Dynamic Queuing Network Model for Flow Contingency Management

Wan, Yan; Taylor, Christine; Roy, Sandip; Wan, Yan; Zhou, Yi

doi:10.1109/tits.2013.2260745

Cited by 45 publications

(36 citation statements)

References 29 publications

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“…To evaluate the performance of our algorithm at a larger scale, we examine a dataset consisting of approximately 90 days from May to July 2014, with each day characterized by 21 SREF ensemble members based on its 9:00Z forecast. Dynamic sector weather-impact intensity profiles measured in the form of sector delay for all 972 sectors in the NAS were generated for each scenario by supplying the corresponding precipitation intensity and traffic demand forecasts to the queuing network simulator [4,17], which estimates delays by modeling the propagation of traffic demand through the air traffic system under weather uncertainties. …”

Section: Clustering Results and Comparative Performance Studiesmentioning

confidence: 99%

Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Xie¹,

Wan²,

Zhou³

et al. 2015

Journal of Aerospace Information Systems

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Convective weather, recognized as the leading contributor to delays in the National Airspace System, causes demand-capacity imbalance in the airspace. Strategic air traffic management aims to resolve the imbalance for offnominal conditions (for example, convective weather events) through redistributing flows and resources at the strategic timeframe (2-15 h in advance). As a component of scenario-driven strategic air traffic management, this paper develops a multiresolution spatiotemporal distance measure that, combined with standard distance-based clustering algorithms, can be used to group a wide range of possible weather-impact scenarios into a few representative clusters to facilitate corresponding management strategy design. Motivations of this new distance measure, its generation algorithm, and parameter impact analysis are described in detail to facilitate practical implementation and subsequent use in decision support. This multiresolution spatiotemporal distance measure not only addresses the needs of scenario-driven strategic air traffic management but also generally applies to broad data-driven decision support applications that involve large-scale physical processes of spatiotemporal spread dynamics, as the measure captures the similarity between scenarios of spatiotemporal spread patterns of varying shape, size, location, and intensity; corrects the "boundary effects"; and is flexible to a variety of data features in temporal and spatial dimensions. Performance evaluation is conducted through comparative studies and sensitivity analysis, using real weather-impact datasets. Nomenclature b i;j = number of hops between spatial cells g i and g j D i;j = pairwise scenario distance between scenarios s i and s j D i;j = pairwise scenario distance between scenarios s i and s j after normalization d i;j;w;h = distance between scenarios s i and s j for spatial resolution w and temporal resolution h (subscript of w or h is eliminated for convenience if data do not have spatial or temporal dimension) F = similarity of two scenarios f w = similarity of two scenarios at spatial resolution w G = set of all spatial cells g k = two-dimensional (regularly or irregularly shaped) spatial cell of index k h = temporal resolution h max = upper bound of temporal resolution I i;k;l = intensity at spatial cell g k and time point t l for scenario s i (subscript of k or l is eliminated for convenience if data do not have spatial or temporal dimension) I i;k;l = intensity at spatial cell g k and time point t l for scenario s i after weighting with β k;l I i;k;l;w;h = intensity at spatial cell g k and time point t l for scenario s i after correcting boundary effects at spatial resolution w and temporal resolution h (subscripts of k, w or l, h are eliminated for convenience if data do not have spatial or temporal dimension) M = adjacency matrix of spatial cells M i;j = (i, j) entry of adjacency matrix M S = set of all scenarios s i = scenario of index i T = set of all time points t l = time point of index l w = spatial resolution w...

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Section: Clustering Results and Comparative Performance Studiesmentioning

confidence: 99%

Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Xie¹,

Wan²,

Zhou³

et al. 2015

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, in [2,8,17] the authors explored Eulerian network models for air traffic flows. These flow-level models for air traffic were later enhanced to represent traffic at varied resolutions, to explicitly capture multiple origin-destination pairs, and to model management initiatives as queueing elements [27,31]. Finally, agent-based modeling is explored in [32] where each flight and control agent is defined as an agent and used to analyze different system properties such as throughput, capacity, delay, delay jitter, and congestion.…”

Section: Related Workmentioning

confidence: 99%

“…This work aims to bridge the gap, by modeling the impact of cyber attacks on air traffic flows, and analyzing attack impacts on regional air traffic management. To perform this analysis, we present a model for air traffic flows management based on the dynamic queuing network introduced in [30]. We then model various attacks from previous literature within this network and calculate the impacts of these various attacks on air traffic flows.…”

Section: Introductionmentioning

confidence: 99%

Cyber Threat Impact Analysis to Air Traffic Flows Through Dynamic Queue Networks

Tamimi

Hahn

Roy

2020

ACM Trans. Cyber-Phys. Syst.

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Air traffic control increasingly depends on information and communication technology (ICT) to manage traffic flow through highly congested and increasingly interdependent airspace regions. While these systems are critical to ensuring the efficiency and safety of our airspace, they are also increasingly vulnerable to cyber threats that could potentially lead to reduction in capacity and/or reorganization of traffic flows. In this paper, we model various cyber threats to air traffic control systems, and analyze how these attacks could impact the flow of aircraft through the airspace. To perform this analysis, we consider a model for wide-area air traffic based on a dynamic queuing network model. Then we introduce three different attacks (Route Denial of Service, Route Selection Tampering, and Sector Denial of Service) to the air traffic control system, and explore how these attacks manipulate the sector flows by evaluating the queue backlogs for each sector's outflows. Furthermore, we then explore graph-level vulnerability metrics to identify the sectors that are most vulnerable to various flow manipulations, and compare them to case-study simulations of the various attacks. The results suggest that Route Denial of Service attacks have a significant impact on the target sector and lead to the largest degradation to the overall air traffic flows. Furthermore, the impact of Sector Denial of Service attack impacts are primarily confined to the target sector, while the Route Selection Tampering impacts are mostly confined to certain aircraft.

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“…In this section, we first review a stochastic modeling framework that captures the dynamics of NAS under weather uncertainties 1 . This model serves as the evaluation foundation for ATFM.…”

Section: Preliminariesmentioning

confidence: 99%

Scalable Multidimensional Uncertainty Evaluation Approach to Strategic Air Traffic Flow Management

Xie

Wan

2015

AIAA Modeling and Simulation Technologies Conference

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Convective weather events cause capacity reduction in the National Airspace System (NAS), and lead to traffic congestion. To mitigate congestion, strategic air traffic flow management plans traffic flows at a long look-ahead time (2-15 hour). Planning at this timeframe is challenging, due to the wide possibility of weather events and requirement for real-time management. To conquer these challenges, we need an approach to quickly assess the impact of predicted weather events on the performance of air traffic system. In this paper, we use a scalable multidimensional uncertainty evaluation approach, called M-PCM-OFFD, to address this problem. Simulation studies show the effectiveness of this approach for the performance evaluation of air traffic system. In addition, we investigate further capability of M-PCM-OFFD through exploring higher-level OFFDs. Finally, we introduce an uncertainty-exploiting framework to enable real-time strategic air traffic management under weather uncertainty.

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Dynamic Queuing Network Model for Flow Contingency Management

Cited by 45 publications

References 29 publications

Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Distance Measure to Cluster Spatiotemporal Scenarios for Strategic Air Traffic Management

Cyber Threat Impact Analysis to Air Traffic Flows Through Dynamic Queue Networks

Scalable Multidimensional Uncertainty Evaluation Approach to Strategic Air Traffic Flow Management

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