Scipediacontent: Scipediacontent moved page Draft Content 148151905 to Zhang et al 2018d

2021-01-28T18:15:55Z

Scipediacontent moved page Draft Content 148151905 to Zhang et al 2018d

Scipediacontent: Created page with " == Abstract == Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide inst..."

2021-01-28T18:15:51Z

Created page with " == Abstract == Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide inst..."

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== Abstract ==

Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making.

== Original document ==

The different versions of the original document can be found in:

* [https://opus.lib.uts.edu.au/bitstream/10453/126180/1/PID5331313.pdf https://opus.lib.uts.edu.au/bitstream/10453/126180/1/PID5331313.pdf]

* [http://xplorestaging.ieee.org/ielx7/8465565/8488986/08489734.pdf?arnumber=8489734 http://xplorestaging.ieee.org/ielx7/8465565/8488986/08489734.pdf?arnumber=8489734],
: [http://dx.doi.org/10.1109/ijcnn.2018.8489734 http://dx.doi.org/10.1109/ijcnn.2018.8489734]

* [https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2018.html#ShiXPYZ18 https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2018.html#ShiXPYZ18],
: [https://opus.lib.uts.edu.au/bitstream/10453/126180/1/PID5331313.pdf https://opus.lib.uts.edu.au/bitstream/10453/126180/1/PID5331313.pdf],
: [https://opus.lib.uts.edu.au/handle/10453/126180 https://opus.lib.uts.edu.au/handle/10453/126180],
: [https://academic.microsoft.com/#/detail/2897997731 https://academic.microsoft.com/#/detail/2897997731]

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Zhang et al 2018d - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 148151905 to Zhang et al 2018d

Scipediacontent: Created page with " == Abstract == Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide inst..."