Airline Data Research Articles

The Impact of Transportation Cost on Inventor Mobility and Knowledge Spillovers

We contribute to the understanding of the microfoundations of regional competitive advantage by investigating the flows of human capital and knowledge when the transportation costs across regions are reduced by newly launched airline routes. We construct a dataset of interregional flows of human capital and knowledge between dyads of 362 U.S. mainland MSAs using the U.S. patent and inventor data to the U.S. airline data, from 1990 to 2014. To minimize the endogeneity concerns, we used two-stage linear squares (2SLS) estimation. The results show that the regions experience lower outmigration of inventors and are more active in technological innovation (e.g., the number of patents, the number of patenting firms). However, once we classify the regions as “core” or “periphery” on the basis of their patenting activities, we find that the technologically advanced core gains from lower transportation costs more than the technologically lagging periphery, which is in line with the agglomeration mechanism.

Strategic Airline Service Review in the Time of the COVID-19 Crisis

This study extracted airline data from several online source to examines operational and service strategy of the airline industry during the COVID-19 pandemic. The results have suggested that airlines were losing market shares in this pandemic situation except for those with high assets. In addition, this study utilizes text analytics techniques to provide insight into service characteristics that distinguish positive from negative reviews. The results suggest that satisfied travelers are demanding services with high empathy and responsiveness, while negative reviewers frequently complaints about insufficient operational aspects such as ground operations, mishandled baggage, system glitches, and staff management on handling cancellation.

A visualization and optimization of the impact of a severe weather disruption to an air transportation network

The task of visualizing data becomes more challenging as the size and complexity of the data increases. Specifically, for Air transportation networks, large data sets or Big Data contain temporal and spatial information that can facilitate decision-making during disruptions. This research explores the interpretation and visualization of time-dependent flight and weather data. We focus on visualizations that characterize the impact of severe weather disruptions to the Air transportation network with respect to traffic flow capacity and route connectivity. We include a binary integer programming optimization model to propose scheduling options that minimize delays for the disrupted flights. This study analyzes airline and weather Big Data during a severe weather event and highlights air traffic management options.

An improvement on shelter airport selection model during large-scale volcanic disasters: A case study of Hakoneyama Japan

Through the movement of passengers and cargo, air transportation contributes to economic growth and prosperity. As it expands in size and complexity, it becomes more susceptible to disruptions caused by weather conditions and natural disasters, such as volcanic eruptions. The eruptions of the Eyjafjallajökull and Merapi volcanoes caused significant problems in Europe and central Indonesia concerning air traffic and the economy in 2010. Numerous active volcanoes in Japan could severely disrupt the country's airspace and damage aircraft. The study is aimed at the challenge of determining how to select an airport as a safe place to land during an aircraft evacuation in the event of a volcanic eruption. Airside, airline, and historical data were used to design the shelter airport selection model. Later, it was used to discover an approximate solution for aircraft evacuation using a genetic algorithm (GA).

APLIKASI PENCATATAN HEALT ALERT CARD (HAC) PADA BANDARA SULTAN THAHA JAMBI BERBASIS WEB

Recording on the standby card or the so-called Health Alert Card (HAC) is still done manually at Jambi Sulthan Thaha Airport. This makes the work less effective and efficient in terms of time to complete work in the field. Seeing these conditions, an application is needed to assist Jambi KKP (Port Health Office) officers or employees in making a policy, therefore a Website-Based Health Alert Card (HAC) data input application for passengers at the Jambi Sulthan Thaha Airport. Which will be applied to assist Jambi KKP (Port Health Office) officers or employees in carrying out their duties to the maximum. With the Web-Based Health Alert Card (HAC) Registration Application at Sulthan Thaha Airport Jambi, it is hoped that it can assist officers or employees in inputting passenger HAC data that is still manual or still recorded as Web-based data input. That way every input data that is inputted to the computer through the Website Application will produce work that is effective, efficient and fast and safe to be stored and managed properly. Making this application is done by collecting data, system design, system analysis, and database design. Making this application program developed with PHP, the database used is MySQL. Data input needs in this application are departure data, airline data, individual admin data, and departure report data.

An iterative cost-driven copy generation approach for aircraft recovery problem

Airline schedules are frequently disrupted by unexpected events such as poor weather conditions, aircraft mechanical failures, airport congestions and crew absences, resulting in tremendous costs to airlines and passengers in addition to those originally planned. The Aircraft Recovery Problem (ARP) is usually solved to help airlines repair disrupted schedules by properly rescheduling their flights and rerouting the aircraft such that the total recovery cost is minimized while a set of operational and maintenance constraints are satisfied. When the flight retiming is allowed in a recovery solution, we can copy a flight by shifting its scheduled time and include the copies in the ARP model to determine either the original flight or one of its copies is to be flown by an aircraft. Many previous studies generate flight copies in some arbitrary manner and the quality of each copy is not assessed. In this paper, we propose a copy evaluation method and develop a solution approach to the ARP by incorporating the method within an iterative process of copy generation and filtration. The approach relies on repeatedly solving the ARP linear programming relaxation defined on time-space networks with respect to each aircraft and evaluating the copies to be added into the networks based on the solution obtained. The goal is to produce a limited number of copies but enough to help reduce recovery cost. Through computational experiments using real airline data, we demonstrate that our approach is able to provide promising recovery solutions in reasonable computational time.

Assessing efficiency in public service obligations in European air transport using Data Envelopment Analysis

In recent years, the use of Public Service Obligations (PSOs) in air transport policy has substantially increased. In remote regions, where air transport services are not profitable, the application of this subsidy program is crucial for the economic and social development of these regions. However, there is a great diversity in the provision of PSO services among different regions in Europe. Therefore, it is essential to assess the performances of the air carriers operating in different PSO routes. The present study uses Data Envelopment Analysis (DEA) to assess the efficiency of PSO routes within the European Union and compares them with the performances of the operating airlines, both when operating under this subsidy program and when operating their regular flights. First, DEA was applied to the annual data of several European airlines, and then applied to the data of their PSO routes, so that a comparison can be made between the performance obtained on regular flights and flights under the PSO regime. The results identify which airlines had the best and worst performances, as well as the routes and respective regions of Europe where the airlines tend to score higher and lower in terms of efficiency. Results suggest that the Nordic European countries and the United Kingdom were the regions where air carriers obtained the lowest results, and thus, this empirical evidence should support the public authorities to consider revisiting their PSO contracts, in order to improve their operation.

Data Analytics for Air Travel Data: A Survey and New Perspectives

From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future. We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research.

COVID-19 Salgını Döneminde Havacılık Sektöründe Kriz Yönetimi: Türk Havayolları (THY) Özelinde Bir Durum ÇalışmasıCrisis Management in the Aviation Sector During the COVID-19 Pandemic: A Case Study for Turkish Airlines (THY)

Purpose – The purpose of this study is to examine the impact of the global crisis that emerged with the COVID-19 epidemic on the aviation sector and the activities implemented in the crisis management process in particular for Turkish Airlines (THY). In addition, THY crisis management practices and crisis management practices recommended for the aviation industry in the literature were compared and the results were evaluated. Design/methodology/approach – In this study, the case study method which is one of the qualitative research designs, was used. The obtained data were analyzed with content and document analysis techniques. Secondary documents were used in document analysis. These documents; Turkish airlines' 2019-2020 annual reports, press briefing of Turkish airlines senior management, information on Turkish airlines website and data obtained through literature review. For content analysis, the short films on the official youtube and facebook pages of the institution were evaluated by content analysis and separated according to the most watched themes. Findings – According to the data obtained as a result of the analysis, it has been determined that the sudden emerging crisis of THY management has been carried out in accordance with the crisis management steps stated in the literature and is similar to the crisis management practices of the airlines in the world. Discussion – It is aimed that the research will contribute to the strategy and management literature by considering the crisis and crisis management concepts together with the aviation industry.

Airline transportation and arrival time of international disease spread: A case study of Covid-19.

In this era of globalization, airline transportation has greatly increased international trade and travel within the World Airport Network (WAN). Unfortunately, this convenience has expanded the scope of infectious disease spread from a local to a worldwide occurrence. Thus, scholars have proposed several methods to measure the distances between airports and define the relationship between the distances and arrival times of infectious diseases in various countries. However, such studies suffer from the following limitations. (1) Only traditional statistical methods or graphical representations were utilized to show that the effective distance performed better than the geographical distance technique. Researchers seldom use the survival model to quantify the actual differences among arrival times via various distance methods. (2) Although scholars have found that most diseases tend to spread via the random walk rather than the shortest path method, this hypothesis may no longer be true because the network has been severally altered due to recent COVID-related travel reductions. Therefore, we used 2017 IATA (International Air Transport Association) to establish an airline network via various chosen path strategies (random walk and shortest path). Then, we employed these two networks to quantify each model’s predictive performance in order to estimate the importation probability function of COVID-19 into various countries. The effective distance model was found to more accurately predict arrival dates of COVID-19 than the geographical distance model. However, if pre-Covid airline data is included, the path of disease spread might not follow the random walk theory due to recent flight suspensions and travel restrictions during the epidemic. Lastly, when testing effective distance, the inverse distance survival model and the Cox model yielded very similar importation risk estimates. The results can help authorities design more effective international epidemic prevention and control strategies.

Analyzing impact of number of features on efficiency of hybrid model of lexicon and stack based ensemble classifier for twitter sentiment analysis using WEKA tool

<span>Twitter is used by millions of people across the world, so the data collected from Twitter can be highly valuable for research and helpful in decision support. Here in this paper ‘Twitter US Airline data’ from Kaggle data repository is used for sentiment classification of customers’ reviews. The current research aims to implement various machine learning classifiers, Stack-based ensemble classifiers and hybrid of lexicon classifier with other classifiers. 11 different classification models are implemented for different sized feature sets. Also, all the 11 models are re-implemented by adding sentiment score of lexicon based classifier as one of the features in the feature set. Results are analyzed by varying number of input feature variables used in the classification. Four different size feature sets having 301,501, 701, and 1301 number of features are used to analyze the variations in the final findings. Chi-Square and Information gain techniques are used for feature selection. The results show that an increase in the number of features increases the accuracy up to 701 features. After that, accuracy is stable or decreases with increase in feature set size. Also, the cost of adding sentiment score of lexicon classifier to the input feature set is nominal, but the results are improved consistently. WEKA and R Studio tools are used for analysis and implementation. Accuracy and Kappa are used for representing and comparing the efficiency of models.</span>

Competitive revenue management models with loyal and fully flexible customers

Developing practical models for capturing competitive effects in revenue management and pricing systems has been a significant challenge for airlines and other industries. The prevalent mechanisms of accounting for competitive effects rely on changing the price structure and making manual adjustments to respond to dynamically evolving competitive scenarios. Furthermore, micro-economic models have also not become popular in practice primarily because of the simplistic mechanisms proposed for modeling consumer behavior in a competitive setting. In particular, many of these models assume that the customers always seek the lowest price in the market, that is they are fully flexible. In practice, customers may display some degree of affinity or loyalty to an airline and may pay a premium for their preferred choice. On the other hand, almost all early revenue management models did not explicitly consider competitive effects and assumed that an airline’s demand only depends on their prices i.e., demand is fully dedicated to an airline (loyal). This paper develops a model to capture more realistic competitive dynamics by including both these types of customer behavior. We also develop a Bayesian machine learning based demand forecasting methodology for such models with explicit competitive considerations and show the benefit of this approach over traditional models on a real airline data set.

A machine learning approach to itinerary-level booking prediction in competitive airline markets

Demand forecasting is extremely important in revenue management. After all, it is one of the inputs to an optimisation method which aim is to maximize revenue. Most, if not all, forecasting methods use historical data to forecast the future, disregarding the why. In this paper, we combine data from multiple sources, including competitor data, pricing, social media, safety and airline reviews. Next, we study five competitor pricing movements that, we hypothesize, affect customer behavior when presented a set of itineraries. Using real airline data for ten different OD-pairs and by means of Extreme Gradient Boosting, we show that customer behavior can be categorized into price-sensitive, schedule-sensitive and comfort ODs. Through a simulation study, we show that this model produces forecasts that result in higher revenue than traditional, time series forecasts.

A machine learning approach to itinerary-level booking prediction in competitive airline markets

Demand forecasting is extremely important in revenue management. After all, it is one of the inputs to an optimisation method which aims to maximise revenue. Most, if not all, forecasting methods use historical data to forecast the future, disregarding the 'why'. In this paper, we combine data from multiple sources, including competitor data, pricing, social media, safety and airline reviews. Next, we study five competitor pricing movements that, we hypothesise, affect customer behaviour when presented with a set of itineraries. Using real airline data for ten different OD-pairs and by means of extreme gradient boosting, we show that customer behaviour can be categorised into price-sensitive, schedule-sensitive and comfort ODs. Through a simulation study, we show that this model produces forecasts that result in higher revenue than traditional, time series forecasts.

The impact of COVID-19 on globalization

Globalization has altered the way we live and earn a livelihood. Consequently, trade and travel have been recognized as significant determinants of the spread of disease. Additionally, the rise in urbanization and the closer integration of the world economy have facilitated global interconnectedness. Therefore, globalization has emerged as an essential mechanism of disease transmission. This paper aims to examine the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel, and countries most impacted.The effect of globalization were operationalized in terms of mobility, economy, and healthcare systems. The mobility of individuals and its magnitude was assessed using airline and seaport trade data and travel information. The economic impact was measured based on the workforce, event cancellations, food and agriculture, academic institutions, and supply chain. The healthcare capacity was assessed by considering healthcare system indicators and preparedness of countries. Utilizing a technique for order of preference by similarity to ideal solution (TOPSIS), we calculated a pandemic vulnerability index (PVI) by creating a quantitative measure of the potential global health. The pandemic has placed an unprecedented burden on the world economy, healthcare, and globalization through travel, events cancellation, employment workforce, food chain, academia, and healthcare capacity. Based on PVI results, certain countries were more vulnerable than others. In Africa, more vulnerable countries included South Africa and Egypt; in Europe, they were Russia, Germany, and Italy; in Asia and Oceania, they were India, Iran, Pakistan, Saudi Arabia, and Turkey; and for the Americas, they were Brazil, USA, Chile, Mexico, and Peru. The impact on mobility, economy, and healthcare systems has only started to manifest. The findings of this study may help in the planning and implementation of strategies at the country level to help ease this emerging burden.

The airline transport regulation and development of public health crisis in megacities of China

BackgroundThe Civil Aviation Administration of China (CAAC) declares the airline transport regulation in January 2020 to help retard the spread of the novel coronavirus disease in China. This study is to examine the effect of airline transport regulation on confirmed cases of the novel coronavirus disease in megacities in China. MethodsThis study combines the multi-source data from the health data platform DXY, the airline data platform Airsavvi, the China Economic Internet Statistical Database and the China Railway website. The megacities whose airports have a passenger throughput of over 30 million per year (11 megacities: Wuhan, Beijing, Shanghai, Guangzhou, Chengdu, Shenzhen, Kunming, Xi'an, Chongqing, Hangzhou, Nanjing) are included in the analysis. The regression analysis is conducted in this study. ResultsThe curvilinear relationship between the limitation on air traffic and confirmed cases of the novel coronavirus disease is identified (coefficient of the linear term = −4.650, p-value < 0.01; coefficient of the quadratic term = 4.089, p-value < 0.01). ConclusionsThis study confirms the effectiveness of airline transport regulation in suppressing the development of this pandemic. The limitation on air traffic is found to negatively affect the confirmed cases in China's megacities. However, such effect marginally recedes as the strength of limitation intensifies. It suggests that comprehensive policy intervention is in need and air traffic can be one of important determinants that affect the epidemic development.

Impact of the COVID-19 pandemic on commercial airlines in the United States and implications for the kidney transplant community.

Many deceased-donor and living-donor kidney transplants (KTs) rely on commercial airlines for transport. However, the coronavirus-19 pandemic has drastically impacted the commercial airline industry. To understand potential pandemic-related disruptions in the transportation network of kidneys across the United States, we used national flight data to compare scheduled flights during the pandemic vs 1-year earlier, focusing on Organ Procurement Organization (OPO) pairs between which kidneys historically most likely traveled by direct flight (High Volume by direct Air transport OPO Pairs, HVA-OPs). Across the United States, there were 39% fewer flights in April 2020 vs April 2019. Specific to the kidney transportation network, there were 65.1% fewer flights between HVA-OPs, with considerable OPO-level variation (interquartile range [IQR] 54.7%-75.3%; range 0%-100%). This translated to a drop in median number of flights between HVA-OPs from 112 flights/wk in April 2019 to 34 in April 2020 (P<.001), and a rise in wait time between scheduled flights from 1.5hours in April 2019 (IQR 0.76-3.3) to 4.9hours in April 2020 (IQR 2.6-11.2; P<.001). Fewer flights and longer wait times can impact logistics as well as cold ischemia time; our findings motivate an exploration of creative approaches to KT transport as the impact of this pandemic on the airline industry evolves.

Distributed subdata selection for big data via sampling-based approach

With the development of modern technologies, it is possible to gather an extraordinarily large number of observations. Due to the storage or transmission burden, big data are usually scattered at multiple locations. It is difficult to transfer all of data to the central server for analysis. A distributed subdata selection method for big data linear regression model is proposed. Particularly, a two-step subsampling strategy with optimal subsampling probabilities and optimal allocation sizes is developed. The subsample-based estimator effectively approximates the ordinary least squares estimator from the full data. The convergence rate and asymptotic normality of the proposed estimator are established. Simulation studies and an illustrative example about airline data are provided to assess the performance of the proposed method.

An algorithm and method for sentiment analysis using the text and emoticon

Abstract People nowadays use emoticons in their text in increasing order in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF-IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.

A stochastic frontier regression model with dynamic frontier

We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence. The likelihood for the model is derived in the context of a bivariate exponential distribution. Bayesian method is suggested for the estimation of parameters. We apply the model and the estimation procedure to a panel of US airlines data and show empirically that the model is dynamic in the sense that it reveals improvement in the efficiency or reduction in the inefficiency over time.