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Prediction of Aircraft Estimated Time of Arrival Using A Supervised Learning Approach.
| 题名： | Prediction of Aircraft Estimated Time of Arrival Using A Supervised Learning Approach. |
| 作者： | Wells, J. Z; Puranik, T. G; Kalyanam, K. M; Kumar, M. |
| 摘要： | We present a novel data-driven approach for prediction of the estimated time of arrival (ETA) of aircraft in the terminal area via the implementation of a Random Forest regression model. The model uses data fused from a number of sources (flight track, weather, flight plan information, etc.) and provides predictions for the remaining flight time for aircraft landing at Dallas/Fort Worth (DFW) International Airport. The predictions are made when the aircraft is at a distance of 200-miles from the airport. The results show that the model is able to predict estimated time of arrival to within ± 5 min for 90% of the flights in the test data with the mean absolute error being lower at 145 seconds. This paper covers the entire pipeline of data collection, pre- processing, setup and training of the ML model, and the results obtained for DFW. |
| 总页数： | 6 pages |
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