Arrival Flights Research Articles

Prediction of Arrival Flight Operation Strategies under Convective Weather Based on Trajectory Clustering

An airport’s terminal area is the bottleneck of the air transport system. Convective weather can seriously affect the normal flight status of arrival and departure flights. At present, pilots take different flight operation strategies to avoid convective weather based on onboard radar, visual information, adverse weather experience, etc. This paper studies trajectory clustering based on the OPTICS algorithm to obtain the arrival of typical flight routes in the terminal area. Based on weather information of the planned typical flight route and flight plan information, Random Forest (RF), K-nearest Neighbor KNN (KNN), and Support Vector Machines (SVM) algorithms were used for training and establishing the Arrival Flight Operation Strategy Prediction Model (AFOSPM). In this paper, case studies of historical arrival flights in the Guangzhou (ZGGG) and Wuhan (ZHHH) terminal area were carried out. The results show that trajectory clustering results based on the OPTICS algorithm can more accurately reflect the regular flight routes of arrival flights in a terminal area. Compared to KNN and SVM, the prediction accuracy of AFOSPM based on RF is better, reaching more than 88%. On this basis, six features—including 90% VIL, weather coverage, weather duration, planned route, max VIL, and planned Arrival Gate (AF)—were used as the input features for AFOSPM, which can effectively predict various arrival flight operation strategies. For the most frequently used arrival flight operation strategies under convective weather conditions—radar guidance, AF changing, and diversion strategy—the prediction accuracy of the ZGGG and ZHHH terminal areas can exceed 95%, 85%, and 80%, respectively.

Optimum problems in environmental emissions of aircraft arrivals

We propose the solution of optimal problems in aircraft noise and environmental emissions on arrival and landing at a major international airport. We present two distinct cases: 1.) arrival flight of a single airplane; 2.) flight planning for a fleet of four different airplanes. Mathematically, they lead to a constrained optimal control problem: either initial-value or a two-value boundary problem. The cost functions are either noise emission or a combination of noise, engine exhaust emissions (including NOx) and health value functions. Official population statistics are used to quantify the overall effect on communities around the airport. In the second case, we demonstrate how the optimal solution leads to a Pareto front that requires post-optimal analysis techniques. We determined that the solution with lowest noise level is not always the one with the lowest number of awakenings; the solution with lowest CO2 emissions might not be the one with the possible shortest operation.

Prediction of Departure Flights’ Taxi-Out Time Based on Intelligent Algorithm Optimized BP

Taxi-out time is the main performance index to evaluate the operational efficiency of major airports. Scientifically and accurately predicting the taxi-out time of departure flights is very important to improve the operational efficiency and coordination decision-making ability of airport. Firstly, the quantifiable influence factors of taxi-out time and their correlation is analyzed, including the number of departure flights, the number of arrival flights, the number of flights pushed back in the same period, the taxi-out time by half-hour in average, the taxi distance, and the number of turns, etc. And then a prediction model of departure flights’ taxi-out time based on BP is constructed. Because the traditional BP neural network is sensitive to the initial weight and threshold and has poor accuracy and stability, the taxi-out time prediction model of BP neural network optimized by intelligent algorithm is proposed. Genetic algorithm (GA) and sparrow search algorithm (SSA) are used to obtain the optimal weight and threshold of BP neural network, which is verified by the actual operation data of a major airport in central and southern China for two weeks. The results show that ① the taxi-out time is strongly correlated with the airport surface traffic flow, moderately correlated with the average taxi-out time, and weakly correlated with the taxi distance and the number of turns. ② The predicted outcome of the 4-element combination prediction model which considers strong correlation and medium correlation factors is the best. After adding weak correlation factors, the prediction accuracy is reduced. ③ By obtaining the local optimal weight and threshold of neural network, intelligent optimization algorithm can effectively improve the accuracy of departure flights’ taxi-out time prediction results. ④ The prediction result of BP neural network optimized based on GA is 1.79% higher than that of MAPE before optimization, MAE is reduced by 7.4 s, and RMSE is reduced by 6.93 s. The prediction result of BP neural network optimized based on SSA is 3.05% higher than that of MAPE before optimization, MAE is reduced by 16.55 s, and RMSE is reduced by 14.31 s. Therefore, Sparrow search algorithm has better optimization effect on the model than genetic algorithm.

An Effective Protocol for Management of International Arrivals at Risk in COVID-19 Pandemic: Experience From the Pre-Hospital Covid-19 Response Teams at Xi'an, China.

BackgroundThe coronavirus disease 2019 (COVID-19) outbreak within China has been well controlled and stabilized since early April 2020. Therefore, the current major focus in China is to prevent the introduction of COVID into China from international arrivals. To achieve this, pre-Hospital COVID-19 Response Teams (pHCRTs) have been established.ContextThe pHRCTs were established in Xi'an, China in early 2020. During the 12 months covered in this report, there were 356 international flight arrivals with over 5,000 COVID-19 Nucleic Acid Test (NAT) positive people, 500 of them with symptomatic COVID-19 and requiring admission to special hospitals. All other arrivals were managed in dedicated facilities by pHRCTs. The outcome measure of this report was the number of positive cases among the pHRCT members.DetailsFour hundred forty-two staff worked in the pHCRTs during the reporting period. Despite multiple throat swab PCR tests during their pHRCTs tour of duty, and the subsequent mandatory 14-day quarantine required before return to the general community, no staff became NAT positive.ConclusionThe prevention of community transmission from imported cases is a vital part of the strategy to maintain the low numbers of cases in countries which have achieved control, or suppression of local internal cases. The program of pHCRTs described in this article gives successful protocols for transportation of patients who are infectious based on the minimal transmission of virus and staff safety. The strategies employed may prove useful in future pandemics.

The Optimization of Airport Management Based on Collaborative Optimization of Flights and Taxis

How to efficiently guide passengers and ensure the order of airport operation is an urgent transport problem for airport management. Based on the analysis of the factors that affect the driver’s decision-making, this paper deeply explores the collaborative association of the core factors, such as the number of flight arrivals in different periods and the average seeking distance of taxis. Firstly, according to the GPS data of taxis, the paper uses clustering algorithm to get the average passenger-seeking time from the airport and makes matching interaction between the number of flights based on time distribution and the average passenger-carrying capacity of vehicles in the parking garage, so as to build a decision-making model based on the number of taxis N; secondly, it takes passenger safety and traffic order as the priority and uses M/M/S queuing model to integrate the two factors. Taking the maintenance cost and passenger evacuation time as constraints, the judgment condition of minimum cost Zmin and the optimal number of boarding points Sm are solved. Finally, taking the flight and taxi data of Shanghai Hongqiao Airport as an example, the driver’s decision-making standard is simulated, and the accuracy of the model is verified by the deviation rate. It can provide decision-making support for taxi management of urban transportation hub and rapid evacuation of airport passengers, so as to realize the collaborative optimization of airport flight arrival and taxi carrying order.

Impact of Aircraft Performance and Time of the Day on Flight Arrival Delays Prediction in the United States: a Machine Learning Classification

The excessive growth of air traffic, with the limited airspace and airports capacity, results in a flight demand-capacity imbalance leading to air traffic delays. This paper explores the factors associated with delay in both microscopic and macroscopic ways. The aim is to develop a model which analyzes and predicts the occurrence of flight arrival delays using US domestic flight data for the year 2018. It will provide passengers, airlines and airport managers with reliable flight arrival schedules, and consequently reduce economic losses and enhance passengers trust. Beside database features, the proposed model is to the best of our knowledge the first attempt to predict flight arrival delays using three new features which are contributive factors to delays: Departure Time and Arrival Time of the day in which the flight was performed (Early morning, late morning, noon, afternoon, evening or night) and model of aircraft. Four Machine Learning classifiers namely Random Forest, Decision Trees, K-Nearest Neighbors and Naive Bayes were used. In order to find the best parameters of each algorithm, we implemented Grid Search technique. The performance of each classifier was compared in terms of hyperparameters tuning, classification metrics and features description. The experimental results showed that the proposed system was able to predict flight arrival delays with the best Random Forest accuracy of 0.9356 and a higher number of correctly classified flights. To prove the importance of our findings, we compared our model to that of existing literature studies.

Data‐driven flight time prediction for arrival aircraft within the terminal area

Predicting the arrival aircraft's flight time plays a critical role in effectively optimizing and scheduling spatial-temporal resources in the terminal airspace. This paper focuses on a data-driven method for predicting the arrival flight time. First, based on the existing research, a feature set is constructed from four aspects: initial state, arrival pressure, sequencing pressure, and wind information, which are believed to affect arrival flight time significantly. Second, eight widely used models are developed to predict flight time, including linear regression models, nonlinear regression models, and tree-based ensemble models. Furthermore, the stacking technique is adopted to improve the prediction performance. Finally, take Guangzhou Baiyun International Airport as a study case to verify the proposed method's effectiveness. The results indicate that the arrival pressure (describing the arrival traffic demand) and the sequencing pressure (sketching the arrival traffic distribution) could effectively improve the prediction accuracy. The mean absolute percentage error of the predicted flight time via ATAGA and IGONO can be increased by 1%. Besides, the proposed method of extracting wind data could also improve the prediction performance. The mean absolute error of the predicted flight time via GYA can be reduced by 4.85 s.

A probabilistic model based optimization for aircraft scheduling in terminal area under uncertainty

Air traffic management relies strongly on the accuracy of prediction, while uncertainties such as weather, navigation accuracy, pilot operations, etc. may weaken the performance of predictive tools and cause potential safety issues or reduced capacity. Among all controlled airspace, the Terminal Maneuvering Area (TMA) is one of the most complex areas in which flight safety can be easily affected by unpredictable disturbances. This paper addresses an aircraft scheduling problem under uncertainty with the aim of providing a robust schedule for arrival flights. Uncertainty quantification and propagation along the routes are realized in a trajectory model that formulates the time information as random variables. Conflict detection and resolution are performed at waypoints of a predefined network based on the predicted time information from the trajectory model. By minimizing the expected number of conflicts, consecutively operated flights can be well separated. Apart from the proposed model, two other models - the deterministic model and a model that incorporates separation buffers - are presented as benchmarks. A meta-heuristic simulated annealing algorithm combined with a time decomposition sliding window is proposed for solving a case study of the Paris Charles de Gaulle (CDG) airport. Further, a simulation framework based on the Monte-Carlo approach is implemented so as to evaluate the optimized solutions obtained from the three models. Statistical comparison among the results shows instability of the model that incorporates the separation buffers, in contrast, the proposed model has absolute advantages in both stability and the conflict absorbing ability when uncertainty arises.

A Comparative Analysis of Delay Propagation on Departure and Arrival Flights for a Chinese Case Study

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

Analysis of Honeybee Drone Activity during the Mating Season in Northwestern Argentina.

Simple SummaryWhen we think of social insects, we mostly neglect males and their biology. Honeybee males or drones are an example. Like queens, drones are founders of the colony, but unlike queens and workers, their activity is not well-known. Drones exit the hive to mate as their only goal, in spite of the high chances of getting lost, being preyed upon and starving; and only if they are lucky, they will die when mating. These nuptial flights take place in Drone Congregation Areas during spring and summer, and we know that their exit time is in the afternoons. Although, drone activity at other hours of the day has not been studied. Using three methods (direct observation, video records and microchip tagging) we evaluated drone activity during the whole day in Northwest Argentina. We detected 24 h of activity for the first time. Surprisingly, several drones were active at dawn, morning, and late morning. Most of them were active in the afternoon, as previously reported. The activity at dawn and during the morning could be a normal pattern in drones around the world, or it could be a result of abnormal factors, such as environmental variables altering their biological clocks.Males in Hymenopteran societies are understudied in many aspects and it is assumed that they only have a reproductive function. We studied the time budget of male honey bees, drones, using multiple methods. Changes in the activities of animals provide important information on biological clocks and their health. Yet, in nature, these changes are subtle and often unobservable without the development and use of modern technology. During the spring and summer mating season, drones emerge from the hive, perform orientation flights, and search for drone congregation areas for mating. This search may lead drones to return to their colony, drift to other colonies (vectoring diseases and parasites), or simply get lost to predation. In a low percentage of cases, the search is successful, and drones mate and die. Our objective was to describe the activity of Apis mellifera drones during the mating season in Northwestern Argentina using three methods: direct observation, video recording, and radio frequency identification (RFID). The use of RFID tagging allows the tracking of a bee for 24 h but does not reveal the detailed activity of drones. We quantified the average number of drones’ departure and arrival flights and the time outside the hive. All three methods confirmed that drones were mostly active in the afternoon. We found no differences in results between those obtained by direct observation and by video recording. RFID technology enabled us to discover previously unknown drone behavior such as activity at dawn and during the morning. We also discovered that drones may stay inside the hive for many days, even after initiation of search flights (up to four days). Likewise, we observed drones to leave the hive for several days to return later (up to three days). The three methods were complementary and should be considered for the study of bee drone activity, which may be associated with the diverse factors influencing hive health.

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

Flight delay is the most common preoccupation of aviation stakeholders around the world. Airlines, which suffer from a monetary and customer loyalty loss, are the most affected. Various studies have attempted to analyze and solve flight delays using machine learning algorithms. This research aims to predict flights’ arrival delay using Artificial Neural Network (ANN). We applied a MultiLayer Perceptron (MLP) to train and test our data. Two approaches have been adopted in our work. In the first one, we used historical flight data extracted from Bureau of Transportation Statistics (BTS). The second approach improves the efficiency of the model by applying selective-data training. It consists of selecting only most relevant instances from the training dataset which are delayed flights. According to BTS, a flight whose difference between scheduled and actual arrival times is 15 minutes or greater is considered delayed. Departure delays and flight distance proved to be very contributive to flight delays. An adjusted and optimized hyperparameters using grid search technique helped us choose the right architecture of the network and have a better accuracy and less error than the existing literature. The results of both traditional and selective training were compared. The efficiency and time complexity of the second method are compared against those of the traditional training procedure. The neural network MLP was able to predict flight arrival delay with a coefficient of determination R 2 of 0.9048, and the selective procedure achieved a time saving and a better R 2 score of 0.9560. To enhance the reliability of the proposed method, the performance of the MLP was compared with that of Gradient Boosting (GB) and Decision Trees (DT). The result is that the MLP outperformed all existing benchmark methods.

Passengers intentions towards self-services check-in, Kuwait airport as a case study

The expansion of the air transportation field has prompted significant air traffic congestion around the world. The management of airport demand intends to moderate air traffic congestion by restricting the uneven aspects among demand and capacity at occupied airport terminals through managerial measures (e.g., slot controls) or financial motivating forces. Kuwait International Airport faced congestion problem which leaded to a noticeable decrease in its performance. In this study, a questionnaire was made to explore the satisfaction level of customers of Kuwait International Airport services and facilities. In order to solve the crisis, a self-check-in kiosk is proposed to be used in the current airport for departure and transit flights. Furthermore, a new eco-friendly building with energy efficient lighting and lifecycle management of electronics from procurement to disposal, which serves arrival flights with full facilities, was proposed. The designed building was feasibly studied and assumed to be a BOT project. A total construction cost of 19,863,157.5 KD, a total revenue for all floors of 6,876,000 KD/year and IRR of 10.56% were estimated that made the building a successful project.

Development of Automated Flight Data Collection System for Air Transportation Statistics

Data and information regarding air transportation is very crucial for all aspects, such as economy, people mobility and tourism. Currently, the Air Transportation Statistics is based on administrative data of different institutions. it is necessary to have a new data source regarding air transportation activities that can be used as an alternative reference for air transportation data which is faster and more granular. This research aims to study a new approach to produce air transportation statistics, especially air transportation statistics in Indonesia from big data that can be used as comparative or as complementary data for official air transportation statistics. This research using a website scraping method from a site that provides monitoring and tracking services for all flights in the world. The flight data was collected daily from the 15 busiest airports in Indonesia for both departure and arrival flights using the API provided by the website. The Scrapy module in the Python programming language is implemented. The data was collected daily from March 15, 2020, to August 31, 2020. The results of the flight data set contain information about the flight code, aircraft code, airline name, departure airport, departure city, arrival airport, arrival city, date/time of departure and arrival, and flight status. The result shows that it is feasible to use big data as a comparative or as a complementary of official statistics, especially in air transportation statistics. By using the web scraping technique, the indicator that usually requires more time and cost can be done in real-time and less cost. This new approach is expected to improve the quality of official air transportation statistics.

Robust Airport Gate Assignment Based on the Analysis of Flight Arrival Time

At present, hub airport is facing a lot of emergency situations, e.g., the management of irregular flight, which affects the sustainable development of the airport. Most of the methods for evaluating the robustness of airport gate assignment have been proposed by giving an independent evaluation function based on their experience, and thus, subjective factors are involved in varying degrees. Therefore, this paper puts forward an objective evaluation method based on the analysis of airport flight arrival, which gives a digital transformation strategy for the airport resource scheduling. A novel evaluation method based on data analysis of flight arrival time, and a mathematical model are formulated accordingly. In order to solve the proposed NP-hard problem, a new hybrid genetic algorithm combined with a heuristic algorithm is designed. In numerical experiments, the actual flight data of an international hub airport are used to analyze and verify the effectiveness of the proposed method. The results showed the advantages in scheme stability, operating cost savings, and risk reduction of the proposed method.

Airline ground operations: Optimal schedule recovery with uncertain arrival times

Airline operations are subject to a number of stochastic influences which result into variable ground and block times for same flights on different days. Our research explores how airline operations control centers may benefit from an integrated decision support system for schedule recovery during aircraft ground operations. In this paper, we study the sensitivity of an optimal set of schedule recovery options towards uncertain arrival times. The calculation of recovery options is based upon an integrated and iterative scheduling and optimization algorithm, which incorporates uncertainties for arrival flights as a function of a given look-ahead time. Potential recovery options include stand re-allocation, quick-turnaround, quick passenger transfer, waiting for transfer passengers, cancellation of passenger or crew connections, and arrival prioritization. Within a case study setting at Frankfurt Airport, 20 aircraft turnarounds are analysed during a morning peak with their respective estimated arrival times (including potential arrival delays). The analysis of simulation results reveals an almost identical set of selected recovery options under high uncertainty circumstances and from post-operational point-of-view, which indicates high solution stability.

Collaborative slot allocation for arrival flights in multi-airport terminal area based on the traffic flow pattern: a case study of Shanghai terminal, China

In order to solve the increasingly serious congestions and delays in busy airports, the problem of collaborative arrival slot allocation for the multi-airport terminal area is investigated in this paper. With the premise of ensuring operation safety and the goal of minimizing total delay, the specific pattern of arrival traffic flow in the terminal area is considered in optimization modelling, and an improved genetic algorithm is designed for effective solution, followed which a simulation experiment of Shanghai terminal in China is conducted based on analysing the spatiotemporal characteristics of the arrival traffic flow in it. Results show that the proposed method in this paper is able to reduce the total arrival delay in Shanghai terminal by 18.67%, and the delays on three runways in the terminal are cut down by 14.56%, 19.35% and 25.37% respectively, indicating a significant improvement in operation efficiency of the total system.

The impact of heterogeneous arrival and departure rates of flights on runway configuration optimization

ABSTRACT The rapid growth of the airline industry has caused an enormous demand in the context of air transport and air traffic congestion in several hub airports. In order to alleviate this situation and resolve the imbalance between the arrival and departure rate, efficient runway usage in airport capacity management is an immediate and feasible solution as compared to airport expansion and runway construction. Air Traffic Control (ATC) operators could optimize their runway capacity by operating dynamic runway configuration in switch-mode runways based on the air and airport traffic conditions. A semi-mixed mode runway is considered in this paper, wherein some runways are configured for either landing or take-off operations, while others are operated in switch mode. The demand for arrival and departure is subject to the passenger's demand, flight availability, and timings, preferred flight schedule, and frequency of flight schedule service, and usually vary in different hours (peak and nonpeak hours). Given this feature, ATC can reconfigure the runway mode responding to the current demand for arrival and departure and further seize the runway capacity via a systematic approach. Under the semi-mixed mode situation, formulating the coordination of dynamic runway configuration planning and the Aircraft Sequencing and Scheduling Problem is proposed. The air traffic pattern in Hong Kong International Airport (HKIA) is used as a test case to evaluate the performance of this proposed model. Based on the test results, it was found that this dynamic runway configuration planning and semi-mixed runway design can utilize runway capacity more efficiently. In the numerical study, the dynamic runway configuration planning achieved 71.6%% and 37.08% reduction of flight tardiness than the two segregated runway systems (two landings and one take-off runways and one landing and two take-off runways) in HKIA.

Analysis of airport weather impact on on-time performance of arrival flights for the Brazilian domestic air transportation system

Air traffic operations are significantly impacted by weather conditions. These external factors may impose operational constraints and generate demand-capacity imbalances, leading to reduced on-time performance, additional airline costs and inconveniences to passengers. Efficient management of such disruptions requires an understanding of the main causes of flight delays towards increasing their predictability. In this study, we investigate the impacts of airport surface weather conditions on the likelihood of flight delays for the Brazilian domestic air transportation system. We use historical flight schedule, on-time performance and weather data and estimate a logit model to analyze how different meteorological variables at the airport of destination affect the probability of a delayed arrival. We obtain empirical evidence for the impacts of low ceiling and visibility conditions, precipitation and wind gusts on the likelihood of arrival delays for the set of Brazilian airports analyzed.

Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model.

In the paper, the flight time deviation of Lithuania airports has been analyzed. The supervised machine learning model has been implemented to predict the interval of time delay deviation of new flights. The analysis has been made using seven algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines. To find the best parameters which give the highest accuracy for each algorithm, the grid search has been used. To evaluate the quality of each algorithm, the five measures have been calculated: sensitivity/recall, precision, specificity, F-measure, and accuracy. All experimental investigation has been made using the newly collected dataset from Lithuania airports and weather information on departure/landing time. The departure flights and arrival flights have been investigated separately. To balance the dataset, the SMOTE technique is used. The research results showed that the highest accuracy is obtained using the tree model classifiers and the best algorithm of this type to predict is gradient boosted trees.

Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods

This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models.