Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

Abstract

traffic service providers have to make decision s regarding changes to air traffic flow in the event of major weather dist urbances and traffic congestions to maintain safety of the system. The b ehavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consi stency of deciding on control action depends on the weather and traffic c onditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic co nditions) on different days can be used to categorize these into days with litt le decision consistency, days with moderate decision consistency and days with high decision consistency. Five years of traffic, weather and ground delay pro gram decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mi ning methods have the best performance in the region of most decision consiste ncy and have the poorest performance in the region of little decision consis tency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize region specifi c performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of diff erent data mining methods for this particular problem. The fact that differe nt mining methods show no significant variation also provides further confide nce in the results of data mining methods. This paper also discusses how prediction errors impact regions of decision consistency.