Limits...
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 delay cost and risk with β.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4477291&req=5

fig3: Demonstration of delay cost and risk with β.

Mentions: Table 1 shows corresponding delay and risk with various β. Figure 3 shows the relationship between β and delay (left) and the relationship between β and risk (right).


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 delay cost and risk with β.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Demonstration of delay cost and risk with β.
Mentions: Table 1 shows corresponding delay and risk with various β. Figure 3 shows the relationship between β and delay (left) and the relationship between β and risk (right).

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