Air Traffic Flow Research Articles

Challenges and initiatives regarding the extension of operating hours at Osaka International Airport

Osaka International Airport is an urban airport located near the city center, and its operating hours are set at 14 hours, from 7:00 a.m. to 9:00 p.m. The airport extends its hours under unavoidable circumstances and allows takeoffs and landings after 9:00 p.m. However, the airport has long conflicted with local communities over aircraft noise at night, and now, the airport receives harsh opinions especially regarding noise at night and takeoffs and landings after 9:00 p.m. And the number of flights taking-off and landing outside of the operating hours has been on the rise, reaching a record high in FY2022, despite all the countermeasures described later. This is caused not only by the congestion of air traffic flow seen for the last several years, but also by severe weather events around the airport, such as frequent typhoons and thunderclouds triggered by the recent intensifying climate changes. It is becoming even more critical to ensure on-time performance and to gain the understanding of local communities about the circumstances above. The presentation will introduce many initiatives to assess the situation accurately, ensure punctuality in cooperation with airlines, and engage in regular dialogues with residents in the surrounding area.

Improved air traffic flow prediction in terminal areas using a multimodal spatial–temporal network for weather-aware (MST-WA) model

Accurately predicting air traffic flow in terminal areas is critical for balancing demand and capacity, particularly under challenging weather conditions. However, the complex interactions between weather patterns and air traffic make reliable predictions difficult. To address this issue, we propose a novel approach called the Multimodal Spatial-Temporal network for Weather-Aware prediction (MST-WA), designed to enhance air traffic flow prediction (ATFP) in terminal areas. Our method begins by constructing a spatial–temporal graph that captures the topology of the terminal area, including airports, routes, and fixes as nodes. A weather-aware module is then introduced, leveraging a Residual Network (ResNet) and attention mechanism to model the deep spatial–temporal correlations in the Weather Avoidance Field (WAF). The proposed model architecture integrates five key branches: arrival flow, departure flow, graph network topology, weather conditions, and flow constraint control, with predictions generated via an attention-based Long Short-Term Memory (LSTM) network. Experimental results using real-world data from Guangzhou Baiyun Airport, China, show that MST-WA outperforms baseline models in ATFP. Furthermore, a case study in convective weather scenarios demonstrates the model’s adaptability and effectiveness. We believe that the proposed model can serve as a valuable tool for air traffic controllers, enhancing decision-making and improving overall air traffic management.

Characterizing Performance Tradeoffs from Air Traffic Management Services for Advanced Air Mobility

The emergence of Advanced Air Mobility (AAM) has called for novel Air Traffic Management (ATM) solutions to enable the projected high number of operations, particularly in already dense and complex metroplex airspace. Understanding the multidimensional performance impacts of developing air traffic rules, procedures, and services is key to guiding this new traffic management system design. This paper presents a simulation framework to provide a quantitative assessment of the operational performance of ATM services in support of AAM. The framework embeds several modules for high-fidelity modeling of urban air traffic flows based on actual data, ATM service provision, stochastic simulation, and performance quantification. Two key services are analyzed: airspace geofencing and strategic deconfliction. We evaluate the use of these services in a scenario of air mobility operations associated with daily urban commuting in the Sao Paulo metropolitan region. Monte Carlo experiments are performed to simulate the execution of aircraft trajectories managed under different ATM service configurations, allowing for the characterization of operational performance tradeoffs, particularly in terms of safety and efficiency. The work contributes to quantifying the value of novel ATM services for integrating AAM operations in dense metroplex airspace, supporting their design and implementation.

Characteristics of Air Traffic Flow in Terminal Airspace: A Multiplex Recurrence Network Analysis

Characteristics of Air Traffic Flow in Terminal Airspace: A Multiplex Recurrence Network Analysis

Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering

Addressing the complexities of modern Air Traffic Management (ATM), this paper introduces a novel framework for dynamic airspace sectorization, tailored to enhance efficiency and safety in congested airspaces. Central to this framework is the WP-ConvLSTM model, an innovative deep learning approach equipped with attention mechanisms. This model excels in accurately predicting workload dynamics, a critical factor in managing air traffic flow. To implement sectorization, we adopt a constrained K-means clustering technique for spatial division, followed by a refinement process involving Support Vector Machine (SVM) algorithms for precise boundary generation. Further optimization of sector boundaries is achieved through an evolutionary algorithm, ensuring both flexibility and stability in airspace divisions. Our methodology was thoroughly evaluated using real-world data from one of the busiest airspaces, demonstrating significant improvements in workload prediction accuracy and airspace sector management. The findings highlight the model’s robustness in practical scenarios, offering a scalable solution for ATM challenges. We conclude with a recognition of the study’s limitations and propose avenues for future research to build upon our findings, particularly in enhancing real-time data integration and adapting to evolving air traffic patterns.

Probabilistic Air Traffic Complexity Analysis Considering Prediction Uncertainties in Traffic Scenarios

This article presents a methodology for analyzing probabilistic air traffic complexity by integrating prediction uncertainties in convective weather scenarios. With the Performance Review Unit (PRU) model as a base, this method modifies the original framework by incorporating a weather-related complexity indicator. The approach was tested in Austrian airspace using ensemble weather forecasts and historical flight plan data. The results demonstrated that a probabilistic model effectively assesses traffic complexity and captures trends in complexity over time, providing greater reliability in high-complexity sectors. Validation revealed a strong alignment between simulator complexity values and probabilistic complexity, especially in sectors characterized by dense data distributions. In contrast, sectors with more elongated distributions tended to overestimate complexity. Quantitative analysis indicated that the error between the probabilistic mean complexity and the simulator complexity values ranged from 12% to 23%, with higher errors in sectors with lower complexity. This validation confirmed the model’s ability to predict complexity trends, thereby assisting flow manager positions (FMPs) in traffic flow and airspace management. Overall, this study demonstrated that probabilistic complexity assessment provides a deeper understanding of traffic behaviour, facilitating more effective air traffic flow management in uncertain and dynamic conditions.

A Knee-Guided Evolutionary Algorithm for Multi-Objective Air Traffic Flow Management

Air traffic flow management plays a crucial role in efficient aviation. Most existing studies assume the flight speed as constant throughout the trip, leading to ineffective fixed-speed schedules. To address this issue, we propose a new problem model, which allows variable speed control to improve the flexibility and maneuverability of the management. In addition, we consider two conflicting objectives, which are minimizing the total flight delays and conflicts between flights, where the conflicts depend on the flight 4D trajectories (3D position plus time). To solve this new challenging problem, we propose a novel multi-objective evolutionary algorithm with new problem-specific individual representation and search operators. Specifically, the multi-chromosomes encoding scheme is designed to adapt to different types of operations. Then, to search the huge search space effectively, we develop a hybrid crossover operator that recombines the parents based on their flight routes. Furthermore, to balance the exploration and exploitation, we develop a new mutation strategy to utilize the heterogeneous search potential of different individuals. For exploitation, the knee individual in the Pareto front is improved by a new time shift operator for exploitation, and other non-dominated solutions are mutated by fixed-route mutation. For exploration, the dominated solutions are mutated randomly. To verify the effectiveness, we compare it with the real air traffic flow management schedules and the state-of-the-art algorithms on a range of real-world air traffic datasets. Extensive results show that the proposed algorithm can significantly outperform the baselines in generating safe and efficient 4D trajectories.

Hybrid AI-based 4D trajectory management system for dense low altitude operations and Urban Air Mobility

Urban Air Mobility (UAM) has emerged as a promising solution to address some of the challenges of transportation congestion and associated pollution, especially in large cities. However, the development of drone transportation and UAM services is limited by the capacity of the low altitude airspace where these new vehicles will operate. Without suitable regulatory advancements and associated traffic management systems, air traffic in the densest low-altitude sectors may incur congestion, which, in addition to affecting operational efficiency, can increase systemic risk and fuel emergency occurrences, thereby affecting the safety of people and property in the air and on the ground. To address these challenges, this study aims to develop an intelligent Uncrewed Aircraft Traffic Management (UTM) system that leverages the complementary strengths of metaheuristic and machine learning algorithms for an effective management of dense low altitude airspace. The UTM system determines time-based three-dimensional airspace Demand-Capacity Balancing (DCB) solutions by processing real-time data updates and dynamically replanning flight paths and DCB decisions in any given context, while also providing UAM operators with relevant inputs for autonomous decision-making. Simulation-based verification activities in representative conditions show that the proposed UTM system has the ability to effectively resolve overload instances and minimize potential conflicts in low-altitude airspace, with an operationally acceptable running time. We conclude that the proposed hybrid algorithm can support a successful implementation of UAM services in and around cities, and it has high potential to address critical airspace resource constraints also in traditional Air Traffic Flow Management (ATFM).

Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions

The study of the environmental transition of the aviation sector calls for prospective traffic scenarios. Detailed traffic and emissions inventories are often needed to refine the available analyses and to enable the simulation of regionalised scenarios. In the past studies, these are generally based on commercial, proprietary traffic data, making their dissemination problematic and reducing the reproducibility of the science produced. Open-source alternatives do exist but with limited geographical coverage. This study bridges this gap by presenting an innovative open-source dataset detailing 2019's global air traffic flows and associated CO2 emissions. A comprehensive approach that compiles diverse flight data sources is presented. The remaining data gaps are addressed by constructing a route network through systematic Wikipedia parsing and by estimating the related traffic using socio-economic data. Then, an aircraft performance model to estimate CO2 emissions is implemented. This methodology promises reinforced reproducibility and broader data accessibility in aviation environmental research. Several reference datasets are used to evaluate the accuracy of the open-source dataset. Despite various levels of accuracy for individual routes, major traffic flows are well estimated at the country and continental levels, albeit with room for refinement to ensure consistent data reliability. To facilitate the exploration of the dataset, the AeroSCOPE tool has been developed. To initiate research prospects, use cases of this dataset are proposed, concerning the network potential of electric and hydrogen-powered aircraft and inequalities in air transport.

Reviews and Responses for Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions

See detailed reviews and responses in the PDF file. DOI for the original paper: https://doi.org/10.59490/joas.2024.7365

Visual analysis of changes in European air transport during the COVID-19 pandemic from interactive maps

Abstract The paper explores the substantial decline in European air transport during 2020, while employing interactive maps for visual analysis. According to the International Civil Aviation Organization’s (2020) economic analysis, there was a sharp 60% global reduction in passenger traffic during the combined second, third, and fourth quarters, equivalent to about 2.7 billion fewer passengers than in 2019. The established air traffic flow, which developed over decades, faced partial disruption due to COVID-19 restrictions. Consequently, the aviation industry strategically focused on restructuring to ensure the sustained operation of major air transport routes. Using OpenSky Network data and a Google Sheets environment for storage, our study utilizes the FlowmapBlue interactive platform to visualize the 2020 European airspace, and to define key air traffic corridors. Despite the substantial decline, the visualization reveals resilient routes and crucial connections, underscoring the imperative of preserving these links for effective crisis response in the future.

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.

Enhancing air traffic operational efficiency by reducing network scale

The growing demand for air travel has led to the saturation of air traffic networks. Conventional methods of adding routes to alleviate congestion and reduce delays may not achieve the desired effect and even degrade system performance. In this paper, we explore the application of Braess’s Paradox in the reduction of air traffic networks. This counterintuitive phenomenon shows that adding new connections to a network can actually increase the overall network pressure. This study uses Hidden Markov methods and the Viterbi algorithm to match air traffic flow with routes, a machine learning approach and a mathematical method to construct cost functions for flight time and traffic volume, and finally uses genetic algorithm and the A* algorithm to detect Braess’s Paradox edges. We uses ADS-B data from the busy month of July 2019 for a case study of the air traffic network over the UK airspace. The results show that Braess’s Paradox is also applicable to multi-flight level air route networks. Removing such network edges can improve system performance. In one day’s case, the total flight time of the day’s traffic volume decreased from 11509.24 minutes to 10459.97 minutes. This equates to an average savings of 4.99 minutes of flight time per flight, which is significant in controlling delay performance.

Harnessing Automation and IIoT in Air Traffic Control for Advanced Efficiency and Safety

The combination of automation and Industrial Internet of Things (IIoT) technology has transformed air traffic control (ATC), improving efficiency and safety in aviation. This article investigates the varied influence of automation and IIoT on ATC operations, emphasizing their importance in maximizing airspace use, boosting situational awareness, and limiting operational hazards. The advent of automatic flight data processing, radar surveillance, and automated decision-support technologies has reduced controller workload and increased operational efficiency in ATC. IIoT sensors and devices enable ATC systems to integrate real-time data on aircraft positions, weather, and runway status, allowing controllers to make more informed decisions and manage air traffic flow. Finally, automation and IIoT have evolved into critical tools for upgrading air traffic control, assisting controllers in managing airspace congestion, optimizing operational performance and safely and quick, efficient traffic movement. This study examines how automation and IIoT alter ATC operations, resulting in a more efficient, robust, and sustainable aviation environment. Keywords— Industrial Internet of Things (IIoT), Air Traffic Control (ATC), Automation, Operational Hazards, Radar surveillance, IoT sensors, Aviation environment.

Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network

Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network

Teaching Artificial Intelligence Good Air Traffic Flow Management

Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.

Short-Term Air Traffic Flow Prediction Based on CEEMD-LSTM of Bayesian Optimization and Differential Processing

With the rapid development of China’s civil aviation, the flow of air traffic in terminal areas is also increasing. Short-term air traffic flow prediction is of great significance for the accurate implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow prediction, this paper proposes a short-term air traffic flow prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) of the Bayesian optimization algorithm and data differential processing. Initially, the model performs CEEMD on the short-term air traffic flow series. Subsequently, to improve prediction accuracy, the data differencing is employed to stabilize the time series. Finally, the smoothed sequences are, respectively, input into the LSTM network model optimized by the Bayesian optimization algorithm for prediction. After data reconstruction, the final short-term flow prediction result is obtained. The model proposed in this paper is verified by using the data from Shanghai Pudong International Airport. The results show that the evaluation indexes of the prediction accuracy and fitting degree of the model, RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R2 (Coefficient of Determination), are 0.336, 0.239, and 97.535%, respectively. Compared to other classical time-series prediction models, the prediction accuracy is greatly improved, which can provide a useful reference for short-term air traffic flow prediction.

Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

AbstractAccurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors.

Prediction of ATFM impact for individual flights: A machine learning approach

Air Traffic Flow Management regulations consist of issuing pre-departure ground delays to deal with demand-capacity imbalances in the airspace, smoothing out the demand at congested infrastructures. This results in discrepancies between the initially planned and executed (regulated) flights, negatively impacting operational efficiency and economic performance. To mitigate downstream effects, airspace users need to assess the severity and impact of these regulations when defining their operational plan. It is crucial to anticipate these potential network issues and plan for mitigation measures during the pre-tactical phase (the day before operations (D−1)). This article presents four independent machine learning models to estimate the impact of Air Traffic Flow Management regulations for individual flights during this pre-tactical phase. An integrated view is proposed to combine the results of the different models, displaying the appropriate level of information. Two models are designed to provide information about the probability of a given flight being affected by Air Traffic Flow Management regulations and the reason for this regulation (airport or airspace congestion). These models have reported accuracy levels between 82% and 87%. The remaining models estimate the impact of the delay (amount of Air Traffic Flow Management delay issued to the flight if regulated), with a Mean Absolute Error of 9.5 min when predicting this amount of delay. The SHapely Additive exPlanations analysis is used to identify the most important factors for detecting Air Traffic Flow Management regulations for individual flights during the pre-tactical phase from a data-driven perspective. An integrated view is proposed to create a system which combines the results of the four different models, displaying the appropriate level of information and avoiding an overload of information. Finally, the results are compared against models considering only static or post-operational data.

Pre‐tactical convection prediction for air traffic flow management using LSTM neural network

AbstractThis paper aims to explore machine learning techniques for post‐processing high‐resolution Numerical Weather Prediction (NWP) products for the early detection of convection. Data from the Arome Ensemble Prediction System and satellite observations from the Rapidly Developing Thunderstorm (RDT) product by Météo‐France are used to train a recurrent neural network model to predict areas of total convection and moderate convection. The learning task is formulated as a binary classification problem using a long short‐term memory (LSTM) network architecture. Results from the LSTM model are compared with an object‐based probabilistic approach to forecast convection using metrics such as a receiver operating characteristics (ROC) curve, the Brier score and reliability. Results indicate that the LSTM model performs similarly to the object‐based probabilistic benchmark when classifying moderate convection areas and shows improved skill when classifying areas of total convective. Finally, the LSTM model results are presented within an air traffic management context to showcase the potential use of machine learning models within an operational application.