The problem of scheduling and collaborative decision-making in airport metroplex or terminal areas can be significantly approached by obtaining accurate ETA (estimated time of arrival). Traditional methods are short of the ability to fine-tune the arrival metering nodes. The accurate quantitative estimation of large-volume and complex flight traffic situations is hard to achieved especially under the influence of highly dynamic environments in a medium to long term. An ETA correction method based on error feedback was proposed. Based on the aircraft performance parameters, an aircraft kinematics model was firstly constructed combined with route planning and meteorological data, which was used to give a preliminary ETA prediction through the calculation of 4D trajectory then. After that an error sequence would be constructed by comparing the difference between ATA (actual time of arrival) and the predicted results, with it the next error could be predicted using the error feedback model and the results obtained previously would be corrected. Finally, the arrival flights to a large hub airport were taken as examples to conduct a simulation, in which the rate of error within ±5 minutes that predicted 30 minutes in advance was chosen as the evaluation criteria. The simulation results show that the accuracy of ETA prediction can be improved by more than 25% in bad weather after corrected by the proposed method when compared with traditional means.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |