Aircraft Trajectory Prediction Made Easy with Predictive Analytics

Ayhan, Samet; Samet, Hanan

doi:10.1145/2939672.2939694

Cited by 115 publications

(60 citation statements)

References 32 publications

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“…Comparing Table. 2 with Table. 1, the accuracy of the TMN predictions are slightly reduced, but are still more accurate than [32], in which the error has increased dramatically indicating that the baseline model has not adapted well to the changed weather conditions.…”

Section: The Results Inmentioning

confidence: 97%

“…The remaining 78,220 trajectories were used for testing. Based on the recommendations provided in [32,55,56], we measure the following three error metrics for the aircraft trajectory prediction experiment. The trajectory prediction errors are calculated for each observed radar track point in each input trajectory segment.…”

Section: Experiments 1: Terminal Area Air Traffic Predictionmentioning

confidence: 99%

“…These examples are not used for training the model. We compare the proposed model against the HMM[32], which explicitly incorporates weather information.…”

mentioning

confidence: 99%

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Tree Memory Networks for modelling long-term temporal dependencies

et al. 2018

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In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question answering, part-of-speech tagging and machine translation. However this success in modelling short term dependencies has not successfully transitioned to application areas such as trajectory prediction, which require capturing both short term and long term relationships. In this paper, we propose a Tree Memory Network (TMN) for jointly modelling both long term relationships between multiple sequences and short term relationships within a sequence, in sequence-to-sequence mapping problems. The proposed network architecture is composed of an input module, controller and a memory module. In contrast to related literature which models the memory as a sequence of historical states, we model the memory as a recursive tree structure. This structure more effectively captures temporal dependencies across both short and long term time periods through its hierarchical structure. We demonstrate the effectiveness and flexibility of the proposed TMN in two practical problems: aircraft trajectory modelling and pedestrian trajectory modelling in a surveillance setting. In both cases the proposed approach outperforms the current state-of-the-art. Furthermore, we perform an in depth analysis on the evolution of the memory module content over time and provide visual evidence on how the proposed TMN is able to map both short and long term relationships efficiently via a hierarchical structure.

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Section: The Results Inmentioning

confidence: 97%

Section: Experiments 1: Terminal Area Air Traffic Predictionmentioning

confidence: 99%

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Tree Memory Networks for modelling long-term temporal dependencies

et al. 2018

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“…Mining flights data The problem of airline fare prediction is discussed in detail in [2] and several data mining models were benchmarked in [5]. The authors of [1] modelled 3D trajectories of flights based on various weather and air traffic conditions. The problem of itinerary relevance ranking in one of the largest Global Distributed Systems was presented in [14].…”

Section: Related Workmentioning

confidence: 99%

Learning Cheap and Novel Flight Itineraries

Karamshuk¹,

Matthews²

2019

Machine Learning and Knowledge Discovery in Databases

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We consider the problem of efficiently constructing cheap and novel round trip flight itineraries by combining legs from different airlines. We analyse the factors that contribute towards the price of such itineraries and find that many result from the combination of just 30% of airlines and that the closer the departure of such itineraries is to the user's search date the more likely they are to be cheaper than the tickets from one airline. We use these insights to formulate the problem as a trade-off between the recall of cheap itinerary constructions and the costs associated with building them. We propose a supervised learning solution with location embeddings which achieves an AUC=80.48, a substantial improvement over simpler baselines. We discuss various practical considerations for dealing with the staleness and the stability of the model and present the design of the machine learning pipeline. Finally, we present an analysis of the model's performance in production and its impact on Skyscanner's users.

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“…The paper [6] proposed a collision avoidance algorithm for UA Vs based on the Lyapunov method. The keys to the UAV's early warn ing for the no-fly zone are the prediction of UAV flight trajectory [7][8] and the difficu lty of how to determine whether the UAV will enter the no -fly zone. Trajectory prediction was presented for the movement track of moving target by the linear neural network [9][10] .…”

Section: Related Workmentioning

confidence: 99%

NUCLEI: A New Unmanned Aerial Vehicle Early Warning Method for the No-fly Zone

Wang¹,

Yu²,

Ma³

et al. 2018

Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)

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Abstract-In order to monitor the unmanned aerial vehicle(UAV) around the no-fly zone, we proposed a new unmanned aerial vehicle Early warning method for the no-fly zone (NUCLEI) which gives an alarm to the UAV around the nofly zone in real time. We utilize the least square method to match with the flight trajectory of the UAV in the current period, and then we use the minimum external rectangle (MERT) method to obtain the MERT of the electronic fence. Finally, we calculate whether the tangent line at the current point of the flight path fits curve will intersect with the electronic fence to make the prediction for if the UAV will enter the no-fly zone.

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Aircraft Trajectory Prediction Made Easy with Predictive Analytics

Cited by 115 publications

References 32 publications

Tree Memory Networks for modelling long-term temporal dependencies

Tree Memory Networks for modelling long-term temporal dependencies

Learning Cheap and Novel Flight Itineraries

NUCLEI: A New Unmanned Aerial Vehicle Early Warning Method for the No-fly Zone

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