Relationship Between Weather, Traffic and Delay Based on Empirical Methods

*† This paper describes an empirical method for developing delay estimation models using the expected traffic demand and weather. The computation of weather weighted traffic counts and its sensitivity to the choice of reference traffic days is studied. Earlier, the features of weather weighted traffic counts were used to develop a linear regression model for delay. The linear regression model was evaluated using 65 days of convective weather data during 2004. The accuracy of delay estimation method using a single linear fit is improved by dividing the delay estimation process into regions of low, medium, and high delay. Results are presented on the development of the three-region linear model using 204 days of convective weather data from 2004 and 2005. The model is validated using weather data for August 2005. The model provides a methodology to characterize delay in the NAS. I. Introduction HE steady rise in demand for air transportation has emphasized the need for improved air traffic flow management (TFM) within the National Airspace System (NAS). The NAS refers to hardware, software and people, including runways, radars, networks, FAA, airlines, etc., involved in air traffic management (ATM) in the U.S. One of the metrics that has been used to assess the performance of NAS is the actual aggregate delay provided through FAA’s Air Traffic Operations Network (OPSNET). These OPSNET delays are caused by the application of TFM initiatives in response to weather conditions and excessive traffic volume. TFM initiatives such as ground stops, ground delay programs, rerouting, airborne holding, and miles-in-trail restrictions, are actions that are needed to control the air traffic demand to mitigate the demand-capacity imbalance due to the reduction in capacity. Consequently, TFM initiatives result in NAS delays. Of all the causes, weather has been identified as the most important causal factor for NAS delays. Therefore, to guide flow control decisions during the day of operations, and for post operations analysis, it is useful to create a baseline for NAS performance and establish a model that characterizes the relation between weather and NAS delays. In post operations analysis the model can be used to check if the recorded delay was within the range of delays for similar weather and, if the delay is out of bounds to examine the operations carefully for other causes. Similarly, given the expected weather, the model can be used to predict the expected aggregate delay. T