Measuring air traffic complexity based on small samples

Zhu, Xiaojin; Cao, Xianbin; Cai, Kai-Yuan

doi:10.1016/j.cja.2017.04.018

Cited by 23 publications

(13 citation statements)

References 20 publications

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“…Similar work was done by Xi ZHU et al [35]. The authors proposed a new model to measure air traffic complexity based on small samples.…”

Section: Overview Of Air Traffic Complexity Models and Methodsmentioning

confidence: 75%

“…It can be seen that all existing methods and models for determining air traffic complexity fall short in one of the categories. Whether it is the lack of data gathered for the machine-learning [35] or artificial networks [34], or it is simply that some models are tied to the specific airspace or to the exact personnel on whom they were tested, all this makes the model unusable for adequate complexity determination when trying to use it on different airspace and other ATCO personnel [16,25]. Finally, there is a problem of inconsistent complexity assessment by the controllers when the same controller assesses different levels of traffic complexity with the same score of complexity [36].…”

Section: Issues and New Solutionsmentioning

confidence: 99%

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Determining Air Traffic Complexity – Challenges and Future Development

Antulov-Fantulin

Juričić

Radišić

et al. 2020

PROMET

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Air traffic complexity is one of the main drivers of the air traffic controllers’ workload. With the forecasted increase of air traffic, the impact of complexity on the controllers' workload will be even more pronounced in the coming years. The existing models and methods for determining air traffic complexity have drawbacks and issues which are still an unsolved challenge. In this paper, an overview is given of the most relevant literature on air traffic complexity and improvements that can be done in this field. The existing issues have been tackled and new solutions have been given on how to improve the determination of air traffic complexity. A preliminary communication is given on the future development of a novel method for determining air traffic complexity with the aim of designing a new air traffic complexity model based on air traffic controller tasks. The novel method uses new solutions, such as air traffic controller tasks defined on pre-conflict resolution parameters, experiment design, static images of traffic situations and generic airspace to improve the existing air traffic complexity models.

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“…Similar work was done by Xi ZHU et al [35]. The authors proposed a new model to measure air traffic complexity based on small samples.…”

Section: Overview Of Air Traffic Complexity Models and Methodsmentioning

confidence: 75%

Section: Issues and New Solutionsmentioning

confidence: 99%

Determining Air Traffic Complexity – Challenges and Future Development

Antulov-Fantulin

Juričić

Radišić

et al. 2020

PROMET

View full text Add to dashboard Cite

show abstract

“…Further on searching for objective air traffic situation measurement, they have used a dynamic weighted network [36]. Some authors have used machine learning methods (ensemble learning models) [37,38] while others human-in-the-loop simulations [39]. Traffic complexity affects control task complexity (Figure 1), where the control is performed by human operator.…”

Section: Complexity Of Air Trafficmentioning

confidence: 99%

Relationship between Air Traffic Demand, Safety and Complexity in High-Density Airspace in Europe

Pejovic¹,

Netjasov²,

Crnogorac³

2020

Risk Assessment in Air Traffic Management

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Air traffic performance of the European air traffic system depends not only on traffic demand but also on airspace structure and its traffic distribution. These structural (airspace structure) and flow characteristics (factors such as traffic volume, climbing/descending traffic, mix of aircraft type, military area activity) influence airspace complexity, which can affect controller workload and influence the probability of safety occurrence. In other words, all these dynamic and static complexity components can potentially have an impact upon the safety of the air traffic management (ATM) system. Having in mind fluctuation in traffic on daily, seasonal or annual level in certain airspace, a few questions arise: How changes in traffic demand influence complexity and conflict risk? Is there any correlation between traffic demand, conflict risk and complexity? and Are there any differences between seasons? For that purpose, an investigation is performed on FAB Europe Central (FABEC) airspace, based on 2 weeks of operated traffic during the summer and fall of 2017. Air traffic complexity is estimated using the EUROCONTROL complexity metrics, while conflict risk is assessed using the conflict risk assessment simulation tool. Results show that certain positive relationship exists between traffic demand, conflict risk and complexity.

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“…and operational factors (presence of severe weather, amount of coordination required, frequency congestion etc.). These characteristics are specific to each airspace sector and change over time (Wang et al, 2013; Tobaruela et al, 2014; Zhu et al, 2017; Rahman et al, 2018).…”

Section: Problem Formulation and The Mathematical Modelmentioning

confidence: 99%

A Mathematical Programming Approach to Optimum Airspace Sectorisation Problem

2019

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The aim of this study is to provide a balanced distribution of air traffic controller workload (ATCW) across airspace sectors taking into account the complexity of airspace sectors and the factors affecting ATCW, both objective and perceived. Almost all the studies focusing on the airspace sectorisation problem use heuristic or metaheuristic algorithms in dynamic simulation environments instead of a mathematical modelling approach. The paper proposes a multi-objective mixed integer mathematical model for airspace sectorisation. The model is applied to the upper, en-route level of Turkish airspace. Geographical information systems (GIS) are used to advantage for airspace analysis. The multi-objective model developed in this paper is scalarised by using the conic scalarisation method. For solving the scalarised problem, the CPLEX and DICOPT solvers of GAMS software are implemented. Finally, the optimal sector boundaries of Turkish airspace are defined.

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Measuring air traffic complexity based on small samples

Cited by 23 publications

References 20 publications

Determining Air Traffic Complexity – Challenges and Future Development

Determining Air Traffic Complexity – Challenges and Future Development

Relationship between Air Traffic Demand, Safety and Complexity in High-Density Airspace in Europe

A Mathematical Programming Approach to Optimum Airspace Sectorisation Problem

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