Fast 4D flight planning under uncertainty through parallel stochastic path simulation

Abstract

The Air Traffic Management system is evolving to deal with efficiency, capacity, safety and environmental challenges. Progress along these fronts requires the development of trajectory planning and prediction tools that can go beyond the current deterministic planning paradigm to deal with an uncertain meteorological and operational context. In this work, we introduce a novel flight planning methodology to generate weather-optimal 4D flight plans under uncertainty. By leveraging general-purpose computing on graphics processing units and combining continuous and discrete elements in an integrated fashion, we can simulate and evaluate multiple trajectory options under multiple scenarios in parallel, allowing us to provide quick iterations to a stochastic optimization algorithm. Our computational experiments show that our proposed solutions can provide efficient solutions in seconds, as required in practical settings, while allowing for simple integration of future extensions thanks to its simulation-based nature.