Derivation of representative air traffic peaks as standard input for airport related simulation

Öttl, G.; Böck, Philipp; Werpup, Nadja; Schwarze, Malte

doi:10.1016/j.jairtraman.2012.12.008

Cited by 8 publications

(9 citation statements)

References 5 publications

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“…From this method the required input parameters characterizing the evaluation environment can be specified. General traffic parameter needs for runway capacity related technology evaluation were also mentioned in previous publications of this application context (see Öttl and Böck, 2011;Öttl et al, 2013). As mentioned earlier, both traffic peaks and daily airport traffic are considered in this approach.…”

Section: Traffic Parameters For Capacity-related Applicationmentioning

confidence: 99%

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Traffic Related Representative Airport Categories for Technology Impact Evaluation

Öttl

2014

JATS

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Technology impact evaluations in air transport require the specification of environment conditions, such as the traffic structure. Since a multitude of worldwide traffic situations exists, this paper presents a systematic approach based on cluster analysis that can handle the worldwide diversity, while ensuring to determine most relevant traffic situations. This is crucial for the universality and global relevance of evaluation results. The approach is presented for the application example of runway capacity evaluation, as part of which features of daily movement distributions of airports and the traffic mix as well as peak situations are quantified. The resulting representative airport and peak categories comprise a limited set of typical traffic situations worldwide that can serve as standard input for capacity-related evaluation, ensuring comparability and clarity.

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Section: Traffic Parameters For Capacity-related Applicationmentioning

confidence: 99%

“…The example of runway capacity impact was considered, since the runway system is one of the most constraining elements influencing airport capacity (Böck and Hornung, 2012). Böck (2013) focused on the evaluation of capacity impact of aircraft concepts based on selected real airport environments only.…”

Section: Introductionmentioning

confidence: 99%

Traffic Related Representative Airport Categories for Technology Impact Evaluation

Öttl

2014

JATS

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“…Traffic peaks can be used to compute congestion indices, helping individuals and traffic management units to avoid traffic jam and minimize travel time [1][2][3][4]. By monitoring traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems [5][6][7]. Automatic traffic event detection is important in intelligent transportation systems (ITSs).…”

Section: Introductionmentioning

confidence: 99%

“…Effective peak detection remains a challenging problem in many fields, including asteroseismology [11,12], analytical chemistry [13,14], disease diagnosis [15,16], and traffic analysis [7,17]. Mathematically, a peak of a function is a local maximum point where the height of the function is the greatest in an interval around the point.…”

Section: Introductionmentioning

confidence: 99%

Detection and Optimization of Traffic Networks Based on Voronoi Diagram

Tao

Liu²,

Song

et al. 2021

Discrete Dynamics in Nature and Society

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Traffic peak is an important parameter of modern transport systems. It can be used to calculate the indices of road congestion, which has become a common problem worldwide. With accurate information about traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems. We present a traffic peak detection method, which constructs the Voronoi diagram of the input traffic flow data and computes the prominence of candidate peak points using the diagram. Salient peaks are selected based on the prominence. The algorithm takes O(n log n) time and linear space, where n is the size of the input time series. As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. The prominence of candidate peaks offers the subsequent analysis the flexibility to choose peaks at any scale. Experiments illustrated that the proposed method outperforms the existing smoothing-based methods in sensitivity, positive predictivity, and accuracy.

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“…Therefore demand-capacity evaluation and bottlenecks analysis play a key role in determining the physical requirement of airport facilities to meet future demand. Simulation modelling can be a cost-effective method for evaluating the systems when actual experimentation is impossible or impractical, and its application to airport performance evaluation has been widely accepted in the world (Galati et al, 2003;Kaltenhäuser, 2003;TheEditorial, 2003;James and Bhasi, 2010;Beck, 2011;Böck and Hornung, 2012;Öttl et al, 2013). There are a number of simulation models (eg, SIMMOD, TAAM and the Airport Machine) aiming at providing assistance in the detailed issues of airport operation (Bazargan et al, 2002;Herrero et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

A simple process simulation model for strategic planning on the airside of an airport: a case study

Chen

Gao

2015

Journal of Simulation

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Strategic planning for airport design and operations is a challenging task, since it should consider a variety of alternative futures, their consequences for airport performance and possible strategies for ensuring that the airport meets its business objectives. Existing models and tools for quantifying airport performance require significant detailed data, substantial airport modelling effort and do not readily produce the information relevant to decision makers. Therefore, a fast and simple simulation model is needed that provides insight into the key performance indicators of an airport for different strategies across different futures. In this paper, a simple simulation model is developed, and different scenarios with increasing traffic and changing traffic distribution are simulated with a general simulation platform called ProModel, which can provide output data for airport capacity evaluation and a bottleneck checking. Case study shows that the model can be easily executed and proved to be feasible to quickly generate interest results for decision makers.

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Derivation of representative air traffic peaks as standard input for airport related simulation

Cited by 8 publications

References 5 publications

Traffic Related Representative Airport Categories for Technology Impact Evaluation

Traffic Related Representative Airport Categories for Technology Impact Evaluation

Detection and Optimization of Traffic Networks Based on Voronoi Diagram

A simple process simulation model for strategic planning on the airside of an airport: a case study

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