Simulation-Based Airport Capacity Estimation

Ramamoorthy, Krishnakumar; Hunter, George

doi:10.2514/6.2013-4354

Cited by 2 publications

(2 citation statements)

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“…Airspace capacity evaluation originated in the 1940s [5] . The main methods commonly used today are mathematical model-based methods [6] , controller workload-based methods [7] , computer simulation-based methods [8] , and data-driven methods [9] . Cheung et al proposed a Mixed Integer Programming (MIP) airport scheduling optimization model considering runway capacity to address the problem of capacity-demand imbalance during peak hours, and verified the advantages of dynamic airport capacity and dynamic runway configuration over fixed capacity [10] .…”

Section: Introductionmentioning

confidence: 99%

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Logistics UAV Air Route Network Capacity Evaluation Method Based on Traffic Flow Allocation

Jia

Zhang

et al. 2023

IEEE Access

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A bi-level optimization model for the logistics UAV air route network capacity evaluation based on traffic flow allocation is designed in order to meet the future trend of large-scale and normalized operation of logistics UAVs. The maximum sorties of logistics UAVs that can be served by the air route network are the upper-bound model objective, namely, the maximum flow of the logistics UAV air route network. The impedance function is constructed by considering safety and efficiency factors, and the lowerbound model objective function with the minimum logistics UAV air route network impedance value. An improved particle swarm optimization(PSO) algorithm is combined with the method of the successive algorithm(MSA) for solving the bi-level optimization model. To verify the effectiveness of the proposed model and algorithm, a simplified logistics UAV air route network is built. The results show that the proposed algorithm obtains reliable results after 26 iterations, and most segments capacity utilization rate is more than 70%. Parametric analysis of safe separation and algorithm population size shows that the capacity of logistics UAV air route network decreases with the increase of safe separation and the decreasing trend is gradually slowed down, and the optimal algorithm population size corresponding to different safe separations also varies. Based on the study described above, a logistics UAV air route network based on actual geographic information data is constructed, and the experimental results demonstrate that the suggested technique could be used to a specific scale of logistics UAV route network capacity evaluation and had validity.INDEX TERMS air traffic management, urban air mobility, UAV logistics, airspace capacity.

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