An aggregate stochastic programming model for air traffic flow management

Exploring critical attributes during air traffic congestion with a fuzzy DEMATEL–ANP technique: a case study in Ninoy Aquino International Airport

“…It is further emphasized by decision-makers that other attributes may potentially be minimized accordingly when safety is kept to maximum. One concrete example is as follows: according to decision-makers, excess fuel costs associated to delays are less likely to be incurred by the airlines management when aircraft are kept on the ground instead of holding them in the air (i.e., by means of airborne holding at arrival holding stacks, rerouting, or speed controlling) which, in theory according to Ball et al [20] and Andreatta et al [33], and validated in reality by decisionmakers, is less safe to implement compared to ground holding. Another example compares the relevance between safety and both economic and social value.…”

Coordination of scheduling decisions in the management of airport airspace and taxiway operations

“…An extensive overview of early contributions can be found in Ball et al (2007), while more recent surveys can be found in Barnhart et al (2012), Bianco et al (2006) and Pellegrini and Rodriguez (2013). We observe that, while the coordination issue is often referred to multi-airport coordination, as in Aktürk et al (2014) or Andreatta et al (2011), this paper focuses on coordination of operations in a single airport. In the latter context, Balakrishnan andChandran (2010), andSölveling et al (2010) focus on the runway scheduling, Artiouchine et al (2008), Hu and Chen (2005), and Di Paolo (2008,2009) focus on the landing scheduling from airspace resources to runways, while other authors deal with the coordination of the TCA airspace and the runways (landing and take-off scheduling), e.g., D'Ariano et al (2012D'Ariano et al ( , 2015, Lieder and Stolletz (2016), Murça and Müller (2015), Samà et al (2013Samà et al ( , 2014Samà et al ( , 2015.…”

A network‐based dynamic air traffic flow model for short‐term en route traffic prediction

“…Thus, in practice, it is difficult to make sound online air traffic control strategies based on the trajectory-based model due to the short forecast horizon and the expensive computational cost.Efficient ATFM requires reasonable prediction of the whole traffic flow situations in the specified airspace, rather than the temporal-spatial information of individual aircraft. Therefore, the aggregate air traffic flow models are introduced recently, which focus on the overall distribution of the air traffic flow in the airspace volumes of interest [5][6][7][8][9][10][11][12][13][18][19][20][21][22][23][24]. Because the aircrafts in the airspace volumes are spatially aggregated, the dimension of the aggregated model depends solely on the number of airspace volumes rather than the total number of aircrafts in the airspace, which will reduce the computational cost significantly.…”

“…Because the aircrafts in the airspace volumes are spatially aggregated, the dimension of the aggregated model depends solely on the number of airspace volumes rather than the total number of aircrafts in the airspace, which will reduce the computational cost significantly. In addition, because the behaviour of the individual aircraft is not taken into account in the aggregated model, it is less sensitive to the uncertainty factors related to individual aircraft, such as the departure delay and the weather, and thus, a longer forecast time horizon with less prediction errors can be achieved.Recently, the aggregated approach is widely discussed in the literatures [5][6][7][8][9][10][11][12][13][18][19][20][21][22][23][24]. A stochastic framework with linear dynamic system model was developed by Sridhar et al, where the dimension of the model depends on the number of control volumes by introducing split parameters to describe the air traffic flow distribution in neighbouring airspace.…”