Analyzing the operational capacity effects of the monitor Alert paramater (MAP)

Hanson, Kevin; Gulding, John; Afshar, Abbas Mohasel

doi:10.1109/icnsurv.2016.7486360

Cited by 4 publications

(4 citation statements)

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“…throughput) that describes the number of aircraft that could traverse a sector in a given period of time was considered as an alternative potential metric, it was not determined to be appropriate for UAM operations in low altitude airspace. The choice of practical capacity is consistent with the FAA's use of the Monitor Alert Parameter (MAP) which recommends maximum sector capacities based upon a variety of operational factors that affect controller workload [8]. Capacity is preferred to throughput because it is an instantaneous metric (i.e.…”

Section: A Mechanism and Factor Influence Mappingmentioning

confidence: 92%

Scaling Constraints for Urban Air Mobility Operations: Air Traffic Control, Ground Infrastructure, and Noise

Vascik

Hansman

2018

2018 Aviation Technology, Integration, and Operations Conference

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The scalability of the current air traffic control system, the availability of aviation ground infrastructure, and the acceptability of aircraft noise to local communities have been identified as three key operational constraints that may limit the implementation or growth of Urban Air Mobility (UAM) systems. This paper identifies the primary mechanisms through which each constraint emerges to limit the number of UAM operations in an area (i.e. the scale of the service). Technical, ecosystem, or operational factors that influence each of the mechanisms are also identified. Interdependencies between the constraints are shown. Potential approaches to reduce constraint severity through adjustments to the mechanisms are introduced. Finally, an effort is made to characterize the severity of each operational constraint as a function of the density of UAM operations in a region of interest. To this end, a measure of severity is proposed for each constraint. This measure is used to notionally display how the severity of the constraint responds to UAM scaling, and to identify scenarios where efforts to relieve the constraint are most effective. The overall purpose of this paper is to provide an abstraction of the workings of the key UAM operational constraints so that researchers, developers, and practitioners may guide their efforts to mitigation pathways that are most likely to increase achievable UAM system scale. U 2 American Institute of Aeronautics and Astronautics perceive to degrade their quality of life and enjoyment of their property. Local governments may use zoning or building codes to limit or prohibit aviation infrastructure development or operations. Finally, congressional representatives may compel the FAA to change ATC procedures to reduce noise, as was the case in the Los Angeles Residential Helicopter Noise Relief Act of 2013 [4]. While community acceptance of UAM activities may be influenced by numerous factors including privacy, viewshed, pollution, safety, and equity, aircraft noise has dominated recent public discourse and action and represents a key constraint for UAM operations. However, the successful implementation of helicopter-based UAM operations in São Paulo, as well as the large private and charter helicopter operations in Moscow and Mumbai, indicate that local values and expectations significantly influence the actual severity of this constraint. U.S. and European communities may exhibit increased sensitivity to aircraft noise compared to other potential UAM markets.These three operating constraints are of keen interest to researchers, potential operators, and transportation planners as they dictate if a UAM system may serve a few hundred customers per day or tens of thousands of customers per day. The contribution of this research is significant because it will support the estimation of achievable UAM system scale based upon local factors and the ecosystem of the city in which it is implemented. Furthermore, this work may support the evaluation of how potential design and operating decisi...

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Section: A Mechanism and Factor Influence Mappingmentioning

confidence: 92%

Scaling Constraints for Urban Air Mobility Operations: Air Traffic Control, Ground Infrastructure, and Noise

Vascik

Hansman

2018

2018 Aviation Technology, Integration, and Operations Conference

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“…To quantify this, the concept of Monitor Alert Parameter (MAP) is used in this study, representing a singular aircraft count threshold which quantifies sector capacity in a given airspace. The criteria for setting MAP values is defined in US Federal Aviation Administration (FAA) Order JO 7210.3Z [6], which depicts MAP as a "numerical trigger value intended to alert facility personnel when sector/airport efficiency may be compromised during specific time intervals." Essentially, MAP provides a measure to evaluate the controller's workload based on the number of flights manageable within a sector.…”

Section: Background a Airspace Sector Capacitymentioning

confidence: 99%

AirFusion: A Machine Learning Framework for Balancing Air Traffic Demand and Airspace Capacity Through Dynamic Airspace Sectorization

Zhou,

Pham,

Alam

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Balancing air traffic demand and airspace capacity is a key challenge in airspace management. This task requires situational awareness among air traffic controllers, necessitating the use of interpretable traffic forecasts and visual tools to facilitate well-informed decision-making processes.This paper proposed AirFusion, a machine learning framework designed to balance airspace demand and capacity through Dynamic Airspace Sectorization (DAS). DAS is a concept that involves the dynamic change of the sectors configuration in response to fluctuations in traffic demand. The proposed framework includes four key components: (i) demand and capacity prediction, leveraging the Temporal Fusion Transformer (TFT) -a high-performing multi-horizon prediction model that offers interpretable insights into temporal dynamics, enabling traffic demand and airspace sector capacity prediction with a 4-hour look ahead and 6-hour look back window; (ii) major flow identification, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to effectively learn traffic patterns and identify major traffic flows; (iii) DAS, optimizing airspace sector capacity by employing graph-based partitioning method to split the sector when predicted demand exceeds capacity; and (iv) visual interface, providing an interactive platform that presents the sector splitting boundary and key influencers for demand and capacity predictions, enabling well-informed timely DAS for air traffic controllers.To validate the proposed AirFusion framework, air traffic data from four selected sectors of Singapore Flight Information Region (FIR) in December 2019 is used for training and evaluation. The experimental results demonstrate the model's high accuracy, with a mean absolute error of 0.0234 for traffic demand prediction and 0.0291 for airspace sector capacity prediction. Furthermore, the R-squared values indicate high predictive performance, with an average of 0.9133 for traffic demand and 0.9605 for airspace sector capacity.

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“…The reason for adopting the four time scales between 15 to 60 minutes is that "15-minute" and "60-minute" (1 hour) are the commonly used horizons for evaluation of controllers' workload and air traffic planning purpose [42]- [45]. For example, in the MAP (Monitor Alert Parameter) model, the airspace capacity is computed on a 15-minute basis [46]. For the one-month en-route flight track data from 1st Dec. 2018 to 31st Dec. 2018, there will be 2976, 1488, 992, 744 weighted networks constructed over time when the time scales are set as 15 minutes, 30 minutes, 45 minutes and 60 minutes, respectively.…”

Section: B Critical Link Detectionmentioning

confidence: 99%

Critical Links Detection in Spatial-Temporal Airway Networks Using Complex Network Theories

Alam

Cai

et al. 2022

IEEE Access

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In an airway network, some critical links exist that are vital for the structural integrity and performance of the network. The detection of such links may assist with improving the imbalance between the limited airspace capacity and the ever-increasing traffic demand, which elicit flight delays, significant economic losses, etc. However, it is challenging to identify such links as they evolve (both in space and time) with changing traffic flow dynamics. This paper proposes a complex network approach for spatial-temporal critical links detection in a given airway network. First, flight track data is employed to characterize the airway network as weighted spatial-temporal networks. Then edge centrality and network percolation metrics are adopted to detect the critical links in each snapshot of the spatial-temporal networks. Afterward, the critical links detected by the two metrics are spatially overlapped to determine the final critical links over time. To examine the operational validity of the proposed method, we carry out a case study on the Southeast Asia airway network derived from one-month flight track data. Results demonstrate that the spatial distribution of the critical links varies over different traffic scenarios, and most of the identified critical links are found in the transition sectors with complex traffic situations. Four links, which are parts and at crosses of major trunk airways connecting to major navigation aids (VOR/DME) in the studied network, appear highly in all examined traffic scenarios. The unavailability of such links may lead to traffic flow disruptions. Observations by subject matter experts from air-traffic data visualizations demonstrate that the complex network-based methods can dynamically identify airway links that are operationally critical under time-evolving air-traffic scenarios. With good traffic flow prediction tools in the future, this method can be adopted to predict critical links in airway networks to better assist controllers in real-time air traffic management.

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Analyzing the operational capacity effects of the monitor Alert paramater (MAP)

Cited by 4 publications

References 0 publications

Scaling Constraints for Urban Air Mobility Operations: Air Traffic Control, Ground Infrastructure, and Noise

Scaling Constraints for Urban Air Mobility Operations: Air Traffic Control, Ground Infrastructure, and Noise

AirFusion: A Machine Learning Framework for Balancing Air Traffic Demand and Airspace Capacity Through Dynamic Airspace Sectorization

Critical Links Detection in Spatial-Temporal Airway Networks Using Complex Network Theories

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