Transactions of Nanjing University of Aeronautics & Astronautics
Terminal Traffic Flow Prediction Method Under Convective Weather Using Deep Learning Approaches
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Abstract:
In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather， a multi-input deep learning （MICL） model is proposed. On the basis of previous studies， this paper expands the set of weather characteristics affecting the traffic flow in the terminal area， including weather forecast data and Meteorological Report of Aerodrome Conditions （METAR） data. The terminal airspace is divided into smaller areas based on function and the weather severity index （WSI） characteristics extracted from weather forecast data are established to better quantify the impact of weather. MICL model preserves the advantages of the convolution neural network （CNN） and the long short-term memory （LSTM） model， and adopts two channels to input WSI and METAR information， respectively， which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area. Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather. The results show that the MICL model has excellent performance in mean squared error （MSE）， root MSE （RMSE）， mean absolute error （MAE） and other performance indicators compared with the existing machine learning models or deep learning models， such as K-nearest neighbor （KNN）， support vector regression （SVR）， CNN and LSTM. In the forecast period ranging from 30 min to 6 h， the MICL model has the best prediction accuracy and stability.
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Project Supported:
This work was supported by the Civil Aviation Safety Capacity Building Project.
Clc Number:
V355
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