| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000433203 |
| 005 | | 20200225113719 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085617123 |
| 035 | |
▼a (MiAaPQ)AAI13857007 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 385 |
| 100 | 1 |
▼a Ayhan, Samet. |
| 245 | 10 |
▼a Airspace Planning for Optimal Capacity, Efficiency, and Safety Using Analytics. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, College Park.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 222 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-03, Section: A. |
| 500 | |
▼a Advisor: Samet, Hanan. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method for planning optimal airspace capacity, efficiency, and safety. These goals require separation and sequencing of aircraft before they depart. Prior approaches have tactically achieved these goals to some extent. However, dealing with increasingly congested airspace and new environmental factors with high levels of uncertainty still remains the challenge when deterministic approach is used. Hence due to the nature of uncertainties, we take a stochastic approach and propose a suite of analytics models for (1) Flight Time Prediction, (2) Aircraft Trajectory Clustering, (3) Aircraft Trajectory Prediction, and (4) Aircraft Conflict Detection and Resolution long before aircraft depart. The suite of data-driven models runs on a scalable Data Management System that continuously processes streaming massive flight data to achieve the strategic airspace planning for optimal capacity, efficiency, and safety.(1) Flight Time Prediction. Unlike other systems that collect and use features only for the arrival airport to build a data-driven model for predicting flight times, we use a richer set of features along the potential route, such as weather parameters and air traffic data in addition to those that are particular to the arrival airport. Our feature engineering process generates an extensive set of multidimensional time series data which goes through Time Series Clustering with Dynamic Time Warping (DTW) to generate a single set of representative features at each time instance. The features are fed into various regression and deep learning models and the best performing models with most accurate ETA predictions are selected. Evaluations on extensive set of real trajectory, weather, and airport data in Europe verify our prediction system generates more accurate ETAs with far less variance than those of European ANSP, EUROCONTROL's. This translates to more accurately predicted flight arrival times, enabling airlines to make more cost-effective ground resource allocation and ANSPs to make more efficient flight scheduling.(2) Aircraft Trajectory Clustering. The novel divide-cluster-merge |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Transportation. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0709 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-03A. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490830
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |