基于可视图的空中交通不安全事件时序特性分析

Time-series Characteristics of Unsafe Events in Air Traffic Based on Visibility Graph

空中交通安全事故的时间序列特性分析,是深入理解空中交通安全的重要手段。为分析空中交通不安全事件的时序特性,提出了基于可视图的不安全事件时序特性分析方法。采用可视图对空中交通不安全事件建模,将时间序列映射成复杂网络;再利用网络的度分布、聚类系数等拓扑指标,分析空中交通不安全事件的静态特征;在此基础上,考虑各事件间的高阶影响及作用模式,构造可视圈比指标,辨识不同事件对整体安全的差异化影响;再针对整体安全水平的动态演化特性,在可视图序模体基础上引入表征时序演化的三阶时序结构,描述不安全事件时间序列的微观演化特性。为验证所提方法的有效性,对2007—2021年美国发生的578起空中交通不安全事件进行实证分析,结果表明:①空中交通不安全事件时间序列可视图在宏观和微观尺度下度值均呈长尾分布,聚类系数均大于0.7;②不安全事件时间序列可视图网络具有小世界网络特征,宏观序列度分布服从于系数为1.852的幂律分布;③具有无标度特性不同地区的可视图网络同样具有小世界网络特征,地区间的网络规模与网络密度存在显著差异,揭示了不安全事件发生频率具有空间异质性可视圈的时序结构占比33.2%,圈比结构指标对网络鲁棒性具有重大影响,证明了圈比指标可用于辨识不同事件对整体安全水平的作用,辨识精度优于度值与节点强度等指标;④三阶时序结构在步长为1和2的情况下,呈现明显的转移特征。综上,空中交通不安全事件的发生是有别于随机性与周期性的复杂性系统性行为,不同区域间的不安全水平具有空间异质性与阶段演进性特征。考虑网络高阶结构影响,管控少数高圈比值节点可从宏观角度提升整体安全水平。分析时序结构的转移模式与趋势偏好,可以从微观角度揭示空中交通不安全事件随时间演变的内在规律。有助于预测潜在的风险点,从而为制定有效的预防措施和安全管理决策提供科学依据。

Time series characteristics of traffic accidents is crucial for understanding air traffic safety.To analyze the characteristics of air-traffic-accident time series,a visual graph(VG)method is proposed.The unsafe-event time se-ries(UETS)are mapped into complex network via the VG,and then the static characteristics of the UETS are de-scribed by the topological indicators such as degree distribution and clustering coefficient.Considering the high-er-order influences and interaction modes between events,a visual circle ratio index is developed to evaluate the im-pacts of each event on the entire safety level.A third-order temporal structure representing temporal evolution is pro-posed based on the sequential model from the VG,describing the dynamic micro-characteristics of the UETS.To demonstrate the proposed method,an empirical analysis is conducted based on 578 unsafe air traffic events that oc-curred in the United States from 2007 to 2021,and the results indicate that:①the VG of the UETS exhibit a long-tail degree distribution at both macroscopic and microscopic scales,with clustering coefficients all greater than 0.7;②the VG network of the UETS possesses small-world characteristics,and the macroscopic sequence-degree distribution follows the power-law distribution with a coefficient of 1.852,indicating scale-free properties of the net-work;③the visibility graphs of different regions also exhibit the characteristics of small-world networks,with sig-nificant differences in network size and density among regions,revealing the spatial heterogeneity in the frequency of unsafe events.The visual circle index of the network reaches 33.2%,the circle ratio structural indicator has a sig-nificant impact on network robustness,demonstrating that the circle ratio index can be used to identify the effects of different events on the overall safety level.④the third-order temporal structure shows significant transition charac-teristics when the step size is 1 and 2.In summary,this paper reveals that the occurrence of unsafe air traffic events has complex pattern that differs from randomness and periodicity patterns,The safety levels among different regions exhibit spatial heterogeneity and temporal evolution characteristics.Considering the impact of higher-order network structures,managing a minority of nodes with high circle ratios can enhance the overall safety level from a macro perspective.Analyzing the transfer patterns and trend preferences of temporal structures can reveal the intrinsic laws of how air traffic unsafe events evolve over time from a micro perspective.This is conducive to predicting potential risk points,thereby providing a scientific basis for formulating effective preventive measures and safety manage-ment decisions.