Trajectory Clustering and an Application to Airspace Monitoring

Gariel, Maxime; Srivastava, Ashok N.; Féron, Éric

doi:10.1109/tits.2011.2160628

Cited by 229 publications

(124 citation statements)

References 18 publications

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“…Secondly, trajectory data are cut-off at an altitude of 800 metres. Finally, these trajectories are clustered according to their nominal flight paths via the method presented by Gariel, Srivastava and Feron [3], where the epsilon parameter used in the clustering is 0.3, with a minimum number of trajectories per cluster of 10. This method identified 10 distinct nominal flight paths, placing 909 trajectories in 10 clusters, where 339 trajectories are considered outliers.…”

Section: Resultsmentioning

confidence: 99%

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A Gaussian process based decision support tool for air traffic management

Eerland

Box

Fangohr

et al. 2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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Technological developments in the last decade have shifted challenges in traffic flow management from obtaining and storing data, to analysing and presenting the enormous amount of available trajectory data in a comprehensible manner. This paper introduces a novel approach to visualising air-traffic, shifting the focus from displaying traffic density, towards directly visualising the flight corridors used by air-traffic. Such an approach is suitable for visualising air-traffic in three dimensions, which is particularly helpful in the vicinity of an airport where the air-traffic often changes level. Furthermore, the approach is data-driven, allowing the comparison of multiple trajectory datasets in order to identify changes in traffic corridors related to changing air-traffic and weather conditions. Finally, by using the probabilistic nature of the approach, it is possible to quantify the air-traffic complexity in terms of the traffic structure. The results presented in this paper show the approach applied to a trajectory dataset as measured by ground-radar near Denver airport (DEN

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Section: Resultsmentioning

confidence: 99%

“…Previous work focuses on clustering the trajectory data based on similar weather conditions [2] and similar flight paths [3], which has been proven effective at clustering and classifying air-traffic flows [4]. However, in order to provide decision support to TFM, the clustered trajectory data needs to be presented in a comprehensible manner.…”

mentioning

confidence: 99%

A Gaussian process based decision support tool for air traffic management

Eerland

Box

Fangohr

et al. 2017

17th AIAA Aviation Technology, Integration, and Operations Conference

View full text Add to dashboard Cite

show abstract

“…Gariel et al [10] present a tool called AirTrajectoryMiner aimed at monitoring the health of the airspace. A healthy airspace is defined as the condition in which all airplanes fly according to the plan.…”

Section: Related Workmentioning

confidence: 99%

Detecting flight trajectory anomalies and predicting diversions in freight transportation

Ciccio

Cabanillas

et al. 2016

Decision Support Systems

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Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation.When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

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“…In most cases, these trajectories have patterns in them, as the tracked objects perform repetitive motions and/or follow specific paths. Extracting these patterns from aircraft trajectories specifically may be important for air traffic controllers wishing to monitor the highly regulated airspace around airports [1], or environmental policy makers wishing to investigate aircraft noise abatement [2], researchers who predict high-altitude gas balloon trajectories [3], researchers who fly unguided atmospheric sounding rockets and wish to predict safe regions of splashdown [4] and for a range of other applications.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Dispersion of Aircraft Trajectories Using Gaussian Processes

Eerland

Box

Sóbester

2016

Journal of Guidance, Control, and Dynamics

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This work investigates the application of Gaussian processes to capturing the probability distribution of a set of aircraft trajectories from historical measurement data. To achieve this, all data are assumed to be generated from a probabilistic model that takes the shape of a Gaussian process. The approach to Gaussian process modelling used here is based on a linear expansion of trajectory data into set of basis functions that may be parametrized by a multivariate Gaussian distribution.The parameters are learned through maximum likelihood estimation. The resulting probabilistic model can be used for both modelling the dispersion of trajectories along the common flightpath and for generating new samples that are similar to the historical data. The performance of this approach is evaluated using three trajectory datasets; toy trajectories generated from a Gaussian distribution, sounding rocket trajectories that are generated by a stochastic rocket flight simulator and aircraft trajectories on a given departure path from Dallas Fort Worth airport, as measured by ground-based radar. The results indicate that the maximum deviation between the probabilistic model and test data obtained for the three data sets are respectively 4.9%, 7.6% and 13.1%.

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Trajectory Clustering and an Application to Airspace Monitoring

Cited by 229 publications

References 18 publications

A Gaussian process based decision support tool for air traffic management

A Gaussian process based decision support tool for air traffic management

Detecting flight trajectory anomalies and predicting diversions in freight transportation

Modeling the Dispersion of Aircraft Trajectories Using Gaussian Processes

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