Application of Non-Negative Tensor Factorization for Airport Flight Delay Pattern Recognition

With the rapid development of civil aviation transportation in China, huge demand growth has broken the balance between supply and demand, resulting in airspace congestion and increasing flight delays. The delays of large airports have been increasing year by year, which has seriously affected the air travel experience of passengers. Obtaining their flight delay patterns can help identify defects in flight scheduling and airspace utilization. The investigation based on the actual flight operation data of Tianjin Binhai International Airport (TSN) is conducted, in order to capture the relationship and impact between the factors such as traffic flow direction, airline attributes and hourly average delay distribution. Furthermore, Non-negative Tensor Factorization (NTF) is applied to pattern recognition by introducing CP (CANDECOMP/PARAFAC) decomposition and Block Coordinate Descent (BCD) algorithm for selected data set. Numerical experiments show that the designed method has good performance in terms of computation speed and solution quality. Recognition results indicate the significant pattern characteristics of the Tianjin airport delay are extracted, which can provide some new perspectives for air traffic management unit to alleviate airspace congestion and improve service quality.