Air Transportation Direct Share Analysis and Forecast
Air transportation direct share is the ratio of direct passengers to total passengers on a directional origin and destination (O&D) pair. Direct share is an essential factor of passenger flow distribution and shows passengers’ general preference for direct flight services on a certain O&D. A...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8924095 |
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| _version_ | 1832553297949491200 |
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| author | Xufang Zheng Chia-Mei Liu Peng Wei |
| author_facet | Xufang Zheng Chia-Mei Liu Peng Wei |
| author_sort | Xufang Zheng |
| collection | DOAJ |
| description | Air transportation direct share is the ratio of direct passengers to total passengers on a directional origin and destination (O&D) pair. Direct share is an essential factor of passenger flow distribution and shows passengers’ general preference for direct flight services on a certain O&D. A better understanding and a more accurate forecast of direct share can benefit air transportation planners, airlines, and airports in multiple ways. In most of the previous research and applications, it is commonly assumed that direct share is a fixed ratio, which contradicts the air transportation practice. In the Federal Aviation Administration (FAA) Terminal Area Forecast (TAF), the O&D direct share is forecasted as a constant based on the latest observation of direct share on the O&D. To find factors which have significant impacts on O&D direct share and to build an accurate model for O&D direct share forecasting, both parametric and nonparametric machine learning models are investigated in this research. We propose a novel category-based learning method which can provide better forecasting performance compared to employing the single modeling method for O&D direct share forecasting. Based on the comparison, the developed category-based learning model is a promising replacement for the model used for O&D direct share forecasting by the FAA TAF. |
| format | Article |
| id | doaj-art-dfe8b356d0794d9a8839caccd4dce9c2 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-dfe8b356d0794d9a8839caccd4dce9c22025-02-03T05:54:27ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/89240958924095Air Transportation Direct Share Analysis and ForecastXufang Zheng0Chia-Mei Liu1Peng Wei2Department of Aerospace Engineering, Iowa State University, Ames, Iowa 50010, USAOffice of Aviation Policy and Plans, Federal Aviation Administration, Washington, DC 20591, USADepartment of Aerospace Engineering, Iowa State University, Ames, Iowa 50010, USAAir transportation direct share is the ratio of direct passengers to total passengers on a directional origin and destination (O&D) pair. Direct share is an essential factor of passenger flow distribution and shows passengers’ general preference for direct flight services on a certain O&D. A better understanding and a more accurate forecast of direct share can benefit air transportation planners, airlines, and airports in multiple ways. In most of the previous research and applications, it is commonly assumed that direct share is a fixed ratio, which contradicts the air transportation practice. In the Federal Aviation Administration (FAA) Terminal Area Forecast (TAF), the O&D direct share is forecasted as a constant based on the latest observation of direct share on the O&D. To find factors which have significant impacts on O&D direct share and to build an accurate model for O&D direct share forecasting, both parametric and nonparametric machine learning models are investigated in this research. We propose a novel category-based learning method which can provide better forecasting performance compared to employing the single modeling method for O&D direct share forecasting. Based on the comparison, the developed category-based learning model is a promising replacement for the model used for O&D direct share forecasting by the FAA TAF.http://dx.doi.org/10.1155/2020/8924095 |
| spellingShingle | Xufang Zheng Chia-Mei Liu Peng Wei Air Transportation Direct Share Analysis and Forecast Journal of Advanced Transportation |
| title | Air Transportation Direct Share Analysis and Forecast |
| title_full | Air Transportation Direct Share Analysis and Forecast |
| title_fullStr | Air Transportation Direct Share Analysis and Forecast |
| title_full_unstemmed | Air Transportation Direct Share Analysis and Forecast |
| title_short | Air Transportation Direct Share Analysis and Forecast |
| title_sort | air transportation direct share analysis and forecast |
| url | http://dx.doi.org/10.1155/2020/8924095 |
| work_keys_str_mv | AT xufangzheng airtransportationdirectshareanalysisandforecast AT chiameiliu airtransportationdirectshareanalysisandforecast AT pengwei airtransportationdirectshareanalysisandforecast |