Comparative Analysis on Propagation Effects of Flight Delays: A Case Study of China Airlines

Wu, Weiwei; Wu, Cheng‐Lung; Feng, Tao; Zhang, Haoyu; Qiu, Shuping

doi:10.1155/2018/5236798

Cited by 17 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Delay spreading has received lots of attention from the Air Traffic Management (ATM) community during the last decade. Some studies [12][13][14] have established flight delay propagation models based on Bayesian Networks and analyzed the internal factors influencing air traffic delay propagation. Pablo Fleurquin [15] introduced an agentbased model that reproduces the delay propagation patterns observed in U.S. performance data and identified passenger and crew connectivity as the most relevant internal factor contributing to delay spreading.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach

Xie

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach

Xie

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…As found in FlightStats, in September 2019, there were 489,801 flight delayed and 34,821 flights cancelled worldwide. Thus, flight delay has attracted a lot of researchers’ attention [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…The existing research mainly focuses on flight delay or no delay [ 5 ], the distribution of delays [ 1 , 2 ], and the delay propagation through airlines and airports [ 3 , 4 ]. But in many cases, the factors affecting flight delay are not independent.…”

Section: Introductionmentioning

confidence: 99%

SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

Mei

Liu

Pan

et al. 2020

Sensors

View full text Add to dashboard Cite

There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task.

show abstract

“…Tu et al [12] employed a smoothing spline model to identify the relationship between seasonal trends, random effects, and daily delay propagation pattern. Delay propagation has also been deeply investigated by many researches to help to understand the air congestion [13][14][15] and alleviate fight delay [16,17]. The effects of day and time were assumed to be additive, and the residuals were assumed to be identically and independently distributed in the study.…”

Section: Introductionmentioning

confidence: 99%

Study of Flight Departure Delay and Causal Factor Using Spatial Analysis

Cheng

Ya-ping

Hao

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

Analysis of flight delay and causal factors is crucial in maintaining airspace efficiency and safety. However, delay samples are not independent since they always show a certain aggregation pattern. Therefore, this study develops a novel spatial analysis approach to explore the delay and causal factors which is able to take dependence and the possible problem involved including error correlation and variable lag effect of causal factors on delay into account. The study first explores the delay aggregation pattern by measuring and quantifying the spatial dependence of delay. The spatial error model (SEM) and spatial lag model (SLM) are then established to solve the error correlation and the variable lag effect, respectively. Results show that the SEM and SLM achieve better fit than ordinary least square (OLS) regression, which indicates the effectiveness of considering dependence by employing spatial analysis. Moreover, the outcomes suggest that, aside from the well-known weather and flow control factors, delay-reduction strategies also need to pay more attention to reducing the impact of delay at the previous airport.

show abstract

Comparative Analysis on Propagation Effects of Flight Delays: A Case Study of China Airlines

Cited by 17 publications

References 14 publications

Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach

Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach

SGDAN—A Spatio-Temporal Graph Dual-Attention Neural Network for Quantified Flight Delay Prediction

Study of Flight Departure Delay and Causal Factor Using Spatial Analysis

Contact Info

Product

Resources

About