TY - GEN
T1 - A Scenario-Based Optimization Approach to Robust Estimation of Airport Capacity
AU - Ju, Fei
AU - Cai, Kaiquan
AU - Yang, Yang
AU - Gao, Yuan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/30
Y1 - 2015/10/30
N2 - Estimation of airport capacity plays a fundamental role in planning air traffic flow around the airport. Due to the impact of various dynamic factors on practical airport operation, e.g., the varying meteorological condition and changing fleet mix, airport capacity is characterized by uncertainties. The robustness of the existing iconic estimation approaches is challenged. This paper proposes a scenario-based optimization approach to robust estimation of airport capacity in the presence of the operational uncertainties. The capacity envelope identified through empirical analysis is associated with some probabilistic level and the estimation problem is then formulated as a chance-constrained optimization program approximately solved via scenario approach. Case study using real data set collected from Beijing Capital International Airport shows that the capacity envelope obtained by the proposed approach is more robust than two iconic approaches, i.e., proportion-based filtration approach and the quantile regression approach.
AB - Estimation of airport capacity plays a fundamental role in planning air traffic flow around the airport. Due to the impact of various dynamic factors on practical airport operation, e.g., the varying meteorological condition and changing fleet mix, airport capacity is characterized by uncertainties. The robustness of the existing iconic estimation approaches is challenged. This paper proposes a scenario-based optimization approach to robust estimation of airport capacity in the presence of the operational uncertainties. The capacity envelope identified through empirical analysis is associated with some probabilistic level and the estimation problem is then formulated as a chance-constrained optimization program approximately solved via scenario approach. Case study using real data set collected from Beijing Capital International Airport shows that the capacity envelope obtained by the proposed approach is more robust than two iconic approaches, i.e., proportion-based filtration approach and the quantile regression approach.
KW - Airport capacity robust estimation
KW - Airport operational uncertainty
KW - Chance-constrained optimization
KW - Scenario approach
UR - https://www.scopus.com/pages/publications/84950286162
U2 - 10.1109/ITSC.2015.334
DO - 10.1109/ITSC.2015.334
M3 - 会议稿件
AN - SCOPUS:84950286162
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2066
EP - 2071
BT - Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems, ITSC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Intelligent Transportation Systems, ITSC 2015
Y2 - 15 September 2015 through 18 September 2015
ER -