Modelling the asymmetric probabilistic delay of aircraft arrival

Pérez‐Rodríguez, Jorge V.; Sánchez, José María Pérez; Gómez–Déniz, Emilio

doi:10.1016/j.jairtraman.2017.03.001

Cited by 38 publications

(15 citation statements)

References 18 publications

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“…Thus, flight arrival delays tend to be shorter than delays at the departure end in general. Departure delay and airborne buffer are used as important explanatory variables to estimate or predict the arrival delay [7,[14][15][16][17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Time-related variables, such as the time of day, the day of the week, and the season, are adopted by many models to estimate flight delays. Periodic patterns are shown as significant variables in empirical models in different airports and even in different countries [16,[19][20][21]. Air traffic flow management regulations will impact flight delays, which also increases the delay cost [22].…”

Section: Literature Reviewmentioning

confidence: 99%

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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%

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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“…is commonly used to analyze the factors of departure delay. First, we perform an ordinary least square (OLS) estimation based on the classical regression model (as in (6)). The estimation results of each variable are demonstrated asî n Table 6.…”

Section: Spatial Econometric Analysis the Regression Modelmentioning

confidence: 99%

“…Wesonga et al [5] proposed and evaluated a multiple parametric approach, which includes the apparently significant meteorological and aviation parameters, to predict the probability of aircraft delay. Recent research and development effort in delay probability prediction are seeking to develop asymmetric Bayesian logit model to take the asymmetric distribution pattern of the dependent variable into consideration (see Perez-Rodriguez et al [6]). By using data from BTS and IATA, this article corroborates the necessity and superiority of the proposed asymmetric Bayesian logit model, as well as identifying new significant factors affecting the probability of arrival delay.…”

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

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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.

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“…proposed an asymmetric Bayesian logit model to predict the daily delay probability of aircraft arrivals [5].…”

Section: Introductionmentioning

confidence: 99%

Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise

Tian

Wan

et al. 2019

Sustainability

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This paper first calculates the departure delay and arrival delay of each flight by mining historical flight data. Then, a new method based on density clustering for identification and visualization of restricted airspace units that considers this activity is proposed. The main objective is to identify the restricted airspace units by calculating the average delay time according to the accumulative delay time of airspace units and the accumulative delay flight. Therefore, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is utilized to match the latitude and longitude coordinates of each spatial domain unit with its delay time to construct a feature matrix, and then clustering analysis is conducted according to the time period. The method aims at identifying the most severe restricted units in each period. The reliability and applicability of the proposed method are validated through a real case study with flight information from Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport during a typical day. The investigation shows that the DBSCAN clustering method can identify the restricted spatial units intuitively on the six flight paths between Beijing Capital International Airport, Hongqiao International Airport, and Baiyun International Airport.

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Modelling the asymmetric probabilistic delay of aircraft arrival

Cited by 38 publications

References 18 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

Study of Flight Departure Delay and Causal Factor Using Spatial Analysis

Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise

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