4D Trajectory Research Articles

A Novel Trajectory Prediction Method Based on CNN, BiLSTM, and Multi-Head Attention Mechanism

A four-dimensional (4D) trajectory is a multi-dimensional time series that embodies rich spatiotemporal features. However, its high complexity and inherent uncertainty pose significant challenges for accurate prediction. In this paper, we present a novel 4D trajectory prediction model that integrates convolutional neural networks (CNNs), bidirectional long short-term memory networks (BiLSTMs), and multi-head attention mechanisms. This model effectively addresses the characteristics of aircraft flight trajectories and the difficulties associated with simultaneously extracting spatiotemporal features using existing prediction methods. Specifically, we leverage the local feature extraction capabilities of CNNs to extract key spatial and temporal features from the original trajectory data, such as geometric shape information and dynamic change patterns. The BiLSTM network is employed to consider both forward and backward temporal orders in the trajectory data, allowing for a more comprehensive capture of long-term dependencies. Furthermore, we introduce a multi-head attention mechanism that enhances the model’s ability to accurately identify key information in the trajectory data while minimizing the interference of redundant information. We validated our approach through experiments conducted on a real ADS-B trajectory dataset. The experimental results demonstrate that the proposed method significantly outperforms comparative approaches in terms of trajectory estimation accuracy.

Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information

In this study, a 4D trajectory weather (4DT-Wx) prototype system was developed and evaluated for effective weather information integration in trajectory-based operation (TBO) environments. The system has two key distinguishing features: multi-model-based trajectory services and buffer zone information provision. We constructed a distributed processing system using Apache Spark, enabling the efficient processing of large-scale weather data. The performance evaluation demonstrated excellent scalability and efficiency in processing large-scale data. An analysis of the buffer configurations highlighted that buffer zone information is valuable in decision-making processes and has the potential to enhance the system performance. The system’s practical applicability is presented through visualizations of the extracted weather information. This system is expected to enhance aviation safety and operational efficiency, providing a foundation for addressing increasingly complex weather conditions and flight scenarios in the future. The approach presented in this study marks a significant step toward effective TBO implementation and the advancement of future air traffic management. The evaluation of the 4DT-Wx system analyzed the accuracy of weather data processing and the performance of distributed processing, finding that the temperature (T) estimation had the highest accuracy, and that the parallel processing using Apache Spark was most effectively modeled by Ahmed et al.’s model. The findings suggest the potential for further optimization in integrating various weather models and developing algorithms to enhance their utilization.

Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model

Abstract With the increase of low-altitude flight tasks, the importance of research on low-altitude flight safety and efficient operation has gradually become prominent. Therefore, it is necessary to try to establish a low-altitude airspace flight path pre-planning method based on 4D trajectory prediction, which can effectively support the above research. In order to improve the integrity, accuracy and reliability of the long-range trajectory prediction results of large fixed wing UAV (LFW UAV), based on the standard neural network as the basic concept to form a fusion model of improved convolutional neural network (CNN) and bidirectional gated recurrent neural network (BIGRU). At the same time, the flight trajectory fitting preprocessing is added to build the completed multilayer cyclic convolutional improvement model (MCCI). The experimental results show that the MCCI model has obvious advantages in terms of prediction accuracy, deviation range and predictable duration when dealing with the LFW UAV 4D trajectory prediction problem, and better reflects the characteristics of 3D position and spatiotemporal elements. The comparative analysis shows that the error values of the MCCI model are smaller than those of the comparison prediction models in any dimension, and the prediction results have better fit and model performance.

Cascade MFAC-based data-driven control for aircraft in 4D trajectory tracking

This study aims to explore a trajectory tracking control approach in four-dimensional (4D) trajectory operation by employing cascade model-free adaptive control (MFAC) technique. The primary objective is to tackle the complexities associated with modeling aircraft mechanisms in the context of tracking control. By utilizing measurable input and output data, an equivalent dynamic linearized data model of the aircraft is constructed. Subsequently, position and speed controllers are developed based on MIMO-CFDL-MFAC and SISO-CFDL-MFAC, respectively. These controllers are then integrated to form an active-follower control system. Finally, a numerical simulation was conducted to track the entire 4D trajectory, encompassing climb, cruise, and descent stages, using historical data from flight CX8176 as a reference trajectory. Unlike previous researches, the proposed method eliminates the requirement for an aircraft mechanism model in the controller design, and it is verified using real flight data as a reference trajectory. The findings demonstrate that the cascade MFAC method can effectively minimize trajectory tracking errors, while also offering advantages in terms of energy conservation and emission reduction.

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).

Incorporating Airspace Constraints in Multi-phase 4D Trajectory Optimization Based on Improved Adaptive Mesh Refinement Method

Incorporating Airspace Constraints in Multi-phase 4D Trajectory Optimization Based on Improved Adaptive Mesh Refinement Method

An improved transformer‐based model for long‐term 4D trajectory prediction in civil aviation

AbstractFour‐dimensional trajectory prediction is a crucial component of air traffic management, and its accuracy is closely related to the efficiency and safety of air transportation. Although long short‐term memory (LSTM) or its variants have been widely used in recent studies, they may produce unacceptable results in long‐term prediction due to the iterative output that accumulates error. To address this issue, a transformer‐based long‐term trajectory prediction model is proposed here, which utilizes the self‐attention mechanism to extract time series features from historical trajectory data. For long‐term prediction scenarios, we a trajectory stabilization module is introduced to ensure the stationarity of the time series for better predictability. Additionally, the transformer output strategy is improved to generate the prediction sequence by a single step instead of serial dynamic decoding, thus effectively enhancing the precision and inference speed. The proposed model is validated using real data obtained from China's Southwest Air Traffic Management Bureau. The experimental results demonstrate that this model outperforms the benchmark model. Further ablation experiments and visualizations are performed to analyze the impact of trajectory stabilization and one‐step inference strategy.

4D Trajectory Planning Based on Fast Marching Square for UAV Teams

4D Trajectory Planning Based on Fast Marching Square for UAV Teams

A deep learning-based approach for predicting in-flight estimated time of arrival

Predictability is key for efficient and safe air traffic management. In particular, accurately estimating time of arrival for current passenger flights may help terminal controllers to plan ahead and optimize airport operations in terms of safety and resource allocation. While traditional physics-based simulations are still widely used, they are complex to model and often fail to include many factors affecting the progress of a flight. In this paper, we propose a deep learning approach based on LSTM that leverages the 4D trajectory of the flight and weather data at the destination airport, to accurately predict estimated time of arrival. We evaluate our model on flights arriving at Adolfo Suárez-Madrid Barajas airport (Spain), in the first three quarters of 2022, achieving a mean absolute error of 2.65 min over the entire flight and reporting competitive short- and long-term predictions at different spatial and temporal horizons.

Four-Dimensional Aircraft Trajectory Prediction Based on Generative Deep Learning

Aircraft four-dimensional (4D) trajectory prediction (TP) is among the critical techniques of current existing automation systems in air traffic management (ATM), which is also the basis of future airspace operation models. This paper proposes a 4D TP method based on a conditional tabular generative adversarial network (CTGAN). The model is trained and tested using historical 4D trajectory data from the Hong Kong region. It is then compared with the most extensively researched conditional generative adversarial network (CGAN) and LSTM methods in the field of TP. The results show that Jensen–Shannon (JS) divergence, maximum mean discrepancy (MMD), and mean Euclidean distance (MED) of the model evaluation metrics for the selected route are 0.0539, 0.0150, and 0.0085, respectively. This confirms that the model for predicting 4D trajectory can accurately learn complex airspace’s trajectory distribution characteristics. Compared with the CTGAN algorithms shown in previous studies, the enhanced CTGAN approach developed in our research can reduce around 10% running time and 20% MED. Compared to CGAN, it results in an 81.55% reduction in MED. Compared to LSTM, when predicting with a 10-minute time span, the average mean absolute error for parameters latitude (Lat), longitude (Lon), geometric height (GH), and ground speed (GS) decreases by 7.76, 84.48, 81.98, and 36.51%, respectively.

Data-Driven 4D Trajectory Prediction Model Using Attention-TCN-GRU

With reference to the trajectory-based operation (TBO) requirements proposed by the International Civil Aviation Organization (ICAO), this paper concentrates on the study of four-dimensional trajectory (4D Trajectory) prediction technology in busy terminal airspace, proposing a data-driven 4D trajectory prediction model. Initially, we propose a Spatial Gap Fill (Spat Fill) method to reconstruct each aircraft’s trajectory, resulting in a consistent time interval, noise-free, high-quality trajectory dataset. Subsequently, we design a hybrid neural network based on the seq2seq model, named Attention-TCN-GRU. This consists of an encoding section for extracting features from the data of historical trajectories, an attention module for obtaining the multilevel periodicity in the flight history trajectories, and a decoding section for recursively generating the predicted trajectory sequences, using the output of the coding part as the initial input. The proposed model can effectively capture long-term and short-term dependencies and repetitiveness between trajectories, enhancing the accuracy of 4D trajectory predictions. We utilize a real ADS-B trajectory dataset from the airspace of a busy terminal for validation. The experimental results indicate that the data-driven 4D trajectory prediction model introduced in this study achieves higher predictive accuracy, outperforming some of the current data-driven trajectory prediction methods.

Predicting Air Traffic Congestion under Uncertain Adverse Weather

This paper presents an approach for integrating uncertainty information in air traffic flow management at the tactical phase. In particular, probabilistic methodologies to predict sector demand and sector congestion under adverse weather in a time horizon of 1.5 h are developed. Two sources of uncertainty are considered: the meteorological uncertainty inherent to the forecasting process and the uncertainty in the take-off time. An ensemble approach is adopted to characterize both uncertainty sources. The methodologies rely on a trajectory predictor able to generate an ensemble of 4D trajectories that provides a measure of the trajectory uncertainty, each trajectory avoiding the storm cells encountered along the way. The core of the approach is the statistical processing of the ensemble of trajectories to obtain probabilistic entry and occupancy counts of each sector and their congestion status when the counts are compared to weather-dependent capacity values. A new criterion to assess the risk of sector overload, which takes into account the uncertainty, is also defined. The results are presented for a historical situation over the Austrian airspace on a day with significant convection.

Model of Multi-Algorithmic-Based Optimization of 4D Approach Trajectory under Thunderstorm Weather

Thunderstorms are recognized as perilous meteorological phenomena characterized by irregular and nonlinear movement, posing significant risks to approaching aircraft and necessitating technical methods to ensure safety to the aviation operations. This research specifically addresses the challenges associated with aircraft during the approach segment and introduces a multialgorithmic model focusing on the optimization of 4D approach trajectory. Firstly, the artificial neural network intelligent model was used to predict the thunderstorm movement track. Secondly, the multialgorithmic model combined by the rapidly exploring random tree with artificial potential field was built to plan the trajectory of the approaching aircraft under thunderstorm weather, and then, the mean filter was adopted to smooth the simulated approaching trajectory. Finally, the reliability of the model with a real case study was demonstrated. After optimized simulation by predicting the thunderstorm weather and trajectory-optimized multialgorithmic model mentioned above, the approach trajectory can be outputted successfully, but with some distortions, postprocessing with the mean filter results in a remarkably smooth approach trajectory, providing enhanced feasibility and efficiency for pilots navigating through thunderstorm weather conditions. It is ultimately proved that refined 4D trajectory operations under hazardous weather conditions hold substantial significance in advancing aviation safety and operational effectiveness.

A Study of Robustness between Two Strategic 4D Trajectory Plannings

A Study of Robustness between Two Strategic 4D Trajectory Plannings

Robust optimization integrating aircraft trajectory and sequence under weather forecast uncertainty

Integration of trajectory optimization into sequence optimization is required for next-generation Arrival Managers (AMANs) to support Collaborative Decision-Making (CDM) and implementation of user-preferred 4D trajectories. In addition, considering uncertainty in the optimization is also necessary for making more robust decisions. To achieve these aims, this study proposes a method to integrate the trajectory and sequence of approach aircraft in a single optimization framework and calculate optimal robust solutions against weather forecast uncertainty. This uncertainty is quantified utilizing the ensemble weather forecast and the robust optimizations for trajectory and sequence are formulated in an ensemble approach. To connect the two optimizations, we introduce the so-called performance surfaces, which represent the characteristics of the optimal trajectory. The resulting integrated Trajectory and Sequence (T&S) optimization is a combination of the robust Optimal Control (OC) and Mixed-Integer Nonlinear Programming (MINLP). The MINLP problem is relaxed to the corresponding Nonlinear Programming (NLP) problem to reduce computational costs. In the case study, the trajectory and sequence are simultaneously optimized for two different objectives: the maximum throughput at the merging point and the minimum fuel burn while maintaining the inter-aircraft separation.

CO2 and non-CO2 balanced Environmental Scores Module for flight performance evaluation and optimisation

The SESAR2020 exploratory research (ER4) programme CREATE (Grant 890898) developed a climate and weather aware Concept of Operations (ConOps) which encompasses a multi-aircraft 4D trajectory optimisation framework, which utilises a CO2 and non-CO2 balanced Environmental Scores Module (ESM) for the en-route flight phase. The ESM provides a computational method to evaluate the “greenness” of aircraft trajectories. Some components related to the internal ESM scoring are based on expert judgement, which is in line with the technology readiness level (TRL) 1 of the solution. Fast-time simulations were performed to demonstrate the proof-of-concept of the ESM in a multi-aircraft tactical optimisation scenario in the North-Atlantic region. The results show that, because of the simplicity of the metric, the ESM could be well used for trajectory optimisation and tactical replanning, and most likely as well as flight and ATC sector environmental performance evaluations.

Interpretable Tracking and Detection of Unstable Approaches Using Tunnel Gaussian Process

Approach and landing are phases of flight with the highest accident risk. Advanced instruments and procedures have been developed to provide precise navigation for a stabilized approach and landing. With the proliferation of sensing techniques, real-time 4D trajectories can be captured at higher spatial-temporal resolution and enable data-driven decision-making for air traffic controllers (ATCO). This research attempts to augment the existing rule-based stable approach criteria using data-driven and interpretable tunnel Gaussian process (TGP) models to probabilistically characterize the 4D approach and landing parameters. The TGP explicitly and continuously models the underlying distribution of approach and landing parameters and their interrelations. In addition, it provides a comprehensible probabilistic description of anomalies in approach and landing parameters. Based on the trained TGP, we infer the landing parameters of go-around tracks recorded by the advanced surface movement guidance and control system (A-SMGCS) and analyze their adherence to stabilized approach criteria. Empirical results show that anomalous scores were in line with the factors (as reported to ATCOs) in all go-around data in the test dataset, between 0.5 NM (missed approach point) to 7.6 NM from the touchdown threshold, and provides better probabilistic insights of non-compliance, comparing to existing work. Hence, the proposed TGP can provide a ground-based safety net for the compliance of stable approaches. Furthermore, the proposed TGP-based anomaly tracking methods can be directly applied to other types of landing systems (e.g., GNSS landing system and RNAV approaches).

An ETA-Based Tactical Conflict Resolution Method for Air Logistics Transportation

Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results.

Dynamic Models with Sigmoid Corrections to Generation of an Achievable 4D-Trajectory for a UAV and Estimating Wind Disturbances

For an unmanned aerial vehicle (UAV) of an aircraft type, the problems of planning achievable trajectories as well as robust control under wind disturbances are considered. A computationally simple method for compiling a primary non-smooth 4D trajectory is proposed. Its segments connect the given waypoints and determine the desired average velocity in various sections. Instead of time-consuming methods of analytical smoothing of broken path joints using polynomials, a tracking differentiator with S-shaped smooth and limited sigmoid corrective actions is developed. This virtual dynamic model provides natural smoothing of the primary trajectory considering the design constraints on the velocity, acceleration, and thrust of the UAV. The tracking differentiator variables create an achievable trajectory and are used to synthesize the UAV tracking system. To compensate for the action of wind disturbances on the UAV, a disturbance observer is developed. It is a replica of the equations of the control plant model, which are directly affected by external uncontrolled disturbances. These algorithms also use sigmoid corrections. Unlike standard disturbances observers, this approach does not require the development of a dynamic model of external disturbances and does not assume their smoothness. The effectiveness of the developed algorithms was confirmed by numerical simulation.