Discrete Choice Models with Latent Variables Using Subjective Data

Morikawa, Takayuki; Sasaki, Kuniaki

doi:10.1016/b978-008043360-8/50024-8

Cited by 20 publications

(10 citation statements)

References 4 publications

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“…The model consisted of two parts-the first part was the SEM model, which was mainly used to describe the causal relationship between the latent variables of the trip mode choice and their corresponding observation variables, and between the latent variables and explicit variables. According to the literature [26,[29][30][31][32], five types of exogenous latent variables-convenience, reliability, comfort, safety, and economy-were selected. The perceptual value and the behavioral intention were endogenous latent variables, and the specific explanation of the selected variables is shown in Table 1.…”

Section: Model Specification and Hypothesismentioning

confidence: 99%

Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

Yang¹,

Guan²,

Cui³

2019

Sustainability

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With the development of Internet technology, online car-hailing is booming in China, which has profoundly affected people’s travel structures. In order to seek the sustainable development of taxi and online car-hailing services from the perspective of passenger mode choice behavior, the mechanism of passengers’ decision-making procedures and their travel mode choice behaviors were analyzed. To study the influence of latent variable factors on passenger choice behavior, this paper firstly designed a questionnaire, and a structural equation model (SEM) was established for the preliminary study of the relationship between the latent variables and the behavioral intentions using the online survey data. Then, the latent variables were introduced into the Logit model, setting up the SEM-Logit model to explore the mode choice patterns between taxis and online car services. The results showed that the SEM-Logit model with the latent variables is better than a general Logit model in terms of the model precision and hit ratio. Meanwhile, after introducing the latent variables, it was found that convenience, comfort, and economy factors have a significant influence on the model, and the explanatory power of the model increases accordingly.

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Section: Model Specification and Hypothesismentioning

confidence: 99%

Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

Yang¹,

Guan²,

Cui³

2019

Sustainability

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“…Whereas previous transportation related applications has included modal comfort and convenience (Morikawa and Sasaki, 1998;Morikawa et al, 2002), we extend the list of included latent variables with environmental preferences and individual preferences for flexibility and safety. Altogether we include five different latent variables in the choice model.…”

Section: Model and Estimationmentioning

confidence: 99%

“…The overriding purpose is to examine whether constructions of latent variables, mirroring the individualÕs preferences, are able to provide insights into the individualÕs decision making ''black box'' and, thus, to help to set priorities in governmental policy and decision making. In recent attempts to gain insight into the decision making process of the individual, traditional choice models have been enriched with constructions of latent variables (Ashok et al, 2002;Ben-Akiva et al, 1999;McFadden, 1986;Morikawa and Sasaki, 1998;Morikawa et al, 2002;Pendleton and Shonkwiler, 2001). For example, Morikawa and Sasaki (1998) and Morikawa et al (2002) include modal comfort and convenience in their analyses of mode choice.…”

Section: Introductionmentioning

confidence: 99%

“…In recent attempts to gain insight into the decision making process of the individual, traditional choice models have been enriched with constructions of latent variables (Ashok et al, 2002;Ben-Akiva et al, 1999;McFadden, 1986;Morikawa and Sasaki, 1998;Morikawa et al, 2002;Pendleton and Shonkwiler, 2001). For example, Morikawa and Sasaki (1998) and Morikawa et al (2002) include modal comfort and convenience in their analyses of mode choice. In their applications, the latent variables are measured and modelled through attitudes (attitudinal indicator variables) towards the chosen and an alternative travel mode.…”

Section: Introductionmentioning

confidence: 99%

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The effects of attitudes and personality traits on mode choice

Johansson

Heldt

Johansson

2006

Transportation Research Part A: Policy and Practice

300

247

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We hypothesise that differences in peopleÕs attitudes and personality traits lead them to attribute varying importance to environmental considerations, safety, comfort, convenience and flexibility. Differences in personality traits can be revealed not only in the individualsÕ choice of transport, but also in other actions of their everyday lives-such as how much they recycle, whether they take precautions or avoid dangerous pursuits. Conditioning on a set of exogenous individual characteristics, we use indicators of attitudes and personality traits to form latent variables for inclusion in an, otherwise standard, discrete mode choice model. With a sample of Swedish commuters, we find that both attitudes towards flexibility and comfort, as well as being pro-environmentally inclined, influence the individualÕs choice of mode. Although modal time and cost still are important, it follows that there are other ways, apart from economic incentives, to attract individuals to the, from societyÕs perspective, desirable public modes of transport. Our results should provide useful information to policy-makers and transportation planners developing sustainable transportation systems.

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“…Joint SP and RP estimation using SEM allows SP and RP error terms to be correlated while simultaneously testing for causal effects of RP (experiences) on SP (preferences). Morikawa and Sasaki (1998) employ an SEM in concert with a discrete choice model to capture the influence of latent subjective indicators of the attributes of choice alternatives on choice. Using a Dutch survey of inter-city travel and joint ML estimation, the authors conclude that models with causality only from attitudes to behavior perform less well than those that incorporate a causal feedback to attitudes from behavior.…”

Section: Attitudes Perceptions and Hypothetical Choicesmentioning

confidence: 99%

Structural equation modeling for travel behavior research

Golob

2003

Transportation Research Part B: Methodological

819

416

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Structural equation modeling (SEM) is an extremely flexible linear-in-parameters multivariate statistical modeling technique. It has been used in modeling travel behavior and values since about 1980, and its use is rapidly accelerating, partially due to the availability of improved software. The number of published studies, now known to be more than fifty, has approximately doubled in the past three years. This review of SEM is intended to provide an introduction to the field for those who have not used the method, and a compendium of applications for those who wish to compare experiences and avoid the pitfall of reinventing previous research. KeywordsStructural equation models, travel behavior, travel demand modeling, Statistical methods, discrete choice models, dynamic models, attitudinal data SEM for travel behavior Research

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Discrete Choice Models with Latent Variables Using Subjective Data

Cited by 20 publications

References 4 publications

Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model

The effects of attitudes and personality traits on mode choice

Structural equation modeling for travel behavior research

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