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Air Traffic Delay Prediction Based o...

Li, Meng.

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Air Traffic Delay Prediction Based on Machine Learning and Delay Propagation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Air Traffic Delay Prediction Based on Machine Learning and Delay Propagation./
Author:	Li, Meng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	61 p.
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Transportation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10748384
ISBN:	9780438011083

Air Traffic Delay Prediction Based on Machine Learning and Delay Propagation.
Li, Meng.

Air Traffic Delay Prediction Based on Machine Learning and Delay Propagation. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 61 p.

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.A.A.)--Purdue University, 2018.

Flight Delay creates significant problems in the current aviation system. Methods are needed to analyze the manner in which delay propagates in the airport networks. Traditional methods are inadequate to the task. This paper presented a new machine learning based air traffic delay prediction model that combined multi-label random forest classification and approximated delay propagation model. To improve the prediction performance, an optimal feature selection process is introduced and demonstrated to have better performance than directly using all the features of available datasets. Departure delay and late arriving aircraft delay are shown to be the most critical features for delay prediction. To utilize these two features, a delay propagation model is proposed as a link to connect them to build a chained delay prediction model. Given the initial departure delay, the chained model is demonstrated to have the ability to predict the flight delay along the same aircrafts itinerary. By updating the actual departure delay with the iteration number along with the itinerary, the model's accuracy can be further improved. Our application results demonstrate the value of machine learning and delay propagation for analyzing and predicting the air traffic delay in daily operation.

ISBN: 9780438011083Subjects--Topical Terms:

555912
Transportation.

Air Traffic Delay Prediction Based on Machine Learning and Delay Propagation.
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245 1 0 $a Air Traffic Delay Prediction Based on Machine Learning and Delay Propagation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 61 p.
500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Dengfeng Sun.
502 $a Thesis (M.S.A.A.)--Purdue University, 2018.
520 $a Flight Delay creates significant problems in the current aviation system. Methods are needed to analyze the manner in which delay propagates in the airport networks. Traditional methods are inadequate to the task. This paper presented a new machine learning based air traffic delay prediction model that combined multi-label random forest classification and approximated delay propagation model. To improve the prediction performance, an optimal feature selection process is introduced and demonstrated to have better performance than directly using all the features of available datasets. Departure delay and late arriving aircraft delay are shown to be the most critical features for delay prediction. To utilize these two features, a delay propagation model is proposed as a link to connect them to build a chained delay prediction model. Given the initial departure delay, the chained model is demonstrated to have the ability to predict the flight delay along the same aircrafts itinerary. By updating the actual departure delay with the iteration number along with the itinerary, the model's accuracy can be further improved. Our application results demonstrate the value of machine learning and delay propagation for analyzing and predicting the air traffic delay in daily operation.
590 $a School code: 0183.
650 4 $a Transportation. $3 555912
690 $a 0709
710 2 $a Purdue University. $b Aeronautics and Astronautics. $3 1035670
773 0 $t Masters Abstracts International $g 57-06(E).
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792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10748384