摘要
为研究航班延误传播规律,利用真实航班运行数据构建机场航班网络。基于易感-感染-免疫(Susceptible-Infected-Recovered,SIR)模型建立航班延误传播模型,选用梯度下降法求解模型参数;以最大连通子图比例(S)作为网络抗毁性指标,使用选择性攻击和随机攻击策略分析抗毁性;将航班延误传播模型与长短期记忆(Long Short-Term Memory,LSTM)网络预测模型、马尔科夫模型进行延误预测比较,引用历史数据进行实例分析。结果表明:机场航班网络平均路径长度为2.387,聚类系数为0.58,度分布呈双幂率分布,具有小世界和无标度特性;随机攻击删除节点累计300个,S>0.5,网络抗毁性强,选择性攻击删除节点比例15%~20%,S=0,网络抗毁性差;航班延误传播模型平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)分别与LSTM预测模型、马尔科夫模型相差4.41百分点、50.98百分点,具有更高精度。所提出的模型为航班延误提供了预测工具,为航空公司、机场等单位制定有效延误缓解措施提供参考。
To delve deeper into the complexities of flight delay propagation,we constructed an airport flight network using real flight operational data.This paper presents a flight delay propagation model based on the Susceptible-Infected-Recovered(SIR) model,and we utilized the gradient descent method to solve the model parameters.In the airport flight network,we utilized the maximum connected subgraph ratio(S) as the network's invulnerability index,and we analyzed its invulnerability through selective attack and random attack strategies.Furthermore,we compared the flight delay propagation model with the Long Short Term Memory(LSTM) prediction model and the Markov model for delay prediction,citing historical data for instance analysis.The results reveal that the average path length of the airport flight network is 2.387,with a clustering coefficient of 0.58.The degree distribution of the airport flight network follows a double power law distribution.The airport flight network demonstrates the characteristics of small-world and scale-free networks.In the invulnerability analysis of the airport flight network,a random attack that deletes a total of 300 nodes results in S>0.5,indicating strong invulnerability of the airport flight network.When the proportion of selective attack deletion nodes ranges from 15% to 20%,the value of S is 0,indicating poor invulnerability of the airport flight network.Additionally,the Mean Absolute Percentage Error(MAPE) for the flight delay propagation model is 16.7%,while the MAPE values for the LSTM and Markov models are 21.11% and 67.68%,respectively.The MAPE value of the flight delay propagation model differs by 4.41 percentage point from the LSTM prediction model and by 50.98 percentage point from the Markov model,indicating its higher accuracy.As such,the flight delay propagation model serves as a reliable prediction tool for flight delays,offering valuable insights for airlines and airports to formulate effective delay mitigation measures.
作者
梁文娟
连蓉蓉
LIANG Wenjuan;LIAN Rongrong(College of Safety Science and Engineering,Civil Aviation University of China,Tianjin 300300,China)
出处
《安全与环境学报》
CAS
CSCD
北大核心
2024年第10期3839-3846,共8页
Journal of Safety and Environment
基金
民航安全能力建设项目(ASSA2023/19)。
作者简介
梁文娟,副研究员,硕士,从事民航安全信息分析与应用、突发事件应急等研究,lwjcm@163.com。
