Outlier Analysis of Airport Delay Distributions in US and China

Outlier detection is a key component of several machine learning approaches. However, many existing techniques, especially for multi-dimensional signals, are not interpretable and do not explain why a specific classification was assigned to a particular data point. Another limitation is that most methods only consider the magnitude or intensity of the signal, and not its spatial distribution. We present a spectral approach to identify outliers based on the spatial distribution of a signal across the nodes of a graph without any explicit assumptions on the underlying probability distribution of the signal. By applying these techniques to airport delays, we not only identify outliers in the spatial distribution of delays, but also gain insights into the delay dynamics. Specifically, we compare spatial delay distributions in the US and China during the period 2012–17, and identify several interesting characteristics pertaining to critical airports for outlier detection. We characterize typical variabilities in the delay distributions, and the frequency of occurrence of outliers. Our results highlight the differences between the operational dynamics of the US and Chinese air transportation systems, and contribute to performance benchmarking between different airspace systems.