Adaptive interacting particle system algorithm for aircraft conflict probability estimation

Jacquemart, Damien; Mitard, J.

doi:10.1016/j.ast.2016.05.027

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…The stochasticity represents the deviation from the initial line due to wind, tracking, navigation or control. However, it can only be applied to simple 2-dimensional scenarios of LoS probability estimations (Jacquemart & Morio, 2016) between straight trajectories with constant altitude and speed. Murça and de Oliveira (2020) cluster the approaching trajectories in São Paulo Airport to identify the main trajectory patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Generative Modelling of Aircraft Trajectories in Terminal Maneuvering Areas

et al. 2022

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Section: Literature Reviewmentioning

confidence: 99%

Deep Generative Modelling of Aircraft Trajectories in Terminal Maneuvering Areas

et al. 2022

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“…Prandini et al (2000) proposed two models to track perturbations on aircraft trajectories: one for calculating the probability of conflict, and a second which combines a deterministic motion with a Brownian motion perturbation. A similar perturbation model was proposed by Jacquemart and Morio (2016) where the authors calculated the probability of conflict and, using rare event probabilities, the probability of collision. Irvine (2002) proposed a geometrical approach for estimating conflict probabilities.…”

Section: Aircraft Conflict Resolution Under Uncertaintymentioning

confidence: 99%

Robust aircraft conflict resolution under trajectory prediction uncertainty

Dias¹,

Rey²

2020

Preprint

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We address the aircraft conflict resolution problem under trajectory prediction uncertainty. We consider that aircraft velocity vectors may be perturbed due to weather effects, such as wind, or measurement errors. Such perturbations may affect aircraft trajectory prediction which plays a key role in ensuring flight safety in air traffic control. Our goal is to solve the aircraft conflict resolution problem in the presence of such perturbations and guarantee that aircraft are separated for any realization of the uncertain data. We propose an uncertainty model wherein aircraft velocities are represented as random variables and the uncertainty set is assumed to be polyhedral. We consider a robust optimization approach and embed the proposed uncertainty model within state-of-the-art mathematical programming formulations for aircraft conflict resolution. We then adopt the approach of Bertsimas and Sim (2004) to formulate the robust counterpart. We use the complex number reformulation and the constraint generation algorithm proposed by Dias et al. (2020a) to solve the resulting nonconvex optimization problem on benchmarking instances of the literature. Our numerical experiments reveal that perturbations of the order of ±5% on aircraft velocities can be accounted for without significantly impacting the objective function compared to the deterministic case. Our tests also show that for greater levels of uncertainty, several instances fail to admit conflict-free solutions, thus highlighting existing risk factors in aircraft conflict resolution. We attempt to explain this behavior by further analyzing pre-and post-optimization aircraft trajectories. Our findings show that most infeasible instances have both a relatively low total aircraft pairwise minimal distance and a high number of conflicts.

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“…It is shown in H. Blom's research [9] that the interacting particle system (IPS) algorithm is efficient when more complex air traffic scenarios and more advanced conflict 2 Journal of Advanced Transportation zones have to be taken into account. Damien Jacquemart et al [10] proposed an adaptive algorithm to significantly increase the convergence rate. Moreover, some hierarchical IPS algorithms proposed in research are considered for Free Flight modeling described by advanced stochastic Petri nets [11].…”

Section: Introductionmentioning

confidence: 99%

A SVM Approach of Aircraft Conflict Detection in Free Flight

Xurui

Wen

et al. 2018

Journal of Advanced Transportation

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Probabilistic conflict detection methods typically require high computational burden to deal with complex multiaircraft conflict detection. In this article, aircraft conflict detection is considered as a binary classification problem; therefore, it can be solved by a pattern recognition method. A potential conflict would be identified, as long as its flight data features are extracted and fed to a classifier which has been trained by a large number of flight datasets. Based on this, a new method based on support vector machine (SVM) is employed to detect multiaircraft conflict in “Free Flight” airspace and to estimate the conflict probability. For that purpose, the current positions, velocity vectors, and predicted look-ahead time are selected as detection factors, and the detection model is established by SVM to detect aircraft conflict within look-ahead time during short and medium terms. Moreover, conflict probabilities are determined by the sigmoid function mapping method. Nevertheless, false alarm rate is always a first and foremost problem that troubles air traffic controllers. For the purpose of reducing false alarm rates, Synthetic Minority Over-sampling Technique (SMOTE) method is used to handle imbalanced datasets. Extensive simulation results are presented to illustrate the rationality and accuracy of this method.

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Adaptive interacting particle system algorithm for aircraft conflict probability estimation

Cited by 16 publications

References 22 publications

Deep Generative Modelling of Aircraft Trajectories in Terminal Maneuvering Areas

Deep Generative Modelling of Aircraft Trajectories in Terminal Maneuvering Areas

Robust aircraft conflict resolution under trajectory prediction uncertainty

A SVM Approach of Aircraft Conflict Detection in Free Flight

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