Characterization and Prediction of Air Transport Delays in China

Zanin, Massimiliano; Zhu, Yanbo; Yan, Ran; Dong, Peiji; Sun, Xiaoqian; Wandelt, Sebastian

doi:10.3390/app10186165

Cited by 17 publications

(8 citation statements)

References 37 publications

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“…In recent years, average departure delay has also attracted much attention. Research on departure delay optimization [34,35] and prediction [36,37] has achieved prominent results. For temporal local risk factors, we adopt average departure delay due to its significant position in air traffic risk assessment.…”

Section: Risk Coupling Modelmentioning

confidence: 99%

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Zhang

Zhao

Mei

2022

Journal of Advanced Transportation

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In recent years, due to the close coupling between airports, airport risk propagation has become a huge challenge. However, it has not been fully understood on the network level. Airport risk can be transferred through other airports owing to connected resources. In this study, we consider two risk factors including airport delay and saturation and propose a risk coupling model based on a clustering algorithm to fit the index and form risk series. To understand the risk propagation mechanism, we build risk propagation networks based on the Granger Causality test, and we apply complex network theory to analyze the evolution of the risk propagation network. We study the regular pattern of risk propagation from perspectives of time and space. Through network analysis, we find four time stages in the risk propagation process and the participation of airports in risk propagation has a positive correlation with airport sizes. In addition, more large airports tend to prevent risk propagation in unoccupied and normal situations, while small airports perform better than large airports in busy situations. Via the conclusion, our work can assist airlines or air traffic managers in controlling the scale of risk propagation before its key time turning point. By identifying the critical airport level and related factors in risk propagation, they can also reduce single airport risk and risk participation through corresponding risk control measures, finally avoiding the large-scale spread of risk and reducing delay or cancellation of more flights.

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Section: Risk Coupling Modelmentioning

confidence: 99%

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Zhang

Zhao

Mei

2022

Journal of Advanced Transportation

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“…Among the weather factors affecting the safety and efficiency of air traffic, TSTM is the most unfavorable weather phenomenon (Zanin et al, 2020). During the lifecycle of a thunderstorm cell (cumulus, mature, and dissipating), the three stages are dominated by strong ascending motions, coexisting updrafts, and downdrafts, as well as downdrafts, respectively.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal characteristics of hazardous weather affecting Chinese airports based on the ERA5/ERA5-land reanalysis dataset

2022

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The growing demand for air transportation has led to increased air traffic and airline operations at airports worldwide, while hazardous weather conditions have a considerable impact on the efficiency and safety of air traffic. The long-term and high-resolution state-of-art fifth-generation reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF) (ERA5) and ERA5-land provide us a perspective on the climatological characteristics of hazardous weather conditions affecting Chinese airports. These hazardous weather conditions include low-level wind shear (LLWS), limited visibility (LIMV), thunderstorms (TSTMs), and snowfall (SNOW). The LLWS is frequent in winter but rare in summer. At the airports in the north of eastern China, managers should take more precautions about LLWS from February to March and October to November. LIMV is major hazardous weather in the south of eastern China that should be carefully monitored in winter, especially from December to January. In northwestern China, LIMV is rare, especially at Yinchuan and Lanzhou airports. TSTMs frequently occur in South China and Southwest China, especially in Yunnan, Guangxi, Guangdong, and eastern Tibet. The corresponding active period is summer. SNOW frequently occurs over the Tibet Plateau and parts of Xinjiang province. SNOW is generally active in early winter (December–January) at Urumchi airport but at Lhasa airport in late winter (February–March). There is no SNOW throughout the year in the south of eastern China. The observations also verify such characteristics of the annual cycles of the four hazardous weather conditions. The trend analysis does not express many warnings on hazardous weather conditions except the SNOW. The SNOW at Lhasa airport shows an increasing trend. Considering the frequent SNOW in Lhasa, more attention should be put to monitoring this weather here.

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“…In search of the indicators that address network resiliency, ATFM delay (among various types of delays in ATM) is receiving more attention, since the comparative American and European reports categorized almost 80 percent of delays as the ATFM delay [11]. Recent studies [12][13][14] tend to address delay prediction with machine learning (ML) methods, which benefit from data availability in aviation compared to other means of transportation.…”

Section: Introductionmentioning

confidence: 99%

Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay

2021

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The European Air Traffic Management Network (EATMN) is comprised of various stakeholders and actors. Accordingly, the operations within EATMN are planned up to six months ahead of target date (tactical phase). However, stochastic events and the built-in operational flexibility (robustness), along with other factors, result in demand and capacity imbalances that lead to delayed flights. The size of the EATMN and its complexity challenge the prediction of the total network delay using analytical methods or optimization approaches. We face this challenge by proposing a deep convolutional neural network (DCNN), which takes capacity regulations as the input. DCNN architecture successfully improves the prediction results by 50 percent (compared to random forest as the baseline model). In fact, the trained model on 2016 and 2017 data is able to predict 2018 with a mean absolute percentage error of 22% and 14% for the delay and delayed traffic, respectively. This study presents a method to provide more accurate situational awareness, which is a must for the topic of network resiliency.

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Characterization and Prediction of Air Transport Delays in China

Cited by 17 publications

References 37 publications

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Analysis of Airport Risk Propagation in Chinese Air Transport Network

Spatio-temporal characteristics of hazardous weather affecting Chinese airports based on the ERA5/ERA5-land reanalysis dataset

Toward ATM Resiliency: A Deep CNN to Predict Number of Delayed Flights and ATFM Delay

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