Latent class models of international air carrier choice

Wen, Chieh-Hua; Lai, Shan-Ching

doi:10.1016/j.tre.2009.08.004

Cited by 139 publications

(56 citation statements)

References 30 publications

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“…One way for public transport (PT) to achieve more competitiveness with private vehicles is to improve the service quality (SQ) Wen andLai 2010, Dell´Olio et al 2010). Evaluating the various aspects of PT could highlight the areas in which it has poor performance to improve service and thereby obtain new users.…”

Section: Introductionmentioning

confidence: 99%

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A Composite Index for Evaluating Transit Service Quality across Different User Profiles

Oña¹,

Oña²,

Diez-Mesa³

et al. 2016

JPT

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show abstract

Section: Introductionmentioning

confidence: 99%

“…In the area of data mining, there are advanced segmentation techniques, such as Cluster Analysis (CA), which enable the reduction of such heterogeneity. This technique has been applied in transport engineering and other fields (e.g., Wen and Lai 2010;Shiftan et al 2008;Prebensen 2005) with satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

A Composite Index for Evaluating Transit Service Quality across Different User Profiles

Oña¹,

Oña²,

Diez-Mesa³

et al. 2016

JPT

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show abstract

“…132-154, © 2016 ζ ∈ Z represent a particular passenger type, e.g., business or leisure. Let H ζ w be the probability for an individual in market w of belonging to passenger type ζ, which is usually modeled with a logit expression as well, where the exponent is denoted by η(ζ | w) (Greene and Hensher 2003;Wen and Lai 2010). Let P w | ζ a be the probability that a passenger of type ζ in market w selects airline a ∈ A among all of the airlines.…”

Section: Nested Logit Model Developmentmentioning

confidence: 99%

Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail

Cadarso

Vaze

Barnhart

et al. 2017

Transportation Science

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Abstract. Airlines and high speed rail are increasingly competing for passengers, especially in Europe and Asia. Competition between them affects the number of captured passengers and, therefore, revenues. We consider competition between airlines (legacy and low-cost) and high speed rail. We develop a new approach that generates airline schedules using an integrated mixed integer, nonlinear optimization model that captures the impacts of airlines' decisions on passenger demand. We estimate the demand associated with a given schedule using a nested logit model. We report our computational results on realistic problem instances of the Spanish airline IBERIA and show that the actual airline schedules are found to be reasonably close to the schedules generated by our approach. Next, we use this optimization modeling approach under multimodal competition to evaluate multiple scenarios involving entry of high speed rail into new markets. We account for the possibility of demand stimulation as a result of the new services. We validate our approach using data from markets that had an entry by high speed rail in the past. The out-of-sample validation results show a close match between the predicted and observed solutions. Finally, we use our validated model to predict the impacts of future entry by high speed rail in new markets. Our results provide several interesting and useful insights into the schedule changes, fleet composition changes, and fare changes that will help the airline cope effectively with the entry of high speed rail.

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“…Zhang et al (2009) used a latent class structure to model different intra-household choice mechanisms regarding car ownership. Wen and Lai (2010) used the latent class approach in the airline choice problem, identifying significantly different willingness to pay across consumer segments. Similar results were obtained by Wen et al (2012) but in the context of the choice of mode to access stations of a high-speed train.…”

Section: Latent Class Models In Discrete Choice Analysismentioning

confidence: 99%

Integrating psychometric indicators in latent class choice models

Hurtubia

Nguyen²,

Glerum

et al. 2014

Transportation Research Part A: Policy and Practice

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Latent class models are a convenient and intuitive way to introduce taste heterogeneity in discrete choice models by relating attributes of the decision makers with unobserved behavioral classes, hence allowing for a more accurate market segmentation. Estimation and specification of latent class models can be improved with the use of psychometric indicators that measure the effect of unobserved attributes in the individual preferences. This paper proposes a method to introduce these additional indicators in the specification of integrated latent class and discrete choice models, through the definition of measurement equations that relate the indicators to attributes of the decision maker. The method is implemented for two modechoice case studies and compared with alternative methods to introduce indicators. Results show that the proposed method generates significantly different estimates for the class and choice models and provide additional insight into the behavior of each class.

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Latent class models of international air carrier choice

Cited by 139 publications

References 30 publications

A Composite Index for Evaluating Transit Service Quality across Different User Profiles

A Composite Index for Evaluating Transit Service Quality across Different User Profiles

Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail

Integrating psychometric indicators in latent class choice models

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