Robust And Practical Traffic Flow Management Optimization Algorithm For Near-Term Implementation

Current-day traffic management initiatives (TMIs), such as ground delay programs (GDPs) and miles-in-trail (MIT) restrictions, are not necessarily optimal in terms of minimizing overall delays while balancing traffic demand and capacity. Existing approaches address uncertainties in traffic demand and capacity forecasts by adopting a “wait and see” approach, which leads to short advance notice times for TMIs and other inefficiencies. Moreover, overly conservative ground delays are implemented to address uncertainty in predicted congestion. This conservativeness causes excess delay and underutilization of constrained National Airspace System (NAS) resource capacities. Furthermore, the use of overlapping, uncoordinated ground delay initiatives (e.g., GDPs) and airborne delay initiatives (e.g., arrival MITs) causes double penalty delays, thereby further degrading the efficiency of the TMI response. This paper presents an integer-programming based TFM optimization approach that is robust to forecast uncertainties and enhances the coordination between strategic ground and airborne delays. Monte Carlo simulation experiments compare this approach with current-day TMIs. Our results show that this approach significantly reduces the total strategic and tactical delays necessary for addressing the uncertainty in predicted congestion events. Moreover, our TFM controls are easily implementable in the form of optimal GDPs and targeted flight-specific airborne, time-based sector delay advisories.