International Airport Research Articles

Joint runway–gate assignment based on the Branch-and-Price algorithm

Airport runway assignment and gate assignment are both crucial problems for large multirunway airports. A cooperative schedule for the runway and gate can have a significant effect on airport ground operation efficiency and safety. This paper focuses on the joint assignment problem of runways and gates considering parking time differences and airline preferences. A quadratic model is established to reduce taxiing fuel consumption loss and increase the robustness of gate assignment. An improved branch-and-price algorithm is proposed by utilizing an improved pulse algorithm and various acceleration strategies to improve the solution performance. The actual flight data at an international hub airport are utilized to validate the proposed model. The results indicate that the joint assignment model can effectively reduce ground taxiing loss, optimize gate assignment robustness, and increase the utilization rate of contact gates. The proposed algorithm also achieves better computational accuracy and efficiency.

Dynamic departure pushback control at airports: Part A—Linear penalty‐based algorithms and policies

AbstractAirport surface congestion can lead to significantly long taxi‐out times, thus resulting in increased fuel‐burn costs as well as excessive emissions of greenhouse gases. To curtail this undesirable syndrome, in this article, we propose a new penalty‐based dynamic departure pushback control (PDPC) strategy, which employs a linear penalty function dependent not only on the taxiway queue limit but also on the current queue length to ration the pushback frequency at airports, and trades taxiway queueing times with gate‐hold delays to minimize the total operational cost (fuel‐burn and gate‐hold costs). Using data from Beijing Capital International (PEK) airport, four different departure pushback control policies, namely: (i) no‐control (baseline case); (ii) traditional ‐control; (iii) PDPC with a constant taxiway limit; and (iv) PDPC with varying taxiway limits; are compared. Detailed Monte Carlo simulations, which showcase the sensitivity of the total cost function to various problem parameters are presented, and our results indicate that deploying the PDPC policy results in a 42% reduction in total operational costs and a 68% reduction in fuel‐burn (kg) as compared to the baseline case. To analytically reinforce these simulation results, an iterative Markov chain‐based optimization algorithm is also developed to estimate the optimal values of the pushback rate and taxiway queue limit that minimize the total cost function. Such an analytical framework is very useful in the absence of reliable airport data as it only requires estimates of the historical pushback request rates and service times at the taxiway, while yet retaining the capability to closely mirror the simulation results. Our Monte Carlo simulations as well as the Markov chain optimization model validate the strength and impact of the proposed PDPC policy, and demonstrate its practical efficacy in reducing airport surface congestion when applied using data from PEK airport.

Quasi-Stochastic Departure Metering Model Considering Non-Deterministic Taxi Time, Standard Instrument Departure Track Time, and a New Delay–Conflict Relationship

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal flight departure schedule and sequence at airports. The proposed formulation addresses two key gaps in the existing departure metering strategies: (i) the variability and effects of the standard instrument departure (SID) procedure and (ii) the prospect of en route conflicts because of departure delays. The variability and effects of the SID procedure are captured through a non-deterministic parameter defined as the SID track time. The probability of en route conflicts because of departure delay is estimated based on a new delay–conflict relationship. Data used for implementation and validation of the model was collected from operations on Runway 7 of Chennai International Airport, India. Overall results indicate that the proposed departure metering model can act as a pre-emptive departure delay strategy framework that takes care of the SID procedure and en route conflict possibility with no harm to the runway throughput. The implications of this work hint toward better management of the airport operations and the performance of the overall air traffic management system.

A physics-based PSO-BPNN model for civil aircraft noise assessment

With the increasingly severe problem of aircraft noise pollution around airports, it is urgent to explore accurate and effective aircraft noise assessment methods. This article proposes a physics-based PSO-BPNN model based on the European Civil Aviation Conference (ECAC) best practice model, backpropagation neural network (BPNN), and particle swarm optimization (PSO) to enrich the methodology system of aircraft noise assessment. The primary modeling process is as follows. Firstly, based on airport parameters, flight information, and other data, the theoretical noise levels of ground monitoring point are calculated using the ECAC dynamic model. Then, a dataset containing measured noise values, theoretical noise values, trajectory data, and meteorological data is constructed to train the physics-based BPNN model, in order to correct the theoretical noise level. Finally, the PSO algorithm is introduced to optimize the parameters of the BPNN model and the construction of the physics-based PSO-BPNN model is completed. By taking Hefei Xinqiao International Airport (HFE) as a research case, the experimental results show that the physics-based PSO-BPNN model, which combines best practice model and machine learning model, demonstrates better performance than the ECAC model and physics-based BPNN model because of its balance between stability and flexibility. In the validation set, the error of 74.77 % of the predicted results was within ±3 dB(A), and the coefficient of determination R2 between all predicted values and measured values reached 0.9450, which indicates the physics-based PSO-BPNN model a potential aircraft noise assessment solution.

A Monte Carlo approach for capacity and delay analyses of multiple interacting airports in Istanbul metroplex

AbstractThe Istanbul metroplex airspace, home to Atatürk (LTBA), Sabiha Gökçen (LTFJ), and Istanbul (LTFM) international airports, is a critical hub for international travel, trade and commerce between Europe and Asia. The high air traffic volume and the proximity of multiple airports make air traffic management (ATM) a significant challenge. To better manage this complex air traffic, it is necessary to conduct detailed analyses of the capacities of these airports and surrounding airspace. In this study, Monte Carlo simulation is used to determine the ultimate and practical capacities of the airport and surrounding airspace and compare them to identify any differences or limitations. The traffic mix, runway occupancy time and traffic distribution at airspace entry points are randomised variables that directly impact airport and airspace capacities and delays. The study aims to determine the current capacities of the runways and routes in the metroplex airspace and project the future capacities with the addition of new facilities. The results demonstrated that the actual bottleneck could be experienced in airspace, rather than runways, which was the focus of the previous literature. Thus, this study will provide valuable insights for stakeholders in the aviation industry to effectively manage air traffic in the metroplex airspace and meet the growing demand.

Air Temperature for Sustainable Airport Infrastructure and Environment

Understanding the patterns of air temperature in the local area is essential to mitigate potential destructive impacts and develop strategies for climate-resilient infrastructure. The pilot study focuses on the analysis of air temperature at Soekarno Hatta International Airport (SHIA), Indonesia. The study utilizes air temperature data collected over a 30-month period, grouped into six-month intervals. To assess the significance of means and distributions of air temperature points, independent t-tests are employed. Data visualizations are provided to clarify the air temperature patterns during the observation period. The findings reveal that SHIA has experienced warm and stable air temperatures over the last 30 months. However, to comprehensively study the various impacts from climatology and meteorology perspectives on airport infrastructure and operations, it is essential for future research to address limitations related to access to certain variables and consider employing multivariable forecasting techniques. By addressing these aspects, further investigations are able to provide deeper insights into the implications of air temperature on airport operations and contribute to formulate a strategy for developing a climate-resilient airport infrastructure.

Optimizing energy solutions: A techno-economic analysis of solar-wind hybrid power generation in the coastal regions of Bangladesh

Hybrid renewable energy systems have acquired attention worldwide for their ability to harness multiple renewable sources parallelly like solar, wind, and hydropower, presenting numerous advantages. Bangladesh is forced to rely on the traditional fossil fuel-powered power generation infrastructure in order to meet the nation’s increasing need for electricity. The goal of this paper is to improve the percentage of renewable energy in Bangladesh’s energy landscape by addressing the technical, economic, and environmental elements of building a specialized hybrid system at Patenga Sea Beach through a methodical approach. This study is the first to pinpoint and address Patenga Sea Beach’s limits in light of Shah Amanat International Airport’s nearby location in Chittagong. In order to build the suggested system, a daily load demand of 1000 kWh and an annual peak demand of 53.57 kW were taken into account. The analysis shows that this system has a levelized cost of energy (LCOE) of 0.03$, generating 4,604.591 MWh of power annually. Findings from HOMER reveal an initial capital outlay of $350,688 for initial capital investment, with an annual O&M cost of $3,821, contributing to its cost-effectiveness. During the span of its lifetime, the hybrid system can also avoid 5,767 tons of CO2. The simulation program PVsyst has been utilized to assess the PV system-specific performance, viability, and outcomes. According to the PVsyst evaluation, the PV system has an 89 % system performance ratio, a 0.017$ levelized cost of energy, and a break-even period of only 6.6 years. The sensitivity analysis examines renewable resources, factoring in climate change’s effects on solar irradiation, wind speed, replacement cost, and operational expenses. It validates the hybrid system’s viability across various environmental scenarios, demonstrating resilience to changes in renewable resource costs and availability. The suggested hybrid system can be placedto minimize costs associated with energy generation, circumvent structural and geographic limitations, improve the Patenga Sea beach’s visual appeal, and lower greenhouse gas emissions. In addition to supporting future decisions about the deployment of hybrid systems, this research could assist in the integration of sustainable and renewable energy sources into the national grid.

Overview of Tanjung Semeti Training Area on The VFR Corridor Path

Zainuddin Abdul Madjid International Airport serves daily training flights and has a training area determined in the "Tanjung Semeti Area" where the location is also the same with the “Tanjung Semeti” checkpoint on the VFR Corridor. The purpose of this research is to review Tanjung Semeti training area to the VFR corridor path and seek solutions from potential safety risks (hazard) in that area. The research method used is a qualitative approach with data collection techniques through observation. The results of the study revealed that the same location causes VFR flight that passes through VFR corridor overlap with the training flight in the training area causing a potential safety risk for both flights. In an effort to be free from potential flight safety risks, by creating a training area at Tanjung Penggeroh or by optimizing the airspace of Sumbawa as a location for a training area will ensure the safety, efficiency and regularity of air traffic in the airspace of Zainuddin Abdul Madjid International Airport.

Estimated changes in carbon stock due to changes in land use around Yogyakarta International Airport

Land use plays an important role in maintaining carbon stock balance, ecosystem sustainability, and the environment. Massive land use changes in forest areas, peatlands, mangroves, and greenways result in an increase in CO2 release. This research aimed to analyze the impact of land use changes on the value of the carbon stock around Yogyakarta International Airport. The data used were Pleiades images in 2014, 2018, and 2022. Image analysis was carried out visually to produce detailed and accurate land use classification. Meanwhile, multitemporal map overlays were carried out to find out land use changes. Changes in carbon stock were obtained from the land use formula multiplied by the value of the Greenhouse Gas Constant (GGC). The results showed that the construction of an airport and its supporting infrastructure triggered land use changes that had implications for the decreasing carbon stock. The decrease in the area of vegetation cover in fields, community plantations, and mixed plantations from 2014 to 2022, amounting to -640.99 ha, increased carbon emissions. The results of the analysis showed that there had been changes in carbon stock. In 2014, the value was 150,286.57 t C/ha; in 2018, it decreased to 136,631.56 t C/ha; and in 2022, it reduced to 133,554.36 t C/ha. Massive economic activity and infrastructure development trigger reduced vegetation cover, resulting in increased carbon and increased carbon being released into the atmosphere. The problem of land conversion that affects changes in carbon stock and impacts climate change requires mitigation, among which is proper land use management and sustainable spatial planning.

Analisis Pelayanan Staf Unit Lost and found dalam Menangani Permasalahan Delay Baggage pada Penumpang Singapore Airlines di Bandar Udara Internasional Juanda Surabaya

Delay baggage is one of the issues handled by the lost and found unit at the airport. This happens when the baggage arrives late at the destination airport, especially on international flights. Some factors that contribute to delay baggage are short connecting flight times, baggage label detachment, and loading errors. This research aims to find out how the staf at the lost and found unit handle delay baggage issues for Singapore Airlines at Juanda Surabaya International Airport. The research method used is descriptive qualitative with interviews, observations, and documentation as data collection methods. The data obtained from this research includes interview results with the staf at the lost and found unit, as well as documents used in their daily activities. The results show that the service provided by the staf at the lost and found unit to Singapore Airlines passengers experiencing delay baggage issues is in accordance with Standard Operational Procedure (SOP). This includes baggage search and retrieval, as well as compensation provided by the airline.

Gestão da fauna aeroportuária: avaliando o estado de saúde de gaviões asa-de-telha (Parabuteo unicinctus) através da avaliação hematológica

The Harris’ Hawk is commonly used by falconers in falconry as a plan to avoid collisions between birds and aircraft, being trained to capture vultures, herons, Southern caracara, Southern lapwing, and pigeons. The aim of this work was to evaluate the hematological parameters in Harris’ Hawks used in airport fauna management in order to assess the health of these birds, compare the results of hawks from similar characteristics and differentiate parameters of trained and not trained hawks. This study has federal authorization under number 57551-1 (Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio/Ministério do Meio Ambiente, Brazil) on 08/30/2017. In this study, six females and three males of Harris’ Hawks (Parabuteo unicinctus), trained with falconry techniques, were used to control of fauna at the Antônio Carlos Jobim-Galeão International Airport, from Centro de Preservação de Aves de Rapina - CePAR, located in Rio de Janeiro - RJ. All birds were over six months of age, clinically healthy and in good body condition. The following analysis were performed: weight, erythrocyte counts, hemoglobin dosage, hematocrit, quantification of the mean corpuscular volume, mean corpuscular hemoglobin concentration, count of leukocytes, basophils, eosinophils, heterophils, lymphocytes and monocytes, processed by microscopy. Compared to other studies of Harris’ Hawks hematological parameters, this study presented decreased values for erythrocytes, hemoglobin, hematocrit, mean corpuscular volume and mean corpuscular hemoglobin concentration, while indicators such as leucocyte, heterophils and lymphocytes were increased. In conclusion, trained birds of prey are more susceptible to stressful factors, which can cause alterations in hematological parameters.

PERANCANGAN PEMBANGKIT LISTRIK TENAGA SURYA (PLTS) PADA ATAP GEDUNG WISTI SABHA BANDAR UDARA INTERNASIONAL I GUSTI NGURAH RAI

The installed PLTS capacity in Bali is currently still 3.44% of the target given by the General National Energy Plan (RUEN) of 108 MWp in 2025. The island of Bali has limited land availability so rooftop PLTS is a solution. The design of the rooftop PLTS was carried out on the roof of the Wisti Sabha Building at I Gusti Ngurah Rai International Airport. The research uses PVSyst simulation software while the investment feasibility method uses the Benefit-Cost Ratio (BCR), Net Present Value and discounted payback period (DPP) methods. Based on the simulation results, the rooftop PLTS capacity is 56.6 kWp with energy production of 98,009 kWh/year. The B-CR value obtained is 2.23, the net present value is IDR 707,436,838.55 and the DPP is 7 years, with a rooftop solar system life of 25 years.

UAM의 인천공항 활주로 횡단 진출입 방안 연구

UAM의 인천공항 활주로 횡단 진출입 방안 연구

Penerapan ARIMA Pada Data Curah Hujan di Stasiun Meteorologi Kelas II Minangkabau Padang Pariaman

Rainfall is one of the indicators of climate change that has an impact. Class II Minangkabau Meteorological Station Padang Pariaman is one of the locations that has an impact on rainfall. This location is Minangkabau International Airport so that rainfall information is very important. The rainfall data used in this study is monthly data with the period January 2017 to June 2022. The ARIMA model is applied to perform forecasting or prediction of rainfall data for the next period. The results of the study based on the analysis of the Akaike Information Criterion (AIC) value obtained the best model ARIMA (3,1,0).

Satisfaction of International Tourists with Departure Services at Kilimanjaro International Airport: A Study in Tanzania

This study assessed the relationship between airport departure non-processing service performance and international tourists' satisfaction at Kilimanjaro International Airport. A survey research design and quantitative approaches with stratified sampling and a 162 sample size were adopted. Data collection was done through closed-ended questionnaires. The questionnaires were distributed to tourists and picked after being filled up. Respondents’ responses were based on a five-point Likert Scale. Data analysis was done using IBM SPSS statistics version 21 and Partial Least Squares – Structural Equation Modelling using SmartPLS 3 software with the help of SmartPLS3 software. The study showed that airport departure facilities were directly and significantly associated with international tourists' satisfaction. This implies that the relationship exists in real life, and any effort to disregard the assessment of these facilities may disturb tourists' satisfaction and lead to untrustworthiness. Moreover, airport departure accessibility and departure retail areas showed an insignificant association with international tourists' satisfaction, implying that no relationship exists in real life. The study recommends that indicators for the constructs with direct relationships be treated as significant factors. However, airport operators should equally monitor service performance to achieve the highest tourist’ satisfaction in all three constructs to make tourists loyal since any service compromise can interrupt tourists' satisfaction. The findings have economic significance in improving tourism performance and airport service management.

Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.

Environmental Impact by Air Traffic: Assessing aircraft noise nearby Hong Kong International Airport

As part of a more global research effort towards a greener aviation, the present study focuses on the noise impact by aircraft operations around a major airport, namely the Hong Kong International Airport (HKIA). To assess a pre-existing aircraft noise prediction platform vis-à-vis the specificities of the local aviation scene, the ground noise impacts of 70+ aircraft flying in and out of HKIA are live-measured in two locations of Hong Kong city. This is achieved through in-situ audio recordings of aircraft during take-off/landing flight phases, along with a real-time tracking of their characteristics (aircraft and engine types, flightpaths, atmospheric conditions, etc.). Once suitably post-processed, the measurements are analyzed to characterize better the noise impacts by these various flights in regard to their specificities (e.g., aircraft types, flight operations). In a second stage, these noise impacts are compared against their digital twins, which are simulated via the noise prediction platform, using the actual flight characteristics (aircraft types, flightpaths, thrust, airspeed, etc.). This comparative assessment is achieved for a total of 60 flights consisting of 37 departures (resp. 23 arrivals) and involving 6 (resp. 5) aircraft types. Overall, the comparison between the field tests and their digital twins proves to be reasonably good, with both measured and predicted noise levels falling within a range of a few decibels across all flights, on average. Finally, specific analyses are conducted to explore the uncertainties weighing on the predictions, possibly explaining some of the mismatches observed between the experimental and computational results. Overall, the study further demonstrates that aircraft noise prediction methods such as the present one constitute a valuable means to assess the environmental impacts of air traffic operations, including when the latter are highly specific — as in Hong Kong city, owing to its complex airspace and meteorological conditions.

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.

Characteristics of Volatile Organic Compounds Emitted from Airport Sources and Their Effects on Ozone Production.

In recent years, commercial air transport has increased considerably. However, the compositions and source profiles of volatile organic compounds (VOCs) emitted from aircraft are still not clear. In this study, the characteristics of VOCs (including oxygenated VOCs (OVOCs)) emitted from airport sources were measured at Shenzhen Bao'an International Airport. The results showed that the compositions and proportions of VOC species showed significant differences as the aircraft operating state changed. OVOCs were the dominant species and accounted for 63.17%, 58.44%, and 51.60% of the total VOC mass concentration during the taxiing, approach, and take-off stages. Propionaldehyde and acetone were the main OVOCs, and dichloromethane and 1,2-dichloroethane were the main halohydrocarbons. Propane had the highest proportion among all alkanes, while toluene and benzene were the predominant aromatic hydrocarbons. Compared with the source profiles of VOCs from construction machinery, the proportions of halogenated hydrocarbons and alkanes emitted from aircraft were significantly higher, as were those of propionaldehyde and acetone. OVOCs were still the dominant VOC species in aircraft emissions, and their calculated ozone formation potential (OFP) was much higher than that of other VOC species at all stages of aircraft operations. Acetone, propionaldehyde, formaldehyde, acetaldehyde, and ethylene were the greatest contributors to ozone production. This study comprehensively measured the distribution characteristics of VOCs, and its results will aid in the construction of a source profile inventory of VOCs emitted from aircraft sources in real atmospheric environments.

Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation

The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management.