Analysis of Delay Propagation in China: From Complex Network Theory Perspective

Li, Qiang; Liu, Jusheng; He, Jianjia

doi:10.1109/itsc55140.2022.9921816

Cited by 1 publication

(3 citation statements)

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“…The results in Fig. 11 further demonstrate that when delays are produced at the highest-degree airports, the disaster area is the strongest [17]. The dynamic propagation of delays is shown in Fig.…”

Section: The Temporal Characteristics Of Delay Propagationmentioning

confidence: 72%

“…Cai et al [19] modeled delay propagation using complex network theory with airports as the nodes and proposed three metrics-magnitude, severity, and speed to analyze the characteristics of delay propagation. Li et al [17] also constructed a delay propagation network with airports as nodes and propagated delays as edge weights to explore delay propagation in China. Their results indicate hub airports play a more significant role in delay propagation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Numerous efforts have been made recently to construct a delay propagation network among airports and investigate the characterization of delay propagation at a network level [15][16][17]. The commonly used methods for creating the delay propagation network include the Granger causality test [13,18], convergent cross-mapping [14], and complex network theory [19,20] and exploring the topological characteristics of the delay propagation network, such as the largest connected cluster [13], the importance of airports [14], the magnitude, severity, and speed of delay propagation [20], and efficiency of delay propagation [18].…”

Section: Introductionmentioning

confidence: 99%

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Revealing Delay Propagation Patterns and Interactions in the Chinese Air Traffic System Using Bayesian Network Analysis

Guan

Liu

2023

Preprint

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Flight delays in the air traffic system exhibit strong correlations among different airports, making it crucial to comprehend the dynamics of delay propagation for optimal structural design and prevention of cascading delays. In this study, we adopt a Bayesian network (BN) approach, assuming that flight delays in individual airports are influenced by various potential airports. Through the application of complex network analysis, we enhance our understanding of delay propagation characteristics, while employing a BN-based analytical framework to uncover spatial and temporal patterns of delay propagation. Using Chinese air traffic data from 2015 to 2017 as a case study, our findings reveal that each airport's flight delays have an impact on approximately eight external airports, indicating a significant degree of interconnectedness within the air traffic system. Larger airports tend to be affected by delays originating from upstream airports, whereas smaller airports typically influence downstream airports. Additionally, economically developed regions experience broader delay propagation, impacting a larger number of airports compared to less developed regions. These insights provide valuable guidance for the development of delay propagation damping strategies and the implementation of efficient delay management techniques. By understanding the patterns of delay propagation and their spatial-temporal dynamics, stakeholders can take proactive measures to mitigate the impact of delays and optimize overall system performance.

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Section: The Temporal Characteristics Of Delay Propagationmentioning

confidence: 72%

Section: Literature Reviewmentioning

confidence: 99%