Predicting airport arrival rate using ADS-B data and weather radar data via machine learning techniques

Airport Arrival Rate (AAR) is a parameter specifying the number of arrival aircrafts that an airport can accept within an hour and it is commonly used for air traffic flow control. The Hong Kong International Airport (HKIA) is one of the busiest airports in the world for passenger and the world’s busiest for cargo in 2024.  Situated in a sub-tropical climate, HKIA often experiences significant convective weather and squall lines associated with cold fronts, low-pressure troughs and tropical cyclones during spring and summer. These intense convective activities sometimes disrupt the air traffic flow and cause significant travel delays in HKIA. An assessment of the impact of convective activities (related to its intensity and coverage) to AAR would facilitate early planning and necessary air traffic flow control. This study attempts to predict the Airport Arrival Rate via machine learning techniques using aircraft data, airport operational data and meteorological data. The aircraft data specifies the aircraft positions extracted from ADS-B data which can be used to evaluate the degree of congestion in the air space. The airport operational data shows the scheduled AAR for the day, which varies depending on the seasons and holidays. For meteorological data, the weather radar data indicates the location and intensity of convection. Besides, radar nowcast products for the next two hours can provide information on the short-term evolution of the significant convection. This paper will present the formulation of a convolutional neural network (CNN) model to predict the AAR. Due to imbalanced data and the changes in air traffic throughout the years, techniques of coupled CNN model and incremental learning are employed and evaluated. The results and limitations for these techniques will be discussed in the paper.  It is worth noting that there are many other factors affecting AAR, not just the weather, but this study may serve as an estimation of the weather’s impact on air traffic.  Following validation and evaluation, the Hong Kong Observatory may utilize the model to work with the air traffic flow management team in Hong Kong to improve air traffic flow planning.