99.99% of transmission, which highlights the importance of ventilation in indoor public spaces. Our simulation also demonstrates that without strict entrance detection to identify the input infections, only performing frequent disinfection cannot achieve desirable epidemic outcomes. Based on our simulation paradigm, we can shed light on better policy designs that achieve a good balance between disease spreading control and social costs.

Data-Driven Departure Flight Time Prediction Based on Feature Construction and Ensemble Learning

Temporal–spatial resource optimization within the terminal maneuvering area has become an important research direction to meet the growing demand for air traffic. Accurate departure flight time prediction from taking off to the metering fixes is critical for departure management, connecting the surface operations, and overhead stream insertion. This paper employs ensemble learning methods (including bagging, boosting, and stacking) to predict departure flight times via different metering fixes based on four feature categories: initial states, operating situation, traffic demand, and wind velocity. The stacking method employs a linear regressor, a support vector regressor, and a tree-based ensemble regressor as base learners. The Guangzhou Baiyun International Airport case study shows that the stacking method proposed in this work outperforms other methods and could achieve satisfactory performance in departure flight time prediction, with a high prediction accuracy of up to 89% within a 1 min absolute error and 98% within a 2 min absolute error. Besides, the affecting factors analysis indicates that the operation direction, flight distance, and traffic demand in different areas significantly improve prediction accuracy.

Passenger spatiotemporal distribution prediction in airport terminals based on insect intelligent building architecture and its contribution to fresh air energy saving

As the essential infrastructure and important transportation hubs, airport terminals play a strategic role in sustainable cities and urbanization. Due to the strict service requirement and round-the-clock operation, the high energy consumption poses unprecedented challenges to developing environment-friendly airport terminals. Actually, passenger flow as a root cause decides the startup and operation of service facilities including air-conditioning systems. But due to spatial connectivity, parameter coupling, and passenger mobility, resource allocation and system tuning have not been effectively related to passenger travel. In this work, Guangzhou Baiyun International Airport Terminal 2 was taken as an object, and a passenger spatiotemporal distribution model was developed and its contribution to fresh air energy saving was estimated. Among them, zone partition and expression rules were formulated based on insect intelligent building (I2B) architecture to construct Wi-Fi positioning system; Next, for domestic and international passenger, their arrival patterns were fitted by Chi-Square distribution with R2 > 0.993, and their spatiotemporal distribution was predicted with R2 over 0.74 and 0.67 respectively; Lastly, a hybrid reset strategy for fresh air volume set-point combing fuzzy and linear rules was proposed with results showing that HVAC systems running on variable occupancy would lead to considerable energy savings.

Investigation and Evaluation of Summer Vegetation in Guangzhou Baiyun International Airport

The bird ecological chain can be broken by the ecological environment management of the airport and its surrounding areas; it is an effective method for bird strike control. Based on a comprehensive survey of plant populations in Baiyun Airport, it can be seen that there are 68 dominant plant species that are more attractive to birds inside and outside Baiyun Airport. The K-means clustering algorithm is used to cluster the 68 plants in MATLAB. The analysis results show that these 68 plants can be divided into three categories according to their comprehensive attraction to birds. The airport authorities can invest different management resources and adopt different management methods for the three types of plants with different risk, so as to minimize the arrival of birds at the airport.

Aircraft Noise Reduction Strategies and Analysis of the Effects.

In this study, six aircraft noise reduction strategies including the optimization of aircraft type, regulation of night flight number, optimization of flight procedure, modification of operating runway, land use planning and installation of sound insulation windows were proposed to alleviate the harmful impact of aircraft noise on the local area and population near Guangzhou Baiyun International Airport (BIA) in China. The effects of all proposed strategies except for land use planning and sound insulation windows were simulated and analyzed using CadnaA software. The results indicate that these noise reduction strategies have their own advantages and each of them can serve as an effective noise reduction measure for different applications. For instance, the replacement of noisy aircraft with low-noise aircraft can simultaneously reduce the area and population exposed to a high noise level, while the optimization of flight procedure can only reduce the population exposed under relatively low noise levels (70 ≤LWECPN ≤ 75 dB). Nevertheless, the modification of operating runway is more effective in reducing the population suffering under high noise levels (LWECPN > 85 dB). Among these strategies, reducing the number of night flights is found to be most effective in reducing the overall noise-exposed area and population. Additionally, with the assistance of noise mapping, proper land use planning was suggested according to national standards, and the installation of sound insulation windows with different sound reduction grades can be determined for different areas impacted by the aircraft noise of BIA. It is believed that the results of this study can be applied as a reference in selecting suitable noise reduction strategies to improve the acoustic environment of a specific airport.

Optimal aircraft arrival scheduling with continuous descent operations in busy terminal maneuvering areas

Continuous Descent Operation (CDO), an aircraft arrival procedure in which an aircraft descends from an optimal position with minimum engine thrust, can significantly reduce aircraft noise, fuel burn and emissions. The implementation of CDOs, however, requires more separation buffers among flights than traditional stepwise descents, resulting in loss of airspace and runway capacity. It is therefore challenging to maximize the number of aircraft performing CDOs for an airport, especially in busy terminal maneuvering areas. In this paper, we tackle the difficulty by developing an aircraft arrival scheduling model to sequence arrival aircraft before their merge point and generate conflict-free trajectories by means of a lateral path stretching method. The contributions of our study to the research community are twofold: firstly, we investigate a new aircraft arrival scheduling problem that incorporates both the CDO trajectory optimization and aircraft scheduling, and develop the first model simultaneously considering these two procedures; secondly, we conduct extensive computational experiments based on historical data in Guangzhou Baiyun International Airport, which would provide an important reference for future research in the area. Our numerical results demonstrate the benefit of the proposed CDO-based aircraft arrival scheduling approach in terms of reducing total flight time and saving fuel consumption when applied in this congested airport.

Establishing the Correlation Between Complexity and Performance for Arrival Operations

Air traffic complexity indicators play an essential role in measuring operational performance and controller workload. However, current studies mainly depend on the manual scoring method to scale performance or workload. This paper focuses on arrival operations and presents a data-driven strategy to establish the correlation between complexity and performance to avoid the subjectivity of the currently used manual scoring method. Firstly, we present twenty-six indicators for describing air traffic complexity and two indicators for arrival operational performance. Secondly, the clustering method distinguishes peak and off-peak situations for arrival operation. Moreover, clustering results are compared to investigate the correlation between complexity and performance initially. Thirdly, the classification method is adopted to determine such correlation further. In addition, we also identify the affecting factors which could influence operational performance. Finally, trajectories of arrival aircraft landing at Guangzhou Baiyun International Airport (ZGGG) are used for case validation. The results indicate that there is a strong correlation between complexity and performance. The accuracy and precision of classification are approximately 90%. Furthermore, the number of aircraft significantly impacts the arrival operational performance within TMA.

Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

Predicting flight delays has been a major research topic in the past few decades. Various machine learning algorithms have been used to predict flight delays in short-range horizons (e.g., a few hours or days prior to operation). Airlines have to develop flight schedules several months in advance; thus, predicting flight delays at the strategic stage is critical for airport slot allocation and airlines’ operation. However, less work has been dedicated to predicting flight delays at the strategic phase. This paper proposes machine learning methods to predict the distributions of delays. Three metrics are developed to evaluate the performance of the algorithms. Empirical data from Guangzhou Baiyun International Airport are used to validate the methods. Computational results show that the prediction accuracy of departure delay at the 0.65 confidence level and the arrival delay at the 0.50 confidence level can reach 0.80 without the input of ATFM delay. Our work provides an alternative tool for airports and airlines managers for estimating flight delays at the strategic phase.

A data-driven trajectory optimization framework for terminal maneuvering area operations

The contradiction between air traffic capacity and demand constrains the development of the civil aviation industry. This paper proposes a Data-Driven Trajectory Optimization Framework for Terminal Maneuvering Area operations (DDTO-TMA) to enhance the capacity of Terminal Maneuvering Area (TMA). First, a Wasserstein-distance-based spectral clustering algorithm is applied to divide historical trajectories into several groups based on flight performance. Then, based on clustering results, a set of candidate Four-Dimensional Trajectories (4DTs) is created via flyable trajectories expansion. Finally, a combined algorithm with Mahalanobis-distance-based conflict detection and Pareto frontier determination is designed to identify optimal trajectories from candidate 4DTs in an efficient way. Two validations have been performed for Guangzhou Baiyun International Airport (ZGGG) TMA. The first validation evaluates the performance of the proposed DDTO-TMA. The results show that the DDTO-TMA can generate optimal trajectories that are not only of the interest of most stakeholders but also conflict-free. Besides, with the increase in the size of the historical trajectory set, the performance of DDTO-TMA gradually increases. The second validation estimates the improvement of a TMA operation after adopting DDTO-TMA. The results illustrate that, compared with the baseline, the proposed DDTO-TMA could enhance the arrival capacity by 30% without sacrificing stakeholders' interests. Furthermore, the proposed optimal trajectory generator can be easily transferred to different airports due to its data-driven characteristics.

Research on the Investment Value of Guangzhou Baiyun International Airport Based on Multiples Valuation Method

Aviation has been an industry that has been widely watched by investors. As the upstream of the civil aviation industry chain, the airport industry is an important core asset of the entire industry chain, providing relevant services for global air transport companies and passengers. In recent years, China's aviation industry has been in a state of steady development. Until the outbreak of COVID-19 in 2020, the market focus turned to the impact of the speed of passenger traffic recovery during the epidemic on the recovery of airport performance. For core hub airports, the priority is whether asset values have changed. This paper analyzes Guangzhou Baiyun International Airport in China, and studies whether the investment value of the airport is fair through financial analysis, peer comparison, and multiple valuation methods. The study found that the airport's overall revenue decline in the past two years was mainly due to the epidemic, but it has performed better compared to industry players. Through multiples valuation, the theoretical stock value of Baiyun Airport in 2021 is 16.19 CNY higher than the actual value. The result indicated that the company's stock was undervalued and has good room for growth in the future, suggesting a purchasing opportunity. It is hoped that the research in this paper can expand the research on related aspects of the private airport industry, and also provide some help and reference for the valuation research of Guangzhou Baiyun International Airport.