Trajectory Data Driven Transit-Transportation Planning

Abstract. In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory (RNN-LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC-ESN substantially outperforms ANN and RNN-LSTM for short-term prediction, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps, equivalent to several Lyapunov timescales. The RNN-LSTM and ANN show some prediction skills as well; RNN-LSTM bests ANN. Furthermore, even after losing the trajectory, data predicted by RC-ESN and RNN-LSTM have probability density functions (PDFs) that closely match the true PDF, even at the tails. The PDF of the data predicted using ANN, however, deviates from the true PDF. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems such as weather/climate are discussed.

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Data-driven approach for anomaly detection of real GPS trajectory data

2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT) ◽

10.1109/icat47117.2019.8938938 ◽

2019 ◽

Author(s):

Emir Barucija ◽

Amra Mujcinovic ◽

Berina Muhovic ◽

Emir Zunic ◽

Dzenana Donko

Keyword(s):

Anomaly Detection ◽

Data Driven ◽

Trajectory Data ◽

Gps Trajectory Data ◽

Data Driven Approach ◽

Gps Trajectory

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Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches

Proceedings ◽

10.3390/proceedings2020059008 ◽

2020 ◽

Vol 59 (1) ◽

pp. 8

Author(s):

Xavier Olive ◽

Junzi Sun ◽

Adrien Lafage ◽

Luis Basora

Keyword(s):

Open Data ◽

Data Driven ◽

Automatic Identification ◽

Trajectory Data ◽

Rule Based ◽

Wide Range ◽

Traffic Performance ◽

Aircraft Trajectory ◽

Aircraft Trajectories ◽

Selection Of

The large amount of aircraft trajectory data publicly available through open data sources like the OpenSky Network presents a wide range of possibilities for monitoring and post-operational analysis of air traffic performance. This contribution addresses the automatic identification of operational events associated with trajectories. This is a challenging task that can be tackled with both empirical, rule-based methods and statistical, data-driven approaches. In this paper, we first propose a taxonomy of significant events, including usual operations such as take-off, Instrument Landing System (ILS) landing and holding, as well as less usual operations like firefighting, in-flight refuelling and navigational calibration. Then, we introduce different rule-based and statistical methods for detecting a selection of these events. The goal is to compare candidate methods and to determine which of the approaches performs better in each situation.

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