How Late Does Your Flight Depart? A Quantile Regression Approach for a Chinese Case Study

Zhe, Zheng; Wei, Wenbin; Zou, Bo; Hu, Minghua

doi:10.3390/su122410553

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Later research noticed that turnaround buffer time is taken as a means to reduce propagated delays from previous flight legs. Estimation methods, including survival analysis, quantile regression, and Bayesian models, are used to analyze flight departure delay with delay propagation-related factors [10][11][12][13]. Beside the effect of delay propagation on turnaround, researchers also notice this effect when a flight is airborne.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Delay Propagation on Departure and Arrival Flights for a Chinese Case Study

Zhe

Wei

2021

Aerospace

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In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Delay Propagation on Departure and Arrival Flights for a Chinese Case Study

Zhe

Wei

2021

Aerospace

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“…They incur direct and indirect costs, such as for maintenance at the gate, extra fees for crew, food service, and lodging. They also affect passenger satisfaction [1,2]. An airport is a maintenance and transit hub where flight service begins and ends.…”

Section: Introductionmentioning

confidence: 99%

Flight delay forecasting and analysis of direct and indirect factors

Wang

Xie

et al. 2022

IET Intelligent Trans Sys

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The accurate prediction of flight delays is of great significance to airports, airlines and passengers. This paper presents a causal flight delay prediction model developed for a single airport. A long short-term memory network of delay prediction with an attention mechanism (LSTM-AM) is established to predict flight delays and analyse their primary causes. In this model, the direct and indirect factors related to delays are comprehensively considered. LSTM-AM can focus on input data combined with the attention vector to capture the critical time points, which can make the prediction more accurate. The model's performance is verified by actual operational data of Beijing International Airport, one of the busiest airports in the world. Experimental results show that LSTM-AM has better prediction accuracy than baseline algorithms such as some machine learning methods and deep learning methods. The mean absolute error of LSTM-AM is about 8.15 min on the test dataset. The study found that using the predicted results of this paper to release delayed information in advance can effectively alleviate the nervousness of passengers. The critical time point captured by LSTM-AM combined with runway and apron flow control can reduce or eliminate delays of one flight.

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Flight delay propagation modeling: Data, Methods, and Future opportunities

Li,

Mao,

et al. 2024

Transportation Research Part E: Logistics and Transportation Re

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How Late Does Your Flight Depart? A Quantile Regression Approach for a Chinese Case Study

Cited by 3 publications

References 17 publications

A Comparative Analysis of Delay Propagation on Departure and Arrival Flights for a Chinese Case Study

A Comparative Analysis of Delay Propagation on Departure and Arrival Flights for a Chinese Case Study

Flight delay forecasting and analysis of direct and indirect factors

Flight delay propagation modeling: Data, Methods, and Future opportunities

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