Modeling Air Traffic Demand for a Real-Time Queuing Network Model of the National Airspace System

Abstract

A predictive model for departure traffic demand and its route distribution at look-ahead times of 2-15 hours is proposed, for use in a queuing-network-based tool for strategic Traffic Flow Management (TFM). The proposed model uses a combination of operational data (filed flight plans, schedules), historical statistics of demand, and timeof-operation-specific factors to generate statistical predictions of traffic demand for particular routes between pairs of airports or airport clusters. Specifically, a two-stage predictor for demand is proposed. First, traffic demand for an origin-destination (O-D) pair is modeled as the summation of a known demand which captures filed and scheduled traffic, and an unknown demand which is modeled as non-homogeneous Poisson process. Second, the fraction of this O-D traffic demand on each route is modeled using a linear regression, with the historical route fractions, known (filed) route fractions, and wind-adjusted transit times for the routes serving as regressors. Historical data on demands and actual traffic volumes are used to evaluate aspects of the model, including the Poisson-process assumption and the regression model for route distributions.

Proactive and Reactive Management of Non-Weather Capacity Disruption Events in the National Airspace System: A Flow Modeling and Design Approach

“…The traffic flows are assimilations of traffic among multiple overlaid origin-and desination-(OD) networks, each of which uses a probabilistic description of traffic routing from an origin airport (or airport group) to a destination airport (or group). The demand for each OD network is modeled as having a deterministic piece which captures filed and nominal scheduled traffic, and an added stochastic piece which captures deviations from schedule as well as "pop-ups" (unmodeled freight and general-aviation traffic) [16]. Capacitations of airspace regions or airports, whether at nominal levels or at reduced levels due to weather, are represented as a queueing actions on flows entering the regions (or on arrivals and departures at the airport).…”

“…Capacitations of airspace regions or airports, whether at nominal levels or at reduced levels due to weather, are represented as a queueing actions on flows entering the regions (or on arrivals and departures at the airport). The changes in airport and airspace capacities due to severe weather are extracted from ensemble forecast products, using rubrics for capacity reduction due to weather [16]. Specifically, forecast futures for these capacity reductions are developed from the ensemble members, and are then used to drive the flow-network model.…”

Air Route Clustering for a Queuing Network Model of the National Airspace System

“…In a recent paper describing the modeling of air traffic demand as input to the FCM network queuing model, 3 it was remarked that routes between two airports (or airport clusters), when represented as a sequence of sector transits, often exhibit notable similarity. For example, the following are two historical routes between Atlanta Hartsfield and Chicago O'Hare airports: Route 1: ZTL38 ZTL37 ZID94 ZID93 ZID76 ZID78 ZID89 ZAU34 ZAU32 Route 2: ZTL38 ZTL37 ZID94 ZID92 ZID76 ZID89 ZAU34 ZAU32…”

“…As a quantitative basis of selecting a similarity threshold, results of a predictive regression model was used -as mentioned, one of the steps of FCM modeling is to predict, via regression analysis, the fraction of total flow per route, between airport pairs. (An operations model is used to generate demand counts 3 , so that flow fractions can be converted to an integer number of flights per route.) A sample problem of flights to-and-from seven major airports in the NAS was selected for study.…”

Cyber- Threat Assessment for the Air Traffic Management System: A Network Controls Approach

“…Queues also are used to model intrinsic capacity restrictions on airspace resources (e.g., Sector capacities, arrival and departure rate constraints). Demand is modeled as having a deterministic component which represents scheduled traffic, and a stochastic component which reflects schedule uncertainty and pop-up traffic [21]. Resource capacities are modulated by forecasted weather dynamics, see discussion on the weather layer below.…”