Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Lee, Jooyoung; Lee, Sang-Jin; Hwang, Inseok

doi:10.2514/1.g001412

Cited by 11 publications

(10 citation statements)

References 20 publications

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“…In both concepts, the accuracy of ETA significantly affects the system performance, and more accurate ETAs are expected. (Bai et al, 2016; Lee et al, 2016). The ATC systems usually rely on each aircraft's ETA information, but this paper demonstrates that the accuracy of ETA depends on the wind information each aircraft uses and aircraft type.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Speed Prediction Error on Oceanic Flights

Mori

2019

J. Navigation

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The accuracy of speed prediction error to the next waypoint is a key factor in maintaining longitudinal separation on oceanic routes. This estimation is often used by air traffic control to estimate the future position of aircraft, and the estimation errors result in potential separation infringement. While most aircraft can calculate the estimated time at each future waypoint using the onboard Flight Management System, the factors affecting the inaccuracy of the estimation require more clarification. This paper investigates the accuracy of the speed prediction error on oceanic routes and examines the main factor of error using airline flight data. The results show that wind prediction error is a main source of speed prediction error, and significant differences of the speed prediction error among airlines and aircraft types were observed.

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Section: Discussionmentioning

confidence: 99%

Analysis of Speed Prediction Error on Oceanic Flights

Mori

2019

J. Navigation

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“…Since the motion of the aircraft is an SLHS with different flight modes, it is more reasonable to establish corresponding motion equations based on different flight modes. Therefore, many scholars use PMM to make predictions under SLHS [14,45,46]. Lymperopoulos et al [14] used the point mass model to model the aircraft under the stochastic hybrid system, combining the continuous state from the physical movement of the aircraft and the discrete state from the flight plan and the flight management system (FMS).…”

Section: Kinetic Modelmentioning

confidence: 99%

“…Lymperopoulos et al [14] used the point mass model to model the aircraft under the stochastic hybrid system, combining the continuous state from the physical movement of the aircraft and the discrete state from the flight plan and the flight management system (FMS). Lee et al [46] proposed an aircraft tracking and estimated time of arrival prediction algorithm based on a stochastic hybrid system model, deduced a nonlinear dynamics model of the continuous motion of the aircraft in each flight mode, and used the continuous state transition probability to model the discrete transition between flight modes.…”

Section: Kinetic Modelmentioning

confidence: 99%

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Zeng

Chu

et al. 2022

Aerospace

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Aircraft four dimensional (4D, including longitude, latitude, altitude and time) trajectory prediction is a key technology for existing automation systems and the basis for future trajectory-based operations. This paper firstly summarizes the background and significance of the trajectory prediction problems and then introduces the definition and basic process of trajectory prediction, including four modules: preparation, prediction, update, and output. In addition, the trajectory prediction methods are summarized into three types: the state estimation model, the Kinetic model, and the machine learning model, and in-depth analysis of various models is carried out. Further, the relevant databases required for the study are introduced, including the aircraft performance database, aircraft monitoring database, and meteorological database. Finally, challenges and future development directions of the current trajectory prediction problem are summarized.

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“…Since the motion of the aircraft is an SLHS with different flight modes, it is more reasonable to establish motion equations corresponding to different flight modes. Therefore, many scholars use PMM to predict under SLHS [27][28][29]. Unified and comprehensive intent information is necessary for trajectory prediction.…”

Section: Kinetic Modelsmentioning

confidence: 99%

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

Zeng

Chu

et al. 2021

Aerospace

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Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model’s performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction.

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Hybrid System Modeling and Estimation for Arrival Time Prediction in Terminal Airspace

Cited by 11 publications

References 20 publications

Analysis of Speed Prediction Error on Oceanic Flights

Analysis of Speed Prediction Error on Oceanic Flights

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

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