Airline Booking Research Articles

Enhancing the Usability, Visibility, and Responsiveness of an Airline Reservation System: A User-Centered Design Approach

This paper presents the idea, design, and prototype of a flight search and airline booking system based on the perspective of user-centered design. The system is first sketched roughly on paper in the form of a sketched plan and implemented through the proper system by connecting with the rapid API to develop a responsive web application. Booking travel tickets is a hassle and quite stressful because there is a chance that the webpages take time, and several decisions to make, hard to choose a discounted or less expensive flight, and the user will have to put in a lot of effort with many browser tabs may leave open. If a user is looking for the lowest travel options within a range of dates, they need to search a lot of websites looking for better options. As UX designers, it is our responsibility to do some user research and identify the problem areas, then we will recommend some design options based on the research findings. After that, we will create a wireframe and prototype before jumping into web design by collecting all the requirements and analyzing the problems. We will be focusing on UI controls such as location picker, date picker, color contrast, accessibility, and so on. In this paper, we present the design and development of a user-centered flight search and booking system for the airline industry. Our goal is to create a system that would meet the needs and preferences of a diverse set of users. This paper will summarize the design, development, and implementation of an airline reservation system. We have used bubble.io to design the overall system and MYSQL as the database management system for this webpage. Our objective is to upgrade the current website by improving the usability, visibility, and responsiveness of the functions that the user will experience while buying a flight ticket. We have generated and managed the design documentation and a perfect user-based online flight booking system.

다이내믹 토픽 모델링을 활용한 아프리카 사파리 관광 동향 분석

Tanzania in Africa is generating socio-economic benefits through safari tourism. Given that safari tourism centers on ecological resources and wildlife, tourists may feel less certain about what they will actually see or encounter. Tourists tend to utilize online reviews to reduce uncertainty and gain insights into trends. This study aims to understand the key topics and trend changes in perceptions of Tanzanian safari tourism products. English online review data from Safaribookings were crawled to accomplish the research purpose, and the Dynamic Topic Modeling (DTM) algorithm, which is suitable for temporal trend analysis, was utilized to address the research questions. The analysis revealed the following six major topics, all demonstrating trend changes: safari tourism recommendations, wildlife tourism experiences, experiences in transport payments, hotel and airline booking experiences, Mount Kilimanjaro tourism experiences, and tourism experiences in Zanzibar and Selous. The study results support that the DTM, which is used to overcome temporal limitations and implement systematic processes, is a systematic research method and can help expand the scope of research methodologies. Further, based on the analysis results, this study can aid stakeholders, such as safari marketers, in establishing Tanzania safari tourism marketing strategies.

Airline booking applications: the case of Ryanair, Easyjet and tap air Portugal

Airline booking applications: the case of Ryanair, Easyjet and tap air Portugal

The Impact of COVID-19 on Airfares—A Machine Learning Counterfactual Analysis

This paper studies the performance of machine learning predictions for the counterfactual analysis of air transport. It is motivated by the dynamic and universally regulated international air transport market, where ex post policy evaluations usually lack counterfactual control scenarios. As an empirical example, this paper studies the impact of the COVID-19 pandemic on airfares in 2020 as the difference between predicted and actual airfares. Airfares are important from a policy makers’ perspective, as air transport is crucial for mobility. From a methodological point of view, airfares are also of particular interest given their dynamic character, which makes them challenging for prediction. This paper adopts a novel multi-step prediction technique with walk-forward validation to increase the transparency of the model’s predictive quality. For the analysis, the universe of worldwide airline bookings is combined with detailed airline information. The results show that machine learning with walk-forward validation is powerful for the counterfactual analysis of airfares.

Pirating the skies? A review of airline booking ploys

In this paper, we review the literature on airline booking ploys/loopholes associated with practices of hidden-city, throwaway, and back-to-back ticketing. A diverse literature on this topic has developed, albeit that the emergence of this literature has to some degree been masked by the often-monodisciplinary approach of individual contributions. In this review, we discuss the main tenets of this increasingly wide-ranging literature and try to identify some of the most notable research gaps. Four research clusters are identified: (1) network revenue management, (2) aviation and consumer law, (3) tourism and business ethics, and (4) consumer marketing and travel journalism/media. Overall, we find that there have been limited interactions across individual contributions and the different clusters. In light of this, we argue that more coherence within the literature could benefit our understanding of airline booking ploys. The literature is furthermore imbued with a normative tone: some authors strongly oppose the exploitation of partial and out-of-sequence ticket use from a legal or ethical point of view, while others take a more nuanced position or even a pro-consumer stance. This is also reflected in the ways in which the fare loopholes are represented in non-academic accounts such as travel blogs, books, and news articles.

Will this search end up with booking? Modeling airline booking conversion of anonymous visitors


 Purpose
 
 Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors.
 
 
 
 Design/methodology/approach
 
 This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website.
 
 
 
 Findings
 
 Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits.
 
 
 
 Originality/value
 
 This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.
 

PERFORMANCE ASSESSMENT OF SCHEDULING ALGORITHM AS IMPLEMENTED IN AN AIRLINE BOOKING SYSTEM

This study entitled, “Performance Evaluation of Scheduling Algorithm as Implemented in an Airline Booking System” was conducted in the IT Department, Institute of Computing Engineering and Technology, Mati, Digos City, Davao Del Sur. The main objectives of the study is to evaluate the performance of an Airline Booking System implemented with priority scheduling algorithm and to evaluate its performance in terms of number of computers added to access the system. The tests were carried out using a database and a client computer. Twenty (20) client computers were used. Based on the result gathered, it was concluded that the performance of priority scheduling algorithm has no significant difference. On the other side, it was also concluded that the number of users or computer who accessed the system really affect the execution time speed. The researcher recommend to try another type of scheduling algorithm for further study. For further study, the researcher would like to recommend considering Networking infrastructure in making any application, consider also the number of users. And lastly, consider all the factors that might affect the performance of the system in terms of execution time speed and consider more than one process.

Forecasting the nearly unforecastable: why aren\u2019t airline bookings adhering to the prediction algorithm?

Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.

Galactic Air Improves Airline Ancillary Revenues with Dynamic Personalized Pricing

Ancillaries are a rapidly growing source of revenue for airlines, yet their prices are currently statically determined using rules-of-thumb and are matched only to the average customer, or to customer groups. Offering ancillaries at dynamic and personalized prices based on flight characteristics and customer needs could greatly improve airline revenue and customer satisfaction. Through a startup (Deepair) that builds and deploys novel machine learning techniques to introduce such dynamically priced ancillaries to airlines, we partnered with a major European airline, Galactic Air, to build models and algorithms for improved pricing. These algorithms recommend dynamic personalized ancillary prices for a stream of features (called context) relating to each shopping session. Our recommended prices are restricted to be lower than the human-curated prices for each customer group. We designed and compared multiple machine learning models and deployed the best performing ones live on the airline's booking system in an online A/B testing framework. Over a six-month live implementation period, our dynamic pricing system increased ancillary revenue per offer by 25% and conversion rate by 15% compared to the industry standard of human-curated rule-based prices.

Will this search end up with booking? Modeling airline booking conversion of anonymous visitors

Purpose Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors. Design/methodology/approach This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website. Findings Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits. Originality/value This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.

Assortment Optimization for Parallel Flights Under a Multinomial Logit Choice Model With Cheapest Fare Spikes

Airline booking data have shown that the fraction of customers who choose the cheapest available fare class often is much greater than that predicted by the multinomial logit choice model calibrated with the data. For example, the fraction of customers who choose the cheapest available fare class is much greater than the fraction of customers who choose the next cheapest available one, even if the price difference is small. To model this spike in demand for the cheapest available fare class, a choice model called the spiked multinomial logit (spiked-MNL) model was proposed. We study a network revenue management problem under the spiked-MNL choice model. We show that efficient sets, i.e., assortments that offer a Pareto-optimal trade-off between revenue and resource usage, are nested-by-revenue when the spike effect is nonnegative. We use this result to show how a deterministic approximation of the stochastic dynamic program can be solved efficiently by solving a small linear program. The solution of the small linear program is used to construct a booking limit policy, and we prove that the policy is asymptotically optimal. This is the first such result for a booking limit policy under a choice model, and our proof uses an approach that is different from those used for previous asymptotic optimality results. Finally, we evaluate different revenue management policies in numerical experiments using both synthetic and airline data.

Airline Booking Limit Competition Game Under Differentiated Fare Structure

We address a two-firm booking limit competition game in the airline industry. We assume aggregate common demand, and differentiated ticket fare and capacity, to make this study more realistic. A game theoretic approach is used to analyze the competition game. The optimal booking limits and the best response functions are derived. We show the existence of a pure Nash equilibrium and provide the closed-form equilibrium solution. The location of the Nash equilibrium depends on the relative magnitude of the ratios of the full and discount fares. We also show that the sum of the booking limits of the two firms remains the same regardless of the initial allocation proportion of the demand.

Safety Considerations for Medical Staff and Patients Who Fly Over Water in a Helicopter for Work or Recreation.

Around 25% of people involved in a helicopter accident in water do not survive. From time to time, physicians and their medical staff are required to fly over water in a helicopter to attend one or more seriously ill patients. Many will have had little or no experience of the issues involved if the helicopter has an accident in the water. Also as Family Practitioners, Aeromedical Examiners, and Flight Surgeons, they are asked to provide advice to patients, travel agents, and airline booking agents about whether an overwater helicopter flight is advisable or not. From 50 yr of helicopter accident evidence in the scientific literature, government agency reports, and statistics from the military safety centers and the offshore oil industry, the critical hazards involved and risks to medical staff and their patients have been identified. Patients most at risk are those who suffer from cardiovascular or respiratory disease, have physical disabilities, have a very large body size, and anyone who is a non-swimmer. Medical staff are at risk if they are not familiar with the procedure for escape from a flooded inverted cabin and difficulties after escape from the fuselage with life jackets, life rafts, and sometimes the necessity to swim ashore. With 50 yr of hindsight, many of the deaths were preventable, and many lives can be saved if a series of very simple mental and physical preventive actions are taken by anyone stepping on to a helicopter that flies over water.Brooks CJ, MacDonald CV. Safety considerations for medical staff and patients who fly over water in a helicopter for work or recreation. Aerosp Med Hum Perform. 2017; 88(4):413-417.

Investigation of Hong Kong Customers’ Preference in Searching for Tourism Products Online

Airlines and hotels focus on their direct-to-customer distribution through their websites. The inter-connectivity and inter-operability between hotels and airlines are major tools used by customers to connect with tourism organizations. An effective hotel and airline website can facilitate communication with customers, enabling the latter to share their information and to use the hotel's or airline's booking processes. The generalized use of the Internet and other information and communication technologies has significantly changed the tourism market. Likewise, easy access to the Internet has facilitated pricing transparency. Thus, customers can use the suppliers’ websites to search for information and compare different products. Hong Kong is one of the cities with high rates of Internet access attributed to the purchase of airlines tickets. Given the need for research on this area, the present study aimed to examine the searching behaviors of Hong Kong web users regarding tourism products. A purposive sampling of 30 interviewees indicated their various expectations from different travel websites. The search behavior of these individuals was also identified. Relevant implications for marketers are presented in this paper to enable them to fully understand the behavioral patterns of Hong Kong web users in searching for tourism information.

Pitfalls of airline revenue management observations

Given the high competition and the inherent variability of most markets, airlines learnt to quickly react to changes in their passenger Origin–Destination (O&D) flows, either by changing route capacities, frequencies, or by implementing Revenue Management (RM) techniques, such as changes in pricing and inventory control. However, properly assessing RM actions proves to be very difficult even with the most sophisticated systems of monitoring and reporting. Even analyzing the impact of granular strategies, such as restricting/relaxing availability for particular price points of particular O&Ds – Points of Sale, can be easily misled by censored data and comparison with unreliable detruncation methods, inaccurate estimations of optimal figures, or unexpected repercussions of other such strategies within the airline or its competitors. The aim of this article is to discuss the challenges of using airline bookings and cancellations data to assess RM performance and underline the differences between systems, market reality and user intervention.

African gateways: measuring airline connectivity change for Africa's global urban networks in the 2003–2009 period

This paper studies the globalization of major African cities through their changing insertion in global airline networks. As such, the paper adds to a growing body of literature that analyzes the role of infrastructure in the formation of world-city networks. We draw on a rich data source that provides longitudinal airline booking data, which can be directly linked up to the evolution of inter-urban networks at the African and global scale. Our results indicate that Johannesburg remains the major gateway to Africa, but other regional centers and in particular Cairo, Lagos, Casablanca and Nairobi are rapidly complementing Johannesburg in this role. The globalization of African cities is related to rapid network growth on the African continent, but is outrun by fast growth in terms of non-African connections. Among the latter, connections to Asia and the Middle East are rapidly gaining importance. In the end, the paper argues that South–South connections are crucial for an understanding of the contemporary globalization of African cities.

Intelligent Agent based Flight Search and Booking System

The world globalization is widely used, and there are several definitions that may fit this one word. However the reality remains that globalization has impacted and is impacting each individual on this planet. It is defined to be greater movement of people, goods, capital and ideas due to increased economic integration, which in turn is propelled, by increased trade and investment. It is like moving towards living in a borderless world. With the reality of globalization, the travel industry has benefited significantly. It could be said that globalization is benefiting from the flight industry. Regardless of the way one looks at it, more persons are traveling each day and are exploring several places that were distant places on a map. Equally, technology has been growing at an increasingly rapid pace and is being utilized by several persons all over the world. With the combination of globalization and the increase in technology and the frequency in travel there is a need to provide an intelligent application that is capable to meeting the needs of travelers that utilize mobile phones all over. It is a solution that fits in perfectly to a user’s busy lifestyle, offers ease of use and enough intelligence that makes a user’s experience worthwhile. Having recognized this need, the Agent based Mobile Airline Search and Booking System is been developed that is built to work on the Android to perform Airline Search and booking using Biometric. The system also possess agent learning capability to perform the search of Airlines based on some previous search pattern .The development been carried out using JADE-LEAP Agent development kit on Android.

The Journey of Redbus

E-ticketing in India has experienced tremendous growth in the last few years and has become a common and popular tool for booking airline and rail tickets. However, nobody thought it could work for bus reservations considering the fragmented nature of this segment. It was not until Phanindra Sama (Phani) felt it to be a huge customer dilemma, which could become a potential business opportunity. It was in the month of October 2005, when Phani decided to go home from Bangalore to Hyderabad to spend time with his family on Diwali—a major Indian festival. When he went to the bus stop to buy a ticket, he found that there was no ticket available as buses were running full on account of festival season. The agent had suggested that Phani contact other agents to find out the possibility of getting a ticket from one of them. Phani was extremely disappointed—he could not spend Diwali with his family. Yet, it also made him think; why weren’t there other ways to find out about the availability of bus tickets rather than running from agent to agent? Phani wondered why one simply could not book a bus ticket online—just like airline and railway bookings were made via the Internet. In this question lay Phani’s answer. Thus, Redbus was born.

Calculating load factors for the transatlantic airline market using supply and demand data – A note on the identification of gaps in the available airline statistics

This paper takes a critical view of the verification of load factors for the direct transatlantic airline market by combining supply and demand-data. The supply-related data originate from the Official Airline Guide, a well-known data source that contains information on scheduled flights. The demand-related data originate from the Marketing Information Data Transfer database, a data set containing information on actual airline bookings. Combining both data sets enables us to calculate the seat occupancy of direct flights between North-West Europe and the US for 2001. Based on the results, we assess the utility of the joint use of these data sources for aviation economic research, and identify various gaps in the available airline statistics.

A multi-flight recapture heuristic for estimating unconstrained demand from airline bookings

A new approach is described for airline revenue management (RM) demand unconstraining considering market recapture effects across multiple flights and fare classes. A good heuristic involving customer choice models is shown to provide a computationally efficient method for estimating demand, spill and recapture from historical bookings and availability data. By jointly estimating spill on closed alternatives and recapture onto open ones, the pervasive problem of double counted demands in RM system forecasts is avoided. Initial implementation experience shows the new methodology can be readily integrated into existing demand forecasting systems and should reduce forecast errors.