Modeling Traffic Prediction Uncertainty for Traffic Management Decision Support

Wan, Yan; Mulgund, Sandeep; Greenbaum, Daniel; Song, Lixia

doi:10.2514/6.2004-5230

Cited by 32 publications

(19 citation statements)

References 7 publications

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“…For example, airlines use the cancellation of flights as a strategy to avoid high departure delays 15 . As in Wanke et al 16 or Ball et al 4 we distinguish three types of events that disturb a flight planning ( Figure 6):…”

Section: Iib Probabilistic Analysismentioning

confidence: 92%

Analysis of Gaps between the Predictive and the Adaptive Component in ATM

Gwiggner¹,

Dương

Nagaoka³

2008

The 26th Congress of ICAS and 8th AIAA ATIO

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In a data analysis, we discovered systematic gaps between the number of planned and the number of realized entries of aircraft into flight sectors. We also found that random disturbances of an arrival process into flight sectors cause systematic gaps. We conclude that even if all controllable uncertainties in flow planning were eliminated, systematic gaps between the number of planned and realized traffic would remain. The results give insight into the macroscopic relationship between planned-and realized traffic flows and are a step towards a realistic model of air-traffic flow.

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Section: Iib Probabilistic Analysismentioning

confidence: 92%

Analysis of Gaps between the Predictive and the Adaptive Component in ATM

Gwiggner¹,

Dương

Nagaoka³

2008

The 26th Congress of ICAS and 8th AIAA ATIO

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“…By aggregating utilization data from a sufficiently long historic horizon, we collect multiple options and determine the average likely utilization. The historic utilization of each route can be calculated as shown in Equation 8:…”

Section: Definition Of Factors a Historic Utilization Factormentioning

confidence: 99%

“…Predicting demand volume given the route is a common need for TFM decision support tools, and it has been studied extensively in the context of tactical congestion management tools. [6][7][8][9][10] Past work suggests that a reasonable model of aggregate demand for some NAS resources can be constructed for 15 minute prediction intervals by considering predicted filed and scheduled flights for the interval of interest, as well as those for the immediately preceding and subsequent intervals. 9 This work was done for airport arrival and en route sector counts.…”

Section: Introductionmentioning

confidence: 99%

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

Wan

Taylor

Maşek

et al. 2012

AIAA Modeling and Simulation Technologies Conference

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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.

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“…Therefore to extend the utility of these predictions to support strategic TFM planning, it is desirable to quantify the uncertainty in these predictions as is done in Ref. 25.…”

Section: Demand Estimationmentioning

confidence: 99%

Integrated Traffic Flow Management Decision Making

Grabbe

Sridhar

Mukherjee

2009

AIAA Guidance, Navigation, and Control Conference

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A generalized approach is proposed to support integrated traffic flow management decision making studies at both the U.S. national and regional levels. It can consider tradeoffs between alternative optimization and heuristic based models, strategic versus tactical flight controls, and system versus fleet preferences. Preliminary testing was accomplished by implementing thirteen unique traffic flow management models, which included all of the key components of the system and conducting 85, six-hour fast-time simulation experiments. These experiments considered variations in the strategic planning look-ahead times, the replanning intervals, and the types of traffic flow management control strategies. Initial testing indicates that longer strategic planning look-ahead times and re-planning intervals result in steadily decreasing levels of sector congestion for a fixed delay level. This applies when accurate estimates of the air traffic demand, airport capacities and airspace capacities are available. In general, the distribution of the delays amongst the users was found to be most equitable when scheduling flights using a heuristic scheduling algorithm, such as ration-by-distance. On the other hand, equity was the worst when using scheduling algorithms that took into account the num ber of seats aboard each flight. Though the scheduling algorithms were effective at alleviating sector congestion, the tactical rerouting algorithm was the primary control for avoiding en route weather hazards. Finally, the modeled levels of sector congestion, the number of weather incursions, and the total system delays, were found to be in fair agreement with the values that were operationally observed on both good and bad weather days.

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Modeling Traffic Prediction Uncertainty for Traffic Management Decision Support

Cited by 32 publications

References 7 publications

Analysis of Gaps between the Predictive and the Adaptive Component in ATM

Analysis of Gaps between the Predictive and the Adaptive Component in ATM

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

Integrated Traffic Flow Management Decision Making

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