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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 risk in deterministic model and our SATNFO model.
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fig4: Demonstration of risk in deterministic model and our SATNFO model.

Mentions: In each instance, capacity distribution changes according to its weather spreading mode and impact degree of weather on capacity. Table 2 illustrates the risk in deterministic model and our SATNFO model. The first column represents different capacity instances. The second and third columns list corresponding delay and operational risk obtained by our SATNFO model for each capacity instance. The last column is the operational risk if we do not consider changes of weather (the optimization result of the deterministic model). Figure 4 shows the comparison between the operational risk under deterministic model and our SATNFO model. In all 6 different capacity instances, our SATNFO model can obtain flight plans with less risk.


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 risk in deterministic model and our SATNFO model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Demonstration of risk in deterministic model and our SATNFO model.
Mentions: In each instance, capacity distribution changes according to its weather spreading mode and impact degree of weather on capacity. Table 2 illustrates the risk in deterministic model and our SATNFO model. The first column represents different capacity instances. The second and third columns list corresponding delay and operational risk obtained by our SATNFO model for each capacity instance. The last column is the operational risk if we do not consider changes of weather (the optimization result of the deterministic model). Figure 4 shows the comparison between the operational risk under deterministic model and our SATNFO model. In all 6 different capacity instances, our SATNFO model can obtain flight plans with less risk.

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