Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics
In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/8184513 |
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| _version_ | 1850164667348942848 |
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| author | Wen Tian Huiqing Xu Yixing Guo Bin Hu Yi Yao |
| author_facet | Wen Tian Huiqing Xu Yixing Guo Bin Hu Yi Yao |
| author_sort | Wen Tian |
| collection | DOAJ |
| description | In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management. |
| format | Article |
| id | doaj-art-ea6cc4c73b864a81a91b857154ac7cd2 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-ea6cc4c73b864a81a91b857154ac7cd22025-08-20T02:21:54ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/81845138184513Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution CharacteristicsWen Tian0Huiqing Xu1Yixing Guo2Bin Hu3Yi Yao4National Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaNational Key Laboratory of Air Traffic Flow Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaIn China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management.http://dx.doi.org/10.1155/2018/8184513 |
| spellingShingle | Wen Tian Huiqing Xu Yixing Guo Bin Hu Yi Yao Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics Journal of Advanced Transportation |
| title | Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics |
| title_full | Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics |
| title_fullStr | Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics |
| title_full_unstemmed | Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics |
| title_short | Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics |
| title_sort | probabilistic en route sector traffic demand prediction based upon statistical analysis of error distribution characteristics |
| url | http://dx.doi.org/10.1155/2018/8184513 |
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