A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

Wang, Zhengyi; Liang, Man; Delahaye, Daniel

doi:10.1016/j.trc.2018.07.019

Cited by 83 publications

(69 citation statements)

References 17 publications

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“…Several TP approaches may be applied, such as ML-enhanced point-mass models TP (Alligier et al, 2015(Alligier et al, , 2013 or data-driven approaches (Alligier and Gianazza, 2018;de Leege et al, 2013;Tastambekov et al, 2013). In this work, we apply an Artificial Neural Net (ANN) TP, following previous works in the literature (Verdonk Gallego et al, 2018b;Wang et al, 2018).…”

Section: Expected Trajectory Based On Observed Trafficmentioning

confidence: 99%

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A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Gallego

Comendador

Carmona

et al. 2019

Transportation Research Part C: Emerging Technologies

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Air Traffic Management is evolving towards a Trajectory-Based Operations paradigm. Trajectory prediction will hold a key role supporting its deployment, but it is limited by a lack of understanding of air traffic associated uncertainties, specifically contextual factors. Trajectory predictors are usually based on modelling aircraft dynamics based on intrinsic aircraft features. These aircraft operate within a known air route structure and under given meteorological conditions. However, actual aircraft trajectories are modified by the air traffic control depending on potential conflicts with other traffics. This paper introduces surrounding air traffic as a feature for ground-based trajectory prediction. The introduction of air traffic as a contextual factor is addressed by identifying aircraft which are likely to lose the horizontal separation. For doing so, this paper develops a probabilistic horizontal interdependency measure between aircraft supported by machine learning algorithms, addressing time separations at crossing points. Then, vertical profiles of flight trajectories are characterised depending on this factor and other intrinsic features. The paper has focused on the descent phase of the trajectories, using datasets corresponding to an en-route Spanish airspace volume. The proposed interdependency measure demonstrates to identify in advance conflicting situations between pairs of aircraft for this use case. This is validated by identifying associated air traffic control actions upon them and their impact on the vertical profile of the trajectories. Finally, a trajectory predictor for the vertical profile of the trajectory is developed, considering the interdependency measure and other operational factors. The paper concludes that the air traffic can be included as a factor for the trajectory prediction, impacting on the location of the top of descent for the specific case which has been studied.

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Section: Expected Trajectory Based On Observed Trafficmentioning

confidence: 99%

“…For doing so, we used an ANN TP to model Θ (see Eq. 29and (Verdonk Gallego et al, 2018b;Wang et al, 2018). The neural networks had the same structure than the horizontal ones, and the inputs for them were , 1 , RFL, the wake vortex and at 1 = 0.…”

Section: Trajectory Vertical Profile Characterisationmentioning

confidence: 99%

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Gallego

Comendador

Carmona

et al. 2019

Transportation Research Part C: Emerging Technologies

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“…Quite a lot of studies on the prediction of ETA are performed in the automotive and aviation industries [4][5][6]. To improve the information system that notifies passengers about the expected time of arrival of buses to stops along a route, artificial neural networks have been successfully applied [4].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The results obtained confirm that the proposed system of forecasting generates more accurate predictions with a much smaller standard deviation than existing systems in EUROCON-TROL. Authors of [6], in order to predict a 4D trajectory of aircraft routes at a terminal maneuvering area, have used the new hybrid model that processes data based on clustering and Multi-Cells Neural Network (MCNN) to predict ETA. Comparing the obtained results with the forecasts constructed using a Multiple Linear Regression (MLR) has proven the effectiveness of the proposed hybrid machine learning model.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Forecasting the estimated time of arrival for a cargo dispatch delivered by a freight train along a railway section

Prokhorchenko

Panchenko

Пархоменко

et al. 2019

EEJET

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“…Machine learning is one of the most prevailing techniques in data-driven approaches. Typical machine learning models for 4D TP include linear regression models and Neural Networks (NNs) models [17]. In terms of 2D CD, preliminary efforts have been done by the authors and the validity of ML approaches was proved [18].…”

Section: Introductionmentioning

confidence: 99%

Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning

Wang¹,

Liang²,

Delahaye³

2020

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-At)

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Trajectory prediction with Closest Point of Approach (CPA) concept is a fundamental element of aircraft Conflict Detection (CD) problem. Conventional motion-based CPA prediction model generally assumes that aircraft is flying in straight line with constant speed. But due to environment uncertainties and ground speed changes, this conventional method frequently lacks accuracy in the real world with a high rate of false alarms and missed detections. In this paper, we introduce a novel automated data-driven CD framework with Machine Learning (ML) for 3D CPA prediction in a lookahead time of less than 20 minutes. Firstly, a 3D CPA model with cylindrical norm is proposed as the baseline. Then, data preparation with Mode-S observation data in France is explained, including data collection and data processing, to convert raw Mode-S data to the closeto-reality dataset. Furthermore, feature engineering is applied to build up a feature set with 16 features. Finally, four prevailing ML models are used to predict the time, horizontal distance and vertical distance of CPA in 3D airspace. CD is conducted based on the predicted values. The prediction and CD results show that all proposed ML models outperform the baseline model. Especially, GBM and FFNNs could strongly enhance the performance of CD.

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A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

Cited by 83 publications

References 17 publications

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Forecasting the estimated time of arrival for a cargo dispatch delivered by a freight train along a railway section

Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning

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