Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach
To better understand the mechanism of air traffic delay propagation at the system level, an efficient modeling approach based on the epidemic model for delay propagation in airport networks is developed. The normal release rate (NRR) and average flight delay (AFD) are considered to measure airport d...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8816615 |
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| _version_ | 1850213108739473408 |
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| author | Shanmei Li Dongfan Xie Xie Zhang Zhaoyue Zhang Wei Bai |
| author_facet | Shanmei Li Dongfan Xie Xie Zhang Zhaoyue Zhang Wei Bai |
| author_sort | Shanmei Li |
| collection | DOAJ |
| description | To better understand the mechanism of air traffic delay propagation at the system level, an efficient modeling approach based on the epidemic model for delay propagation in airport networks is developed. The normal release rate (NRR) and average flight delay (AFD) are considered to measure airport delay. Through fluctuation analysis of the average flight delay based on complex network theory, we find that the long-term dynamic of airport delay is dominated by the propagation factor (PF), which reveals that the long-term dynamic of airport delay should be studied from the perspective of propagation. An integrated airport-based Susceptible-Infected-Recovered-Susceptible (ASIRS) epidemic model for air traffic delay propagation is developed from the network-level perspective, to create a simulator for reproducing the delay propagation in airport networks. The evolution of airport delay propagation is obtained by analyzing the phase trajectory of the model. The simulator is run using the empirical data of China. The simulation results show that the model can reproduce the evolution of the delay propagation in the long term and its accuracy for predicting the number of delayed airports in the short term is much higher than the probabilistic prediction method. The model can thus help managers as a tool to effectively predict the temporal and spatial evolution of air traffic delay. |
| format | Article |
| id | doaj-art-8ba56eac3cb64e82bf717d037c81d7e0 |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8ba56eac3cb64e82bf717d037c81d7e02025-08-20T02:09:11ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88166158816615Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model ApproachShanmei Li0Dongfan Xie1Xie Zhang2Zhaoyue Zhang3Wei Bai4College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaCollege of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaAir Traffic Control Department, North China Air Traffic Management Bureau, Beijing 100621, ChinaTo better understand the mechanism of air traffic delay propagation at the system level, an efficient modeling approach based on the epidemic model for delay propagation in airport networks is developed. The normal release rate (NRR) and average flight delay (AFD) are considered to measure airport delay. Through fluctuation analysis of the average flight delay based on complex network theory, we find that the long-term dynamic of airport delay is dominated by the propagation factor (PF), which reveals that the long-term dynamic of airport delay should be studied from the perspective of propagation. An integrated airport-based Susceptible-Infected-Recovered-Susceptible (ASIRS) epidemic model for air traffic delay propagation is developed from the network-level perspective, to create a simulator for reproducing the delay propagation in airport networks. The evolution of airport delay propagation is obtained by analyzing the phase trajectory of the model. The simulator is run using the empirical data of China. The simulation results show that the model can reproduce the evolution of the delay propagation in the long term and its accuracy for predicting the number of delayed airports in the short term is much higher than the probabilistic prediction method. The model can thus help managers as a tool to effectively predict the temporal and spatial evolution of air traffic delay.http://dx.doi.org/10.1155/2020/8816615 |
| spellingShingle | Shanmei Li Dongfan Xie Xie Zhang Zhaoyue Zhang Wei Bai Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach Journal of Advanced Transportation |
| title | Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach |
| title_full | Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach |
| title_fullStr | Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach |
| title_full_unstemmed | Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach |
| title_short | Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach |
| title_sort | data driven modeling of systemic air traffic delay propagation an epidemic model approach |
| url | http://dx.doi.org/10.1155/2020/8816615 |
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