Learning Traffic Patterns at Small Airports From Flight Tracks

Mahboubi, Zouhair; Kochenderfer, Mykel J.

doi:10.1109/tits.2016.2598064

Cited by 29 publications

(21 citation statements)

References 23 publications

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“…Gariel et al [25] identify and cluster these so-called turning points across a dataset of trajectories, then treat a trajectory as a (discrete) sequence of the cluster labels, then cluster those (discrete) sequences using the least common subsequence algorithm. Mahboubi and Kochenderfer [26] find that the turning point model does not perform well on real, noisy radar data, and extend the work of Gariel et al by representing the transitions between turning points at small airports using a Gaussian hidden semi-Markov model.…”

supporting

confidence: 55%

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data

Barratt

Kochenderfer

Boyd

2019

IEEE Trans. Intell. Transport. Syst.

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Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion in terminal airspace, the controlled airspace surrounding a given airport. The method fits the model based on a historical dataset of radar-based position measurements of aircraft landings and takeoffs at that airport. We find that the model generates realistic trajectories, provides accurate predictions, and captures the statistical properties of aircraft trajectories. Furthermore, the model trains quickly, is compact, and allows for efficient real-time inference.

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supporting

confidence: 55%

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data

Barratt

Kochenderfer

Boyd

2019

IEEE Trans. Intell. Transport. Syst.

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“…One main challenge with non-parametric approaches is that one must derive all the necessary expressions to properly perform inference [19]. Here, to make our algorithm tractable, we assume that observations are drawn from a Gaussian distribution like in [17]. The θ i is set as θ i = [µ pi , Σ pi ].…”

Section: Emission Modelmentioning

confidence: 99%

Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications

Wang

Zhao

2018

IEEE Robot. Autom. Lett.

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Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important paradigm that allows automated vehicles to both learn from human drivers and gain insights is understanding the principal compositions of the entire traffic, termed as traffic primitives. However, the exploding data growth presents a great challenge in extracting primitives from highdimensional time-series traffic data with various types of road users engaged. Therefore, automatically extracting primitives is becoming one of the cost-efficient ways to help autonomous vehicles understand and predict the complex traffic scenarios. In addition, the extracted primitives from raw data should 1) be appropriate for automated driving applications and also 2) be easily used to generate new traffic scenarios. However, existing literature does not provide a method to automatically learn these primitives from large-scale traffic data. The contribution of this paper has two manifolds. The first one is that we proposed a new framework to generate new traffic scenarios from a handful of limited traffic data. The second one is that we introduce a nonparametric Bayesian learning method -a sticky hierarchical Dirichlet process hidden Markov model -to automatically extract primitives from multidimensional traffic data without prior knowledge of the primitive settings. The developed method is then validated using one day of naturalistic driving data. Experiment results show that the nonparametric Bayesian learning method is able to extract primitives from traffic scenarios where both the binary and continuous events coexist.

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“…The rigorous analysis of traffic control systems requires an accurate model of aircraft behaviour [6,36]. The feasibility of using a Markov decision process for analysis of an air-traffic alert system has been investigated in [26]. The different approaches for learning of traffic models from recorded flight data are evaluated.…”

Section: Probabilistic Modelling Of Air Trafficmentioning

confidence: 99%

“…Factors such as wind, weather conditions, aircraft flight characteristics, unavoidable imprecision in operations and manoeuvres, as well as impression of radar readings increase uncertainty in air traffic data, see e.g. [24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Bayesian learning of models for estimating uncertainty in alert systems: Application to air traffic conflict avoidance

Schetinin

Jakaite

Krzanowski

2018

ICA

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Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems.

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Learning Traffic Patterns at Small Airports From Flight Tracks

Cited by 29 publications

References 23 publications

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace From Position Data

Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications

Bayesian learning of models for estimating uncertainty in alert systems: Application to air traffic conflict avoidance

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