Dynamic discrete choice model for railway ticket cancellation and exchange decisions

Cirillo, Cinzia; Bastin, Fabian; Hetrakul, Pratt

doi:10.1016/j.tre.2017.12.004

Cited by 20 publications

(14 citation statements)

References 29 publications

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“…The works of Antonio et al (2017bAntonio et al ( , 2017c, Gayar et al (2011), were the only ones focused on hotels while Lan et al (2011), Lemke et al (2009Lemke et al ( , 2013, Morales & Wang (2010), and Pulugurtha & Nambisan (2003) worked with airlines or employ airline data. Azadeh et al (2013), Cirillo et al (2018), and Tsai (2011) worked with railways. The only document that did not deal with industries related to travel and tourism is that by Metzger et al (2012).…”

Section: Resultsmentioning

confidence: 99%

“…Cirillo et al (2018) developed a model to predict when railway customers would exchange or cancel their tickets. All of the previous works employed different methods: time series-based techniques (Lemke et al, 2009(Lemke et al, , 2013, economics-based techniques (Cirillo et al, 2018;Tsai, 2011), and more modern techniques usually applied in machine learning problems, like genetic algorithms, neural networks, or other advanced classification techniques (Antonio et al, 2017b(Antonio et al, , 2017cAzadeh et al, 2013;Morales & Wang, 2010;Pulugurtha & Nambisan, 2003). Except the works by Antonio et al (2017bAntonio et al ( , 2017c and Morales & Wang (2010), who considered bookings cancellation estimation a classification problem, all other authors considered it a regression problem.…”

Section: Resultsmentioning

confidence: 99%

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Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

António

Almeida²,

Nunes³

2019

TMS

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This study sought to combine data science tools and capabilities with human judgement and interpretation in order to demonstrate how semiautomatic analysis of the literature can contribute to identifying and synthesising research findings and topics about booking cancellation forecasting. The study also focused on recording in detail the analysis's full experimental procedure to encourage other researchers to conduct automated literature reviews in order to understand more fully the current tendencies in their field of study. The data were obtained through a keyword search in Scopus and Web of Science databases. The methodology presented not only diminishes human bias but also enhances data visualisation and text mining techniques' ability to facilitate abstraction, expedite analysis and improve literature reviews. The results show that, despite the importance of forecasting booking cancellations to understanding net demand and improving cancellation and overbooking policies, further research on this subject is needed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

António

Almeida²,

Nunes³

2019

TMS

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“…By exposing cancellation drivers, models help hoteliers to develop efficient cancellation policies and overbooking tactics. Authors in [18] proposed a dynamic discrete choice model for ticket cancellation and exchange with an application in the context of railway ticket purchase. Their model did not account for fare correlation among adjacent departure times and assumed that fares are not dependent on the demand.…”

Section: A Related Workmentioning

confidence: 99%

“…QoE Cancellation service Data mining Decision aid AHP Arline industry Revenue management [6] --- [7], [8] ---- [9] --- [10], [11], [12] --- [13], [14] ----- [15], [16], [17] ---- [18], [19] ---- [20], [21] ---- [22], [23] -…”

Section: Referencesmentioning

confidence: 99%

A Novel Cancellation Protection Service in Online Reservation System

Sadreddini

2020

IEEE Access

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Web-based systems are frequently used by the customers as well as travel agencies for online flight reservation and booking. In general, customers prefer to plan their trips as early as possible to take advantage of affordable booking opportunities as the price of flight tickets increases over time. However, changing the flight plan may lead to high penalties for the customers which may turn their profits into a loss in promotion tickets. Motivated by this, in this research work, quality of experience (QoE)-based approach is proposed to support user satisfaction in the aforementioned scenario. A cancellation protection service (CPS) is developed to provide assurance for early booking while establishing a balance between user satisfaction and the profit of the service provider (SP). Furthermore, three different CPS functions, namely Fixed CPS, QoE-based CPS, and Flexible CPS are modeled. In our proposed QoE-based CPS method, Analytic Hierarchy Process (AHP) is adopted to assign appropriate weights to different criteria. The proposed CPS approach can enable the customers to have a refund under certain terms if they decide to cancel their tickets. The proposed method is analyzed with real-world data from an airline reservation system which shows the customer transactions in a 6 months-period. The results indicate the effects of the fixed CPS, flexible CPS, and QoE-based CPS methods in the SP's profit points of view. Last but not least, the proposed QoE-based CPS method provides a balance between both the SP profit and user satisfaction.

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“…In recent years, research on passengers' travel preferences has gained popularity. Most studies use a logit model to analyze the choice of passengers between flights or railways by Moeckel et al [29], revealing factors influencing passengers' flight choices by Algers et al [30], Yan et al [31], Hagmann et al [32], and Fleischer et al [33], predicting the total number of air travelers [34], or optimizing passenger flows in a flight network, Dou et al [35] and Yang et al(2017). The above models are only suitable for normal transportation networks, not analysis of passengers' preferences in the case of an accident or disruptions.…”

Section: Passengers Preferencesmentioning

confidence: 99%

Considering Passenger Preferences in Integrated Postdisruption Recoveries of Aircraft and Passengers

Yang

2019

Mathematical Problems in Engineering

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After a disruption to its operations, an airline needs to dispatch its aircraft and accommodate affected passengers in order to resume normal operations. When facing a flight delay or cancelation, passengers usually have two options—switching to a different itinerary or receiving ticket refunds. This paper focuses on the integrated recovery of both aircraft and passengers by taking into consideration passengers’ preferences regarding the two options. Objectives include minimizing the financial loss of airlines and minimizing the utility loss of passengers. To solve the optimization problem, we present a loop-based multiobjective genetic algorithm (GA) by leveraging a special characteristic of flight networks. Experiments based on real-world data demonstrate the effectiveness and stability of the algorithm in various situations. The outcome has theoretical and practical implications for disruption management and airline operations.

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Dynamic discrete choice model for railway ticket cancellation and exchange decisions

Cited by 20 publications

References 29 publications

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

A Novel Cancellation Protection Service in Online Reservation System

Considering Passenger Preferences in Integrated Postdisruption Recoveries of Aircraft and Passengers

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