Arrival Flights Research Articles

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

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

Flight Arrival Scheduling via Large Language Model

The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabilities and generalization potential of large language models (LLMs). Our method conceptualizes the dynamic scheduling problem for training flight as a language modeling problem, a perspective not previously explored. Specifically, we represent the allocator’s inputs and outputs as language tokens, utilizing LLMs to generate conflict-free results based on a language description of requested landing information and assigned training flight information. Additionally, we employ a reset strategy to create a small dataset for scenario-specific samples, enabling LLMs to quickly learn allocation schemes from the dataset. We demonstrated the capability of LLMs in addressing time conflicts by evaluating metrics such as answer accuracy, conflict rate, and total delay time (without the wrong answer). These findings underscore the feasibility of employing LLMs in the field of air traffic control.

Prediction of Taxi-in Time and Analysis of Influencing Factors for Arrival Flights at Airport with a Decentralised Terminal Layout

Accurately predicting taxi-in times for arrival flights is crucial for efficient ground handling resource allocation, impacting flight departure timeliness. This study investigates terminal layout characteristics, specifically decentralised layouts, to predict and analyse arrival flight taxi-in times. We develop a surface traffic flow calculation method considering arrival and departure flights, eliminating fixed thresholds. We introduce runway-crossing operations for decentralised airports, creating new prediction variables. We consider factors like runway, aircraft type, airline, taxi distance, and time periods. Gradient Boosting Regression Tree predicts taxi-in times, while Lasso analyses factor impact. Our approach yields highly accurate predictions for decentralised airports, with Surface traffic flow and Runway-crossing variables significantly influencing taxi-in times. This research informs airport managers in decentralised layouts, enabling tailored management strategies.

Identification of Traffic Flow Spatio-Temporal Patterns and Their Associated Weather Factors: A Case Study in the Terminal Airspace of Hong Kong

In this paper, a data-driven framework aimed at investigating how weather factors affect the spatio-temporal patterns of air traffic flow in the terminal maneuvering area (TMA) is presented. The framework mainly consists of three core modules, namely, trajectory structure characterization, flow pattern recognition, and association rule mining. To fully characterize trajectory structure, abnormal trajectories and typical operations are sequentially extracted based on a deep autoencoder network with two specially designed loss functions. Then, using these extracted elements as basic components to further construct and cluster per-hour-level descriptions of airspace structure, the spatio-temporal patterns of air traffic flow can be recognized. Finally, the association rule mining technique is applied to find sets of weather factors that often appear together with each flow pattern. Experimental analysis is demonstrated on two months of arrival flight trajectories at Hong Kong International Airport (HKIA). The results clearly show that the proposed framework effectively captures spatial anomalies, fine-grained trajectory structures, and representative flow patterns. More importantly, it also reveals that those flow patterns with non-conforming behaviors result from complex interactions of various weather factors. The findings provide valuable insights into the causal relationships between weather factors and changes in flow patterns, greatly enhancing the situational awareness of TMA.

Proactive real-time scheduling method for apron service vehicles based on mixed strategies

Due to the uncertainty of flight arrival and departure times, the scheduling of apron vehicles in airports has always faced significant challenges. This paper considers the real-time scheduling problem of apron vehicles under dynamic requests, establishing a new parking rule to allow apron vehicles to dynamically adjust parking areas. In order to maximize service level and minimize operational costs, this paper proposes a proactive real-time scheduling method for apron vehicles based on mixed strategies. Through strategies such as flexible waiting and dynamic relocation, this paper responds proactively to future requests. The proposed strategies are validated through implementation in large and medium-sized scale cases at Beijing Capital International Airport and Nanjing Lukou International Airport in China, respectively. Numerical results indicate that the proposed scheduling method is effective and applicable. The flexible waiting strategy significantly reduces vehicle travel distance, while the dynamic relocation strategy is significant in shortening request response times.

Probabilistic Pretactical Arrival and Departure Flight Delay Prediction with Quantile Regression

Airports plan their resources well in advance based on anticipated traffic. Currently, the only traffic information accessible in the pretactical phase is the flight schedules and historical data. In practice, however, flights do not always depart or arrive on time for a variety of reasons, such as air traffic flow management or reactionary delays. Because neither air traffic flow management regulations nor aircraft rotations are known during the pretactical phase, predicting the precise arrival and departure delays of individual flights is challenging given current technologies. As a result, probabilistic flight delay predictions are more plausible. This paper presents a machine learning model trained on historical data that learned the various quantiles of the departure and arrival delay distributions of individual flights. The model makes use of input features available during the pretactical phase, such as the airline, aircraft type, or expected number of passengers, to provide predictions of the delay distribution several days before operations. The performance of the model trained on operational data from Geneva airport is compared to a statistical baseline, providing evidence that machine learning is superior. Furthermore, the contribution of the various input features is quantified using the Shapely method, stressing the importance of the expected number of passengers. Finally, some practical examples are presented to illustrate how such a model could be applied in the pretactical phase.

Optimization of terminal area arrival flight sorting based on an improved sparrow search algorithm.

At present, airspace congestion and flight delays have become widespread concerns. This study aims to optimize the sequencing of arrival flights in the terminal area of multirunway airports. Considering the constraints of multiple runways, slant intervals and moving flight positions, this article establishes an optimization model for arrival flight sequencing in a multirunway airport terminal area. Accordingly, an improved sparrow search algorithm (ISSA) is proposed based on Chebyshev chaotic mapping, the golden sine strategy, and the variable neighborhood strategy. Through six basic test functions, the ISSA is compared with particle swarm optimization, the whale optimization algorithm, the genetic algorithm, and other algorithms to verify its superiority. Finally, two sets of instance data from Kunming Changshui Airport were used for experiments. The results show that the total delay times (TDTs) of small-scale flights (number of aircraft: 29) and large-scale flights (number of aircraft: 147) are 55.3% and 20.5% lower, respectively, than those of the first-come-first-served algorithm. The superiority of the ISSA designed in this article is verified, and it can significantly reduce the TDTs of arrival flights. It is suitable for optimizing arrival flights during peak hours at most airports. This approach provides theoretical support for optimizing the sorting of flights in terminal areas.

Optimising the management of arriving baggage at a Kuwait Airways passenger terminal using mathematical programming and simulation

Handling luggage systems is a critical component of a passenger terminal’s operations. The proposed study attempts to find the optimal solution for a manually operated terminal concerning the luggage offloading process from arrival flights carried by a limited number of ground handling agents. Handling agents start the offloading process from the aircraft to the cargo luggage containers carried by a cargo car that will take the containers’ trolleys to the cargo area to offload them into the reserved luggage belt carousel. This study aims to improve airport service quality by minimizing the baggage handling process time for arrival flights which leads to minimizing passenger waiting time in the baggage claim area. We proposed both a deterministic and stochastic approach. The integer programming method is provided to minimize the total number of flights assigned to the belt carousel under the realistic constraint of minimizing the luggage load on each belt carousel. Simulation tools were used so that the offloading process could be modeled to study the effects of various parameters such as the number of ground handling agents for different flights with different amounts of luggage.

Estimating the Lag Time Between Flight Arrivals and Parking Exit Volumes at a Major Airport: A Practical Approach

Estimating the Lag Time Between Flight Arrivals and Parking Exit Volumes at a Major Airport: A Practical Approach

A novel intelligent approach for flight delay prediction

Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed.

Machine learning-enhanced aircraft landing scheduling under uncertainties

Aircraft delays lead to safety concerns and financial losses, which can propagate for several hours during extreme scenarios. Developing an efficient landing scheduling method is one of the effective approaches to reducing flight delays and safety concerns. Existing scheduling practices are mostly done by air traffic controllers (ATC) with heuristic rules. This paper proposes a novel machine learning-enhanced methodology for aircraft landing scheduling. Data-driven machine learning (ML) models are proposed to enhance automation and safety. ML enhancement is adopted for both prediction and optimization. First, the flight arrival delay scenarios are analyzed to identify the delay-related factors, where strong multimodal distributions and arrival flight time duration clusters are observed. A multi-stage conditional ML predictor is proposed for improved prediction performance of separation time conditioned on flight events. Next, we propose incorporating the ML predictions as safety constraints of the time-constrained traveling salesman problem formulation. The scheduling problem is then solved with mixed-integer linear programming (MILP). Additionally, uncertainties between successive flights from historical flight recordings and model predictions are included to ensure reliability. We demonstrate the real-world applicability of our method using the flight track and event data from the Sherlock database of the Atlanta Air Route Traffic Control Center (ARTCC ZTL). The case studies provide evidence that the proposed method is capable of reducing the total landing time by an average of 17.2% across three case studies, when compared to the First-Come-First-Served (FCFS) rule. Unlike the deterministic heuristic FCFS rule, the proposed methodology also considers the uncertainties between aircraft and ensures confidence in the scheduling. Finally, several concluding remarks and future research directions are given. The code used can be retrieved from Link.

Neural Airport Ground Handling

Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, and capacity. Then we propose a construction framework that decomposes AGH into sub-problems (i.e., VRPs) in fleets and present a neural method to construct the routing solutions to these sub-problems. In specific, we resort to deep learning and parameterize the construction heuristic policy with an attention-based neural network trained with reinforcement learning, which is shared across all sub-problems. Extensive experiments demonstrate that our method significantly outperforms classic meta-heuristics, construction heuristics and the specialized methods for AGH. Besides, we empirically verify that our neural method generalizes well to instances with large numbers of flights or varying parameters, and can be readily adapted to solve real-time AGH with stochastic flight arrivals. Our code is publicly available at: https://github.com/RoyalSkye/AGH.

Flight delay propagation inference in air transport networks using the multilayer perceptron

Recent studies have explored the delay propagation network among airports by inferring Granger causality among time series using linear methods. Granger causality indicates whether delays at one airport can help predict delays at itself or other airports and thus help to understand the interconnection among airports. This paper makes inferences about the Granger causality relation among airports by applying a nonlinear multilayer perceptron method to the arrival delay time series in Europe and China. We find that the propagation results can be overestimated with data input containing early arrival flights, besides delayed flights. Europe presents a smaller magnitude of delay propagation than China, considering only delayed flights, indicated by the link densities of the networks with the same penalty parameter. Unlike some recent research findings, our results suggest that large airports have more out-degree and more impact in the delay propagation network. These results can help predict and understand delay propagation in daily operations or simulation environments.

Optimal Sequencing of Arrival Flights at Metroplex Airports: A Study on Shared Waypoints Based on Path Selection and Rolling Horizon Control

The civil aviation industry is experiencing significant growth in air traffic density within terminal areas, necessitating improved air traffic efficiency. In China’s pursuit of world-class airport clusters, operational complexities arise due to the co-location of these airports in the same terminal area airspace, resulting in lower operational efficiency. To mitigate congestion and flight delays, this study proposes an integrated model that considers multiple runways and route selections, accounting for actual route point restrictions. Utilizing actual operational data from Shanghai metroplex, the proposed model is validated. The study focuses on the airport metroplex system and presents a comprehensive mixed-integer programming (MIP) model for arrival sequencing, considering multiple airports, runways, and routes. The maximum landing efficiency is adopted as the objective function, optimizing arrival scheduling while considering time intervals, route selection, and landing constraints. The Multi-waypoint Rolling Horizon Control (MWRHC) algorithm is employed to tackle time-efficiency challenges, ensuring flight safety by continuous monitoring of flights in the terminal area. Comparative analysis reveals the algorithm’s superior optimization performance for single-runway airports compared to dual-runway airports. Overall, the proposed model and algorithm effectively improve the efficiency of multi-airport arrival scheduling in airport metroplex systems.

Ferry Scheduling Optimization Considering Arrival Time Uncertainty and In-Place Time Differences

The aircraft ferry service is an important link in the transfer of travelers between the remote parking stand and the terminal. By analyzing the process of inbound and outbound ferry service, the relationship between the in-place time of each ferry on the same flight is clarified. A ferry service splitting method considering the difference in in-place time is proposed. Based on this, a dynamic programming scheduling model for ferries is innovatively developed. Considering the impact of flight arrival and departure uncertainty on the in-place time of ferry service, the model is transformed into a dynamic programming stochastic model with opportunity constraints. The optimization objective of this model is to minimize the number of ferries used. To describe the model more easily, a sample average approximation technique is introduced to transform the stochastic model into a deterministic model with confidence. A genetic algorithm based on Monte Carlo random sampling is designed to solve the model. The experimental results based on the actual operation data of an airport in South China show that the dynamic splitting method considering the difference in the in-place time of the ferry can improve the resource utilization efficiency; the scheduling scheme based on the dynamic planning stochastic model has better robustness.

Improving flight delays prediction by developing attention-based bidirectional LSTM network

Recently, the significance of accurate aircraft delay forecasting has grown in the aviation sector, which caused multi-billion-dollar losses faced by airlines and airports and passenger loyalty losses. Due to the importance of accurate flight delay prediction for all stakeholders involved, the aviation sector seeks to develop techniques for more robust flight delay prediction. Is quickly becoming an important research issue to improve airport and airline service performance and offer customers dependable travel itineraries. Machine learning techniques have been used in a number of studies to evaluate and resolve issues with flight delay prediction. This paper proposes a framework integrated network called ‘Attention-based Bidirectional long short-term memory’ (ATT-BI-LSTM) for flight delay prediction. The Bidirectional LSTM model extracts the spatial and temporal of the flight network with weather features. The ‘Attention mechanism’ has been proposed to enable the model to discover significant and discriminating features that contribute to categorization. The first stage of the proposed framework is the ‘preprocessing of dataset’ which is performed through two steps. The first step is data transformation using MinMax scaler to reduce the variation in the data. The second step is ‘balancing the dataset’ using SMOTE technique for balancing data. The second stage is the establishment of the ATT-BI-LSTM network through the deep tuning experiments of network structure to identify the best combination of parameters and network architecture. To validate the performance of the proposed framework, a wide network of US domestic flights tested in two scenarios. In Scenario 1, the objective is to predict the delay of flight arrival and departure, by using basic flight features with the weather. In Scenario 2, the objective is to predict the delay of flight arrival, by using basic flight features with departure delays. Simulation results show that in scenario 1, the training accuracy of flights’ delay is 88% in both flight delay arrivals and departure, and the testing accuracies of flights’ delay 83% and 82% in departure and arrival respectively. On the other hand, in scenario 2, testing accuracies are 94.30% and 93.71% in the two datasets respectively. The simulation results show that the ATT-BI-LSTM model outperforms other models found in the literature. Therefore, the developed ATT-BI-LSTM framework can contribute strongly to mitigating flight delays by providing a high accuracy prediction system in real-time monitoring to airport and aviation authorities.

A bilevel flight collaborative scheduling model with traffic scenario adaptation: An arrival prior perspective

Flight Optimization Scheduling (FOS) is a crucial component of decision-making support systems in air traffic management. This study addresses the hierarchical leader–follower relationship between arrival and departure flights and develops a bilevel flight collaborative scheduling model. The upper-level model focuses on scheduling arrival flights, while the lower-level model concentrates on scheduling departure flights. Given the varying demands across different traffic states, both the upper and lower-level models are driven by an efficiency objective during peak hours, namely, mitigating flight delays and improving runway throughput, correspondingly. Conversely, during non-peak hours, a trade-off between efficiency and fairness is sought. Furthermore, the lower-level model aims to maximize fairness among departure routes for all traffic states, leading to the formulation of a multi-objective programming model. To solve this proposed model, genetic algorithms are employed in conjunction with the elitism strategy and the forgetting mechanism. Experimental results demonstrate that the proposed model not only enhances the efficiency of flight and runway operations but also promotes fairness among flights and departure routes.

Environmental inefficiencies for arrival flights at European airports.

In this paper, we analyze two months of trajectory data for aircraft landing in five major European airports. Based on open ADS-B data from the OpenSky Network and open performance models, we enrich all trajectories with automatically detected procedure information, fuel consumption, and emissions for supported aircraft types. To assess the inefficiencies associated with holding patterns, point merges, and continuous descent operations across different airports, we propose methodologies to quantify and compare these environmental inefficiencies. Holding patterns are found to have a higher negative impact on the environment than point merge and continuous descent operations. Furthermore, the paper provides recommendations for procedure evaluations of future airports, which could help policymakers and relevant stakeholders to evaluate the environmental performances of arrival procedures based on open data and open models.

Unstable Approach Detection and Analysis Based on Energy Management and a Deep Neural Network

The study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through historical flight data. In addition to weather factors such as low visibility and crosswinds, human factors also account for a large part of the risk. From the accidents studied in the stochastic report of the Flight Safety Foundation, nearly 70% of the accidents occurred during the approach and landing phases, which were caused by improper control of aircraft energy. Since the information of the flight data recorder (FDR) is regarded as the airline’s confidential information, this study calculates the aircraft’s energy-related metrics and investigates the influence of non-weather-related factors on unstable approaches through a publicly available source, automatic dependent surveillance-broadcast (ADS-B) flight data. To evaluate the influence of weather- and non-weather-related factors, the outliers of each group classified by weather labels are detected and eliminated from the analysis by applying hierarchical density-based spatial clustering of applications with noise (HDBSCAN), which is utilized for detecting abnormal flights that are spatial anomalies. The deep learning method was adopted to detect and predict unstable arrival flights landing at Taipei Songshan Airport. The accuracy of the prediction for the normalized total energy and trajectory deviation of all flights is 85.15% and 82.11%, respectively. The results show that in different kinds of weather conditions, or not considering the weather, the models have similar good performance. The input features were analyzed after the model was obtained, and the flights detected as abnormal are discussed.

Study of Delay Prediction in the US Airport Network

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine.