关键词: 航空运输, 交通拥堵传播, 传递熵, 航路网络, 时空因果关系

Abstract: In order to deeply analyze the evolutionary characteristics of air traffic congestion situations, support air traffic control and operation of an air traffic route network, this paper conducted a study on congestion propagation of the air traffic route network. First, segment traffic flow, segment traffic density, and segment traffic convergence were selected as segment congestion identification indicators, and an FCM segment traffic state evaluation model was established. Second, a congestion propagation model based on transfer entropy theory was proposed, and the correlation between congestion and congestion propagation was analyzed. Finally, a method for identifying key routes based on propagation index and significant area index was developed. Real-measured data from the Guangzhou area control sector was used to validate the effectiveness of the proposed methods. The research results indicate that a congestion identification model that considers both macroscopic and microscopic features can effectively classify the traffic status of routes. Congestion propagation is strongly influenced by time and congestion levels. During nighttime periods, smooth traffic dominates in congestion propagation, with information dissemination being 30% higher than during daytime, and with a longer information validity period. Conversely, during daytime, heavily congested routes dominate congestion propagation, with information dissemination being approximately 1.2 times that of lightly congested routes, but with a shorter information validity period. Congestion propagation in the Guangzhou high altitude route network exhibits significant spatiotemporal variations, with congestion propagation peak periods mainly occurring around 12:00-14:00 and 18:00-20:00, slightly earlier than congestion peak periods. Key routes for congestion propagation include major east-west routes such as the Shanghai-Chongqing route. These patterns provide theoretical support for air traffic control units to implement control measures and optimize route structures.

Key words: air transportation, traffic congestion propagation, transfer entropy, air traffic route network, spatial temporal causality