Modeling Multi-Timescale Dynamics for Airport Surface Congestion and Recovery

Understanding surface congestion is essential for improving taxiing efficiency and reducing carbon emissions in busy airports. Diverging from the traditional input-output and dynamics analysis, we propose a novel end-to-end framework to study the complete dynamic process of airport surface congestion and recovery. This framework employs a stochastic hybrid system to model multi-timescale dynamics, integrating continuous states and discrete modes of surface operations under uncertainty. First, the probabilistic reachable set for the input-output state is computed via a chance-constrained optimization program to represent the relationship between the number of aircraft taxiing out and the departure throughput. Next, the discrete modes are divided based on taxiing efficiency and traffic load, utilizing tailored congestion contour regression and density-based clustering, respectively. Finally, the transition trajectory incorporating mode information is constructed to depict the complete process from congestion formation to subsequent recovery, followed by employing an unsupervised algorithm to identify representative patterns. The proposed framework is verified using two years of real-world datasets from Chengdu Shuangliu International Airport, China. Experimental results demonstrate the superiority of our approach compared with the baselines. Moreover, this work also reveals some intriguing findings, such as the diverse multi-timescale dynamical phenomena, and their implications for practical airport surface operations.