Latent class nested logit model for analyzing high-speed rail access mode choice

Wen, Chieh-Hua; Wang, Wei-Chung; Fu, Chiang

doi:10.1016/j.tre.2011.09.002

Cited by 97 publications

(44 citation statements)

References 22 publications

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“…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. Koutsopoulos and Farah (2012) used latent classes to identify and model different patterns (or regimes) of driving behavior for a microscopic traffic simulator.…”

Section: Latent Class Models In Discrete Choice Analysissupporting

confidence: 81%

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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Section: Latent Class Models In Discrete Choice Analysissupporting

confidence: 81%

Integrating psychometric indicators in latent class choice models

Hurtubia

Nguyen²,

Glerum

et al. 2014

Transportation Research Part A: Policy and Practice

View full text Add to dashboard Cite

show abstract

“…The random parameters found in the RPL model (indicated by rp superscripts) were assumed to be normally distributed [see [5]] and had statistically significant standard deviations at 0.05 significance levels. [75,81,87,88]]. An attempt was made to develop LC random parameters logit model for this study.…”

Section: Estimation Resultsmentioning

confidence: 99%

Effects of Human-Centered Factors on Crash Injury Severities

Adanu

Jones

2017

Journal of Advanced Transportation

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Factors related to drivers and their driving habits dominate the causation of traffic crashes. An in-depth understanding of the human factors that influence risky driving could be of particular importance to facilitate the application of effective countermeasures. This paper sought to investigate effects of human-centered crash contributing factors on crash outcomes. To select the methodology that best accounts for unobserved heterogeneity between crash outcomes, latent class (LC) logit model and random parameters logit (RPL) model were developed. Model estimation results generally show that serious injury crashes were more likely to involve unemployed drivers, no seatbelt use, old drivers, fatigued driving, and drivers with no valid license. Comparison of model fit statistics shows that the LC logit model outperformed the RPL model, as an alternative to the traditional multinomial logit (MNL) model.

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“…For example, Polydoropoulou and Ben-Akiva [20] show that parking fee, walk access time, the number of transfer and transit fare are the dominant factors in selecting travel mode on access and main mode. Meanwhile, by using latent class nested logit model, Wen et al [21] had been analyzing the high-speed rail access mode choice in Taiwan. The result showed that access cost, access time, parking fee, and waiting time have the significant impact on the access mode choice.…”

Section: Survey Design and Research Methodsmentioning

confidence: 99%

Analyzing Commuters’ Behavior on Egress Trip from Railway Stations in Yogyakarta, Indonesia

Irawan¹,

Putri²,

Belgiawan³

et al. 2017

TOTJ

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Background:Prambanan Ekspres Railway is known as one of the commuting modes in Yogyakarta, Indonesia. Sufficient egress modes do not support this railway. Due to lack of urban mass transit facilities, the commuters independently utilize a reliable mode for their mobility, for instance by owning a motorcycle and using overnight parking service facility in their non-home-station. Objective:This paper aims to understand the commuters' behavior on their egress trip when they decide to use the train as their main mode. Methods:A direct interview survey on the train was conducted during peak hours from Monday to Friday. By implementing stated preference survey, a logit model was used to analyze mode choice decision from the railway station to activity end destination. Results:The results indicate that walking distance to the parking area and bus-waiting time have a more significant impact compared to the walking distance to bus stop and in-bus travel time. Furthermore, the high cost of overnight parking also significantly influences the decision of choosing an egress mode. Otherwise, egress trip cost has less significance to encourage commuters' to shift to bus mode.

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Latent class nested logit model for analyzing high-speed rail access mode choice

Cited by 97 publications

References 22 publications

Integrating psychometric indicators in latent class choice models

Integrating psychometric indicators in latent class choice models

Effects of Human-Centered Factors on Crash Injury Severities

Analyzing Commuters’ Behavior on Egress Trip from Railway Stations in Yogyakarta, Indonesia

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