Transactions of Nanjing University of Aeronautics & Astronautics
Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty
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Abstract:
This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management （ATFM） framework. Further with previous study， the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons， like the impact of weather， the strike of air traffic controllers （ATCOs）， the military use of airspace and the spatiotemporal distribution of nonscheduled flights， etc. These recessive factors affect the outcome of traffic flow optimization. In this research， the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing （DCB） optimization and ATFM， and multiple options， such as delay assignment and rerouting， are intended for regulating the traffic flow. A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution. The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems， solving large-scale instances （24 h on seven capacity scenarios， with 6 255 flights and 8 949 trajectories） in 5—15 min. To the best of our knowledge， our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.
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This work was partially funded by the China Scholarship Council （CSC）. The opinions expressed herein reflect those of the authors only. The authors would like to acknowledge the following people for their assistance： WAN Xianrong and YI Jianxin， both with the Electronic Information School， Wuhan University.
Clc Number:
U8
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