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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

Demonstration of scenario tree.
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fig1: Demonstration of scenario tree.

Mentions: Analyze the impact of weather along time horizon, including the weather spreading modes (decide which region will be influenced) and the degree of its impact. The impact of weather is uncertain. As time stage transfers from time stage t to t + 1, the changes of weather may spread to different regions with a certain probability. And moreover, for a region, the impact degree of weather is also uncertain. Hence, a scenario can generate several subsequent scenarios with a certain decision probability. And, along the time horizon, a scenario tree can be established. Suppose Q = 3, N = 1, airspace is divided into 4 regions (shown in the left figure of Figure 1), weather spreading from north to south, and a scenario tree can be established as the right figure of Figure 1. In this scenario tree, the initiative scenario, that is, the root node A, is (P0,1region, P0,1degree).


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

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

Demonstration of scenario tree.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Demonstration of scenario tree.
Mentions: Analyze the impact of weather along time horizon, including the weather spreading modes (decide which region will be influenced) and the degree of its impact. The impact of weather is uncertain. As time stage transfers from time stage t to t + 1, the changes of weather may spread to different regions with a certain probability. And moreover, for a region, the impact degree of weather is also uncertain. Hence, a scenario can generate several subsequent scenarios with a certain decision probability. And, along the time horizon, a scenario tree can be established. Suppose Q = 3, N = 1, airspace is divided into 4 regions (shown in the left figure of Figure 1), weather spreading from north to south, and a scenario tree can be established as the right figure of Figure 1. In this scenario tree, the initiative scenario, that is, the root node A, is (P0,1region, P0,1degree).

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