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A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

Cai K, Jia Y, Zhu Y, Xiao M - ScientificWorldJournal (2015)

Bottom Line: However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical.In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem.Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China ; National Key Laboratory of CNS/ATM, Beijing 100191, China.

ABSTRACT
Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

No MeSH data available.


Related in: MedlinePlus

A schematic diagram of dividing airspace into 4 regions.
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fig2: A schematic diagram of dividing airspace into 4 regions.

Mentions: (1) Partition of Airspace. The airspace consisting of 25 sectors is divided into 4 regions. Figure 2 is a schematic diagram of the airspace partition, thick lines are boundary of each region, and the value in sector denotes sector number. Weather has the same impact on sectors which are in a same region. Each region contains a set of sectors as follows: 


A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

Cai K, Jia Y, Zhu Y, Xiao M - ScientificWorldJournal (2015)

A schematic diagram of dividing airspace into 4 regions.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4477291&req=5

fig2: A schematic diagram of dividing airspace into 4 regions.
Mentions: (1) Partition of Airspace. The airspace consisting of 25 sectors is divided into 4 regions. Figure 2 is a schematic diagram of the airspace partition, thick lines are boundary of each region, and the value in sector denotes sector number. Weather has the same impact on sectors which are in a same region. Each region contains a set of sectors as follows: 

Bottom Line: However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical.In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem.Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China ; National Key Laboratory of CNS/ATM, Beijing 100191, China.

ABSTRACT
Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

No MeSH data available.


Related in: MedlinePlus