Arrival Flights Research Articles

Study on Typical Flight Ground Transit Scenario

In the process of aircraft design, it is necessary to study the flight ground transit scenario. In this paper, to research a typical flight ground transit scenario, a domestic airport is selected, and the typical values of taxi distance, service transit time, and arrival flight time are assessed, analyzed, and given after the influence of passenger flow, runway, terrain, and temperature. This typical transit scenario can be used for the relevant calculations of flight transit capacity in the top-level design of the aircraft.

Socio-Economic Assessment of Value for Climate Services Case Study, Uganda and Kenya

Socio-Economic Assessment of Value for Climate Services Case Study, Uganda and Kenya

ACHIEVING OPTIMAL FLIGHT DELAY PREDICTION AND ERROR CALCULATION USING MACHINE CLASSIFIERS

Flight delays are one of the main issues facing the aviation industry. Increased air traffic as a consequence of the aviation industry's expansion throughout the last two decades has led to flight delays. Flight delays not only waste time and money, but they also have an adverse effect on the environment. Airlines that operate commercial flights suffer huge losses as a result of flight delays. As a result, they make all necessary steps to prevent or minimize flight disruptions and delays. In this research, we apply algorithms based on machine learning to forecast when a specific flight's arrival will be postponed or not, including logistic regression, decision tree regression, bayesian ridge, random forest regression, and gradient boosting regression.

Performance Analysis of the Distributed Support Vector Machine Algorithm Using Spark for Predicting Flight Delays

In big data analysis requires powerful machine learning frameworks, strategies, and environments to analyze data at scale. Therefore, Apache Spark is used as a cluster computing framework to process big data in parallel and can run on multiple clusters. In this study, the Support Vector Machine (SVM) algorithm is used as a classification method to predict whether a flight will experience a delayed arrival. This study also aims to analyze the performance of the distributed SVM algorithm using the Apache Spark framework in classifying delayed flight arrivals. Running time evaluation is important in proving how fast the data processing is done using Apache Spark. In addition, there is a test to prove the effect of using the SVM algorithm with a distributed system on the results of the classification accuracy of delayed flight arrivals. Distributed SVM performance testing is carried out using variations in data size and the number of worker nodes in the built cluster. From the test results, it was found that the most effective number of worker nodes used in the flight delay classification process was 4 worker nodes with the lowest running time results from the experiment of 4 variations in the number of worker nodes. In terms of accuracy, adding the number of worker nodes does not affect the accuracy of the program. The difference in the accuracy of results is caused by the random oversampling process on the data performed on each system test. Using the SVM algorithm with Spark is sufficient to provide good performance in the classification process with the highest accuracy result in the test being 93.98 %.

Contribution of Airplane Engine Emissions on the Local Air Quality around Stuttgart Airport during and after COVID-19 Lockdown Measures

Air quality investigations at airports have shown that aircrafts cause a significant increase in air pollution at and around the vicinity of the airport, which can cause adverse effects on human health. The objective of this research was to investigate the aircraft-sourced pollutant levels at the Stuttgart airport and in the surrounding areas during and after COVID-19 lockdown measures. Three phases of stationary measurements of ultrafine particles (UFP), particulate matter (PM), black carbon (BC), CO2, O3, NO, and NO2 were made at various points on the east and west sides of the airport in the extension of the airport runway. In first phase of measurement, the airport was closed for construction, and no air traffic took place. In the second phase, the airport was reopened with limited operation due to a lockdown period at the beginning of the COVID-19 pandemic. Finally, in the third phase, measurements were performed during the peak summer holiday travel season to measure the air quality during maximum air traffic, after the end of the first lockdown period. While there were fewer notable changes in the BC concentrations, coarse PM fractions, and gases across the three phases, there were significant increases in the UFP concentrations from aircraft emissions. Throughout the three phases, the peak particle concentration decreased from between 27 and 86 nm in phase 1. to between 27 and 35 nm in phase 2, to finally 11 nm in phase 3 on all days in which the aircraft plumes were measured. During flight arrivals, definite increases in UFP particle number concentration (PNC) were observed, with the majority of the particles being in the 10 nm size class. These results were measured repeatedly on both sides of the airport in the direct prolongation of the runway and even at distances of up to 3 km away in nearby neighbouring communities. While the overall PM and UFP levels are affected by vehicular traffic, the freeway measurements showed particles from aircrafts and vehicles are distinguishable using the parameters PNC and Dp. The BC concentrations were rarely influenced by aircraft activity, while only some NO and NO2 peaks were measured depending on the consistency of the wind.

Arrival flight efficiency in pre- and post-Covid-19 pandemics

Covid-19 pandemic affected aviation severely, resulting in unprecedented reduction of air traffic. While aviation is slowly re-gaining traffic volumes, we use the opportunity to study the arrival performance in the Terminal Maneuvering Area (TMA) in non-congested scenarios.Applying flight efficiency and environmental performance indicators (PIs) to the historical data of arrivals to Stockholm Arlanda and Gothenburg Landvetter airports, we discover noticeable inefficiencies, despite significant reduction of traffic intensity. We analyze the impact of such factors as weather and traffic intensity on arrival efficiency in isolated scenarios when only one factor dominates: isolated scenario with low traffic and isolated scenario with good weather conditions. Our analysis uncovers that weather has a stronger influence than traffic intensity on the vertical efficiency, while traffic intensity has stronger effect on the lateral efficiency. Impact of traffic intensity on the lateral efficiency might be explained by frequent hold-on patterns and flight trajectory extensions due to vectoring in high traffic conditions. Further investigation is needed to explain weather and vertical/lateral efficiency correlations, the conclusions might be country-specific.

Flight reliability during periods of high uncertainty

In times of great uncertainty for the airline industry, travelers are in search of reliable itineraries now more than ever. With condensed airline schedules and less options, air travelers must rely on making flight connections and manage layover times to arrive at their final destination on time. In an era with readily available information, passengers expect accurate and transparent reliability information to help improve decision making for multi-leg itineraries. However, often for reliability in air travel, this information is incomplete or not useful. In this paper we utilize historical probability distributions of flight arrival and departure times using publicly available data to give an intuitive and predictive flight itinerary reliability metric. The COVID-19 pandemic significantly affected air-travel in the US and this uncertainty is still being felt with cancellations and delays due to staff shortages and reduced demand. Therefore, we extend the stochastic network model from our previous research to air travel during COVID-19 to see the effects on flight reliability. Using this model, we conduct computational experiments to evaluate air travel through multiple reliability metrics. We show that during periods of high uncertainty, predictive historical distributions of flight data considering recency and seasonal effects are less accurate given many cancellations and a reduced flight schedule.

Flight-Level Analysis of Departure Delay and Arrival Delay Using Copula-Based Joint Framework

The main goal of the current study is to identify the factors affecting flight-level airline delay by jointly modeling departure and arrival delays. Toward this end, we develop a novel copula-based group generalized ordered logit (GGOL) model system that accommodates for the influence of common observed and unobserved effects on flight departure and arrival delays. The proposed model is estimated using 2019 marketing carrier on-time performance data compiled by the Bureau of Transportation Statistics (BTS) for 67 airports in the continental U.S. The delay data is augmented with a comprehensive set of independent variables including traffic conditions at the origin and destination airports in the hours preceding flight departure and arrival, trip-level attributes, weather variables for the entire flight duration, and spatial and temporal factors. The model estimation results highlight that the Joe copula model with parameterization provides the best data fit. The model performance is further established to be excellent using a holdout sample. Finally, to illustrate the applicability of the model for prediction and highlight the impact of independent variables, we perform a prediction exercise under a host of hypothetical scenarios. The illustration provides a mechanism for employing the proposed model as a tool for airline-carrier-level or airport-level delay prediction analysis using weather forecasts while controlling for a host of independent variables.

A NOVEL SCHEDULING APPROACH FOR PILGRIM FLIGHTS OPTIMIZATION PROBLEM

The main goal of airport administrations around the world is to facilitate the conduct of passenger services and reduce waiting time as much as possible. This can be achieved by regulating the flow of passengers at the various stages of the airport, including arrival and departure halls, passport checkpoints, luggage handling, and customs. This study focuses on improving the flow of passengers in the Hajj terminal at King Abdulaziz International Airport (KAIA) in the Kingdom of Saudi Arabia, as it is one of the most welcoming stations for travelers during the Hajj season and is the fourth largest passenger terminal in the world. Three different optimization algorithms are applied to improve the scheduling process of assigning the arrival flights to available airport gates, as well as the stages inside the various airport lounges and areas. These algorithms are genetic algorithm (GA), harmony search algorithm (HSA), and differential evolution algorithm (DEA). The results give a prior knowledge of how the whole passengers’ arrival process and show the stages that are prone to congestion and cause process delay. Experimental performance results in terms of fitness value and convergence rate show that GA outperforms HSA and DEA when the population size is equal to 5, whereas DEA provides better performance compared to other algorithms when the population size is equal to 20 and 50. Moreover, the results show that the largest waiting time for passengers was in the arrival gate lounges due to the lack of allocated spaces in the passport areas, followed by the luggage area, then the passport control and customs areas, respectively.

Çoklu terk ediş noktası bulunan pistler için stokastik sıralama planlama modeli

The runway exit points (REPs) of the airport are constructed considering the operational performance of different types of aircraft based on historical flight data. In sequence planning, it is assumed that aircraft will vacate the runway from an expected exit point. However, real performance can be uncertain, and the same type of aircraft may vacate the runway from different exit points rather than the expected point. In addition, the runway occupancy times (ROTs) of aircraft that vacate the runway from the same exit point may not be equal. This situation brings two types of uncertainty when making traffic plans in an airport with several REPs. The first uncertainty is the REP of the aircraft, and the second is the ROT uncertainty considering the exit points. In this study, a two-stage stochastic programming model was developed for aircraft sequencing in an airport that has multiple runway exit points. In the model, both runway exit and ROT uncertainties are considered. A runway with multiple exit points at an airport in Turkey was selected and flight track data of 154 arrival flights to this runway was examined. Various expected time of arrival and departure (ETAD) scenarios were generated based on real data and integrated into the mathematical models. The proposed model was then compared with deterministic and first come first serve (FCFS) approaches in terms of total delay. As a result of the comparison and analyses, the presented stochastic programming model provided robust solutions and delay savings compared to the other approaches.

Influential factors to aircraft taxi time in airport

Operation of aircraft in the airport movement area is a major source of fuel combustion and emission, and has a great influence on aircraft exit time, fuel cost, and labor cost. Slots are calculated according to runway capacity, mooring capacity, terminal capacity, and control capacity, but aircraft flow in climate or mobile areas is not considered. This study proposes a model to explain a taxi time in an airport with influential factors such as airport weather (wind direction, wind speed, visibility, air pressure, precipitation), number of take-off and landing flights, number of aircraft moving in the moving area, runway occupation time for arrival, runway occupation time for departure, take-off and landing direction, and terminal location. In this study, the factors influencing aircraft movement are analyzed using data from Gimpo Airport and Jeju Airport in 2019. Result shows that taxi-in-time and taxi-out time are affected by wind speed, air pressure, and precipitation, especially among weather variables, the number of arrival flights by time, and departure flights before takeoff. The runway occupancy time, the take-off and landing direction, and the terminal location shows a significant factor to taxi hours. Gimpo Airport and Jeju Airport show a significant difference in taxi in and taxi out time. It is expected that these results can contribute to systematically and scientifically calculating aircraft flow while considering moving areas and weather. This study can help optimize the landing runway, landing direction, and travel path and time of the aircraft.

Exploring the impact of flow values on multiscale complexity quantification of airport flight flow fluctuations

Being able to characterize the dynamic characteristics of the air traffic system and embody the dynamic evolution thereof, the complexity of air traffic flows has attracted the broad attention of scholars and researchers. To fully consider amplitude information, we adopt the improved multiscale weighted permutation entropy (IMWPE) and propose an improved multivariate multiscale weighted permutation entropy (IMMWPE) to explore the complexity of flight flows of the top ten busy airports in China from a univariate and a multivariate perspective, respectively. In addition, we propose an improved multiscale weight contingency to investigate the inhomogeneity between fluctuation pattern distributions in departure and arrival flight flows. At the advantages of the multiple coarse-graining procedures, we obtain more reliable and stable entropy and contingency results, considering amplitude information. Comparisons between the weighted and unweighted results show that the weighted methods are as capable as the unweighted in exactly characterizing the dynamic characteristics of flight flow fluctuations, i.e., temporal scale dependency, spike distributions, differences between univariate and multivariate perspectives. Results also show that the weight has a stronger impact on the complexity of univariate time series than the multivariate. From the univariate perspective, the complexity of departure and arrival flight flows of ZLXY and ZGGG is more sensitive to the amplitude information. From the multivariate perspective, ZUCK could be a typical representative of the complexity of the total flight flows of the ten airports.

Modelling the root causes of fatigue and associated risk factors in the Brazilian regular aviation industry

This work evaluates the potential root causes of fatigue and its relationships with accident risks using a bio-mathematical model approach and a robust sample (N = 8476) of aircrew rosters from the Brazilian regular aviation, extracted from the Fadigômetro database. The fatigue outcomes derive from the software Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST), which considers the homeostatic process, circadian rhythms and the sleep inertia. The analyses include data from January 2019 until March 2020 and show relevant group effects comparing early 2019 and 2020, with the latter presenting lower fatigue outcomes in most cases. The average minimum SAFTE-FAST effectiveness during critical phases of flight (departures and arrivals) decreases cubically with the number of shifts that elapse totally or partially between mid-night and 6 a.m. within a 30-day period (NNS). As a consequence, the relative fatigue risk increases by 23.3% (95% CI, 20.4–26.2%) when increasing NNS from 1 to 13. The average maximum equivalent wakefulness in critical phases also increases cubically with the number of night shifts and exceeds 24 h for rosters with NNS above 10. The average fatigue hazard area in critical phases of flight varies quadratically with the number of departures and arrivals within 2 and 6 a.m. (NWocl). These findings demonstrate that both NNS and NWocl represent potential root causes of fatigue and therefore should be considered key performance indicators and kept as low as reasonably practical when building aircrew rosters, in order to properly manage the fatigue risk. The effectiveness scores obtained at 30-minute time intervals allowed a model estimate for the relative fatigue risk as a function of the time of the day, whose averaged values show reasonable qualitative agreement with previous measurements of pilot errors in the cockpit. Moreover, the 2019 data revealed a risk exposure factor two times (14%) higher than the figures reported by de Mello et al. (2008) (7%), within 0h00 do 05h59. Tailored analyses of the SAFTE-FAST inputs for afternoon naps before night shifts, commuting from home to station and vice-versa, and bedtime before early-start shifts were carried out using the responses of a questionnaire. The average fatigue hazard area in critical phases of flight increases by 43 to 63% switching off the afternoon naps, 14 to 21% increasing the commuting from one to two hours and 35 to 54% switching off the advanced bedtime criterion, with significant group effects in all cases (p<0.001), evidencing the need of a better and more accurate understanding of these parameters when modelling fatigue risk factors. The non-linear relationships between SAFTE-FAST outcomes and key roster’s parameters, such as NNS and NWocl, provided a novel statistical approach for risk assessment and led to few safety recommendations to the aviation sector regarding regulatory reviews and fatigue risk management and mitigation policies.

Applying digital twins for the management of information in turnaround event operations in commercial airports

The aerospace sector is one of the many sectors in which large amounts of data are generated. Thanks to the evolution of technology, these data can be exploited in several ways to improve the operation and management of industrial processes. However, to achieve this goal, it is necessary to define architectures and data models that allow to manage and homogenise the heterogeneous data collected. In this paper, we present an Airport Digital Twin Reference Conceptualisation’s and data model based on FIWARE Generic Enablers and the Next Generation Service Interfaces-Linked Data standard. Concretely, we particularise the Airport Digital Twin to improve the efficiency of flight turnaround events. The architecture proposed is validated in the Aberdeen International Airport with the aim of reducing delays in commercial flights. The implementation includes an application that shows the real state of the airport, combining two-dimensional and three-dimensional virtual reality representations of the stands, and a mobile application that helps ground operators to schedule departure and arrival flights.

Multiobjective Optimization of Airport Ferry Vehicle Scheduling during Peak Hours Based on NSGA-II

Airport ferry vehicles are used to transport passengers between the far apron and terminals. During the peak hours of flight arrival or departure, the demand for ferry vehicles will increase, and improper scheduling will result in significant delays. In this study, we construct a 0–1 integer programming scheduling model for ferry vehicles with multiple objectives: minimize flight delays and vehicle travel distances and balance vehicle workloads when there is an insufficient number of ferry vehicles. To solve the multiobjective model, this study selects the second version of the nondominated sorting genetic algorithm-II as the main framework and designs a modified coding and decoding scheme to solve the constraint problem in the model. The proposed model and algorithm are verified using actual data from Kunming Changshui International Airport, China. Compared with the scheduling method of manual first come first serve, the total delay time of the proposed model is reduced by 24.52%, the transit distance by 6.63%, and the balance index by 24.05%, which helps the airport solve the problem of insufficient ferry vehicles during peak hours. Comparison results show that the proposed model has a shorter calculation time and better distribution of solution sets than three state-of-the-art multiobjective optimization algorithms: MOPSO, MOACO, and SPEA-II. This research can help airports reduce flight delays, vehicle purchases, and operation costs.

Weather impact quantification on airport arrival on-time performance through a Bayesian statistics modeling approach

Compared with departures, predicting the weather impact on arrival delays is more challenging because of possible non-linear, cascading effects, and higher uncertainty. Existing weather impact studies are location-dependent and often neglect the impacts of dangerous phenomena. We propose a data-driven model for severe weather impact quantification on airport arrival on-time performance based on the Bayesian approach to address these issues. Our model considers the impact of the dangerous phenomenon by evaluating the mean shift and is flexible enough to be applied to different airports. Using two years’ worth of data (2017-2018) from the Hong Kong International Airport, we studied over 55,000 local meteorological reports and analyzed over 430,000 arrival flights. Across all three key performance metrics considered, a non-linear relationship with the weather score, akin to a phase transition, could be observed. This framework allows a comparison between the sensitivity of each airport’s arrival performance metric towards severe weather. Delay rate is the most sensitive metric, while cancellation rate is the least. For the impacts of dangerous phenomena, cumulonimbus has the most significant impact on the delay rate. Shower rainfall/cumulonimbus has a similar and vital impact on the mean arrival delay per hour. Because of its potential applications in different airports, this framework can provide a deeper insight into weather impact on air traffic networks.

Stochastic scheduling of ground movement problem integrated with taxiway routing and gate/stand allocation

The authors propose a new scheduling model and solution method for Ground Movement Problem (GMP) which is designed to manage the movements of flights on the surface of airport air-side in an effective manner. Since the arrival time for arrival flights and off-block time for departure flights are usually uncertain parameters for GMP, the proposed scheduling model is stochastic and integrates the three-dimensional (3-D) taxiway routes’ assignment (with variable taxiing speed for flights) and the gates/stands allocation for the flights, with fully capturing multiple constraints related to taxiways, gates/stands and aircraft performance limits. The solution method also provides a stochastic quantification scheme concerning both uncertainties by the arrival time and off-block time information. The schedule results can realize the immunity of the above time uncertainties within a tolerable risk probability. We finally provide numerical analysis to verify the stochastic scheduling model with mixed integer and linear programming (MILP) basics and illustrate the effectiveness of proposed solution method by a test case on Hangzhou international airport in China.

Spatial–Temporal Graph Data Mining for IoT-Enabled Air Mobility Prediction

Big data analytics and mining have the potential to enable real-time decision making and control in a range of Internet of Things (IoT) application domains, such as the Internet of Vehicles, the Internet of Wings, and the Airport of Things. The prediction toward air mobility, which is essential to the studies of air traffic management, has been a challenging task due to the complex spatial and temporal dependencies in air traffic data with highly nonlinear and variational patterns. Existing works for air traffic prediction only focus on either modeling static traffic patterns of individual flight or temporal correlation, with no or limited addressing of the spatial impact, namely, the propagation of traffic perturbation among airports. In this article, we propose to leverage the concept of graph and model the airports as nodes with time-series features and conduct data mining on graph-structured data. To be specific, first, airline on-time performance (AOTP) data is preprocessed to generate a temporal graph data set, which includes three features: 1) the number; 2) average delay; and 3) average taxiing time of departure and arrival flights. Then, a spatial–temporal graph neural networks model is implemented to forecast the mobility level at each airport over time, where a combination of graph convolution and time-dimensional convolution is used to capture the spatial and temporal correlation simultaneously. Experiments on the data set demonstrate the advantage of the model on spatial–temporal air mobility prediction, together with the impact of different priors on adjacency matrices and the effectiveness of the temporal attention mechanism. Finally, we analyze the prediction performance and discuss the capability of our model. The prediction framework proposed in this work has the potential to be generalized to other spatial–temporal tasks in IoT.

An examination of the potential impact of 5G on air travel in the U.S.

The rollout of 5G wireless service has been much anticipated by consumers across the U.S. since it will bring faster data download speeds and expanded service; however, the impact to aviation and air travel are as a major issue and the potential impact to aviation has not been estimated or assessed. This paper fills that gap and provides the first analysis of the potential number of flights impacted by 5G at ten of the busiest airports in the U.S. Using detailed historical weather and flight data in 2019 at 10 large hub airports, we find that nearly 15,700 flight arrivals could be directly impacted annually, at the ten airports considered. The geographical and seasonal impact varies significantly with more flights affected by the potential for 5G interference at airports in Seattle and Dallas Fort-Worth and relatively few flights affected at airports in Phoenix and Las Vegas. A case study examining ten years of data at Chicago O’Hare International, a hub for two major airlines, suggests that over 2,400 flights every year could be impacted by 5G interference. Fortunately, there has been increasing cooperation between the cell phone industry and aviation stakeholders to ensure a safe rollout of 5G C-band; this paper provides a significant contribution by documenting the issues and context for aviation operations.

A machine learned go-around prediction model using pilot-in-the-loop simulations

Go-around manoeuvers are challenging for the management of arriving air traffic due to their complexity and unpredictability. In the present work, we propose to augment the air traffic control (ATC) environment with a predictive tool that assists Air traffic controllers (ATCOs) in the detection and prediction of the likelihood of go-around events. First, a safe and cost-effective flight approach data collection mechanism is introduced via a pilot-operated flight simulator under varying visibility conditions. Then, data are processed and a go-around probability assignment mechanism is introduced. Finally, a go-around probability prediction model is proposed. The proposed model updates the go-around probability for each new flight record, namely every second, and converges by detecting either a go-around or a successful landing. Experiments are conducted for two airports, namely Philadelphia International Airport (PHL) and Van Nuys Airport (KVNY), which feature different go-around procedures and are operated by different types of aircraft. Findings demonstrate that the trajectories of predicted go-around probabilities follow closely the computed probabilities regardless of the dissimilarities in the trajectory patterns. Furthermore, results suggest that the likelihood of go-around exceeds 93% when the go-around is initiated, for almost 50% of the flights in the test set. Moreover, the proposed model is able to detect the successful landing of aircraft. In fact, the system indicates low chances of go-around when the aircraft landed. For instance, the likelihood of a go-around at the touchdown is below 35% and 10% for KPHL and KVNY, respectively. The findings underscore the ability of the proposed model in providing accurate and timely go-around probabilities. This will help the ATCO to be prepared for re-sequencing arrival flights and clearing the go-around path, prior to the actual initiation of a go-around.