Flight support Petri net construction based on Bayesian network structure learning

Journal of Civil Aviation University of China ›› 2020, Vol. 38 ›› Issue (3): 28-33.

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Flight support Petri net construction based on Bayesian network structure learning

XING Zhiwei¹, CHEN Zhe¹, XIA Huan², LUO Qian², CONG Wan², CHEN Fenghua³

（1. College of Electronic Information and Automation, CAUC, Tianjin 300300, China; 2. Second Research Institute, CAAC,Chengdu 610041, China; 3. Guangzhou Baiyun International Airport Incorporated, Guangzhou 510410, China）

Online:2020-06-26 Published:2020-06-22
About author:邢志伟（1970—），男，辽宁沈阳人，教授，博士，研究方向为民航装备与系统、民航智能规划与调度、机场交通信息与控制.
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Abstract

Abstract: Due to the limitation of airport resource and time window during peak hours, the actual process of flight support will be biased compared to the planned process. K2 algorithm of Bayesian network structure learning is employed into flight support working scenarios, proposing the Bayesian network structure learning model of flight support.Bayesian network and Petri net are combined to build a Bayesian acyclic Petri net in order to accurately describe the status alternation of aircraft during flight support procedures. Finally, the actual data of one domestic hub airport are used to construct flight support network structure. Experimental analyses show that the flight support network obtained from historical data can better fit the actual working flow.

Key words: Petri net, flight support, Bayesian network structure learning, K2 algorithm

CLC Number:

V352
TP391.9

XING Zhiwei, CHEN Zhe, XIA Huan, LUO Qian, CONG Wan, CHEN Fenghua. Flight support Petri net construction based on Bayesian network structure learning[J]. Journal of Civil Aviation University of China, 2020, 38(3): 28-33.

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Flight support Petri net construction based on Bayesian network structure learning

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