Airline on-time performance management

Hajko, Jakub; Badánik, Benedikt

doi:10.1016/j.trpro.2020.11.011

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…To achieve the data story objectives, two new attributes have been derived, which are "Departure Status" and "Arrival Status". According to the Federal Aviation Administration (FAA), a flight is said to be delayed when it is late for more than 15 minutes of its scheduled time [15]. Figure 3 shows the snapshots of the derivative attributes.…”

Section: Figure 1 Steps Involved In the Studymentioning

confidence: 99%

Data stories and dashboard development: a case study of an aviation schedule and delay causes

Salleh

Shukri²,

Othman³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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In this case study, five key processes in modelling a data story of aviation data patterns during COVID-19 have been executed. It started with the collection of secondary data from relevant sources. Data inspection, transformation, and preparation activities, including data cleaning, filtering, and sampling, are all included in this work. Iterative exploratory data analysis (EDA) has been conducted to determine the pattern of each independent attribute, followed by an assessment after the data story is modelled and integrated on a dashboard. The questionnaire has been distributed and the visuals were assessed by giving respondents a few tasks to interpret stories based on their comprehension. The result shows that the data stories have been interpreted in a similar narrative by all the respondents. The overall mean score is 4.71, and this significantly shows that the respondents agree and strongly agree that the visual objects help in communicating patterns and stories. The overall process gives researchers experience and guidelines for future work. Overall, the objectives of the study have been met. Nevertheless, it gives researchers a lot of experience in interpreting data, cleansing and transformation, analysis, modelling the visualisation by selecting suitable charts, and integrating the objects together into a dashboard.

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Section: Figure 1 Steps Involved In the Studymentioning

confidence: 99%

Data stories and dashboard development: a case study of an aviation schedule and delay causes

Salleh

Shukri²,

Othman³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…As the authors mentioned, weather is not the reason for every flight delay, but when they do the delay is significant. [7] Another method that is not widely used is to upgrade the aviation infrastructure. Zou and Hansen suggested this method and discovered that there is a balance in the complicated set of adjustment between passenger demand, air fare, flight frequency, aircraft size, and flight delays which will lead to an equilibrium.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Aircraft Arrival Delay Due to Weather

Charnvanichborikarn,

Kantanatha

2023

Advances in Transdisciplinary Engineering

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The world has seen a dramatic change in weather patterns caused by global warming. Extreme weather and unpredictable events are occurring more often. As weather is the most unpredictable component of a flight, if there is a slight chance of predictability in what the weather is going to be, it could help with the logistical issues and prevent further delays. Therefore, aircraft arrival prediction could potentially be a solution to improve the efficiency of the operation and the effectiveness of the contingency plans to respond to such events. The objective of this paper was to gain more insights and study the effect of weather on aircraft arrival delay by using random forest classification method and adaboost classification method. The results show that random forest classifier is the most accurate model for the weather delay prediction with the accuracy of 92.98 percent. When comparing F-1 score with adaboost, random forest model also gives higher values for all conditions.

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“…This finding may explain why some scholars find financial distress leads to more service failures (Phillips & Sertsios, 2013; Stamolampros & Korfiatis, 2019). Besides, our study contributes to more reliable empirical results by including an appropriate measure of on‐time performance, typically the most crucial service aspect (Hajko & Badánik, 2020), while the other three aspects of service examined are cancelations, mishandled baggage, and involuntary denied boardings. Since the US domestic airline market is quite an important and controversial market, our study could help provide a critical topic for debate for researchers and practitioners.…”

Section: Introductionmentioning

confidence: 98%

Financial distress and service decisions: Empirical evidence from the US domestic airlines

Wang

2023

Manage Decis Econ

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Understanding the firm's service decisions under financial distress is of central importance to service quality regulators and operation management scholars. This study aims to add new evidence to the relationship between financial distress and airline services and reconcile the discrepancies between theory, practical implications, and empirical results. We examine if and how airlines in financial distress may change their service provisions to turn around, with a particular focus on the moderating role of operating unit costs and market share. In the study, four measures of service failure (the inverse of service quality) are constructed and evaluated: airline arrival delay minutes attributable to the carrier, cancelations attributable to the carrier, mishandled baggage, and involuntary denied boardings. In the US domestic airline business context, we estimate the mixed-effects model using panel data from the Department of Transportation and Wharton Research Data Services. We find robust evidence that financial distress plays a vital role in airline service provisions, and the distress-service relationship is contingent upon the operating unit costs of airlines.The findings provide evidence to help explain the different and sometimes contradictory empirical findings. We depend on the aggressive strategy framework to understand the service effect of airline financial distress.

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Airline on-time performance management

Cited by 7 publications

References 10 publications

Data stories and dashboard development: a case study of an aviation schedule and delay causes

Data stories and dashboard development: a case study of an aviation schedule and delay causes

Prediction of Aircraft Arrival Delay Due to Weather

Financial distress and service decisions: Empirical evidence from the US domestic airlines

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