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Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs.
| 题名： | Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs. |
| 作者： | Kulkarni, D.; Sridhar, B.; Wang, Y. |
| 关键词： | Air Traffic Control; Air Transportation; Airline Operations; Airspace; Convection Cells; Data Mining; Flight Hazards; Weather |
| 摘要： | The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. |
| 报告类型： | 科技报告 |
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