On allowing a general form for unobserved heterogeneity in the multiple discrete–continuous probit model: Formulation and application to tourism travel

Bhat, Chandra R.; Astroza, Sebastián; Bhat, Aarti C.

doi:10.1016/j.trb.2016.01.012

Cited by 22 publications

(6 citation statements)

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“…Similarly, Juschten and Hössinger (2021) examined the joint choice of destination and transport mode among the Viennese population traveling on summer vacation within Austria. Bhat et al (2016) apply the Multiple Discrete-Continuous Probit model to study the leisure destination choice of domestic tourists in New Zealand. To the author's knowledge, by far the most comprehensive study dealing with leisure and tourism destination choice specifically in the Alpine regions was conducted for Switzerland by Tschopp et al (2010).…”

Section: Destination Choice On Vacationmentioning

confidence: 99%

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

2022

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In the face of a continuous increase in the number of tourists in the Alps, the associated traffic volumes, and the resulting negative externalities, there is an urgent need to design policies capable of managing tourist traffic efficiently and to invest in transport systems and infrastructure wisely, given the limited financial, spatial and environmental resources. Unfortunately, while there is a considerable research interest in long-distance travel and arrival/departure patterns of tourists, research on tourist mobility during the stay at the destination is almost non-existent. This prevents policy-makers from making informed decisions backed by scientific evidence. The paper presents a data collection study that contributes to filing this gap. This ‘data paper’ reports on the motivation, design and administration of a bespoke travel-activity survey of tourists at vacation destinations. It informs other researchers about the encountered difficulties and helps them optimize data collection effort in their future studies. Besides the contribution to survey methodology, the paper reports on a rare and policy-relevant dataset, provides descriptive results, and thereby contributes to evidence-based design of transport policies in tourist regions. With this paper, we also want to inspire and invite researchers to conduct further quantitative and modeling work in this under-studied field.

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Section: Destination Choice On Vacationmentioning

confidence: 99%

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

2022

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“…Its primary goal is to derive behavioral insights from machine-learning methods that can then be used to inform transportation planning and policy intervention. More specifically, the paper examines individual taste heterogeneity, an essential research topic in travel behavior modeling that has been a primary focus within the random utility framework (e.g., Bhat, 2000;Bhat et al, 2016;Li et al, 2016;Srinivasan and Mahmassani, 2003;Vij and Walker, 2016). To our knowledge, the application of interpretable machine learning to model heterogeneity in travel behavior is novel and is largely absent from the existing literature.…”

Section: Introductionmentioning

confidence: 99%

Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach

Zhao,

Yan,

Van Hentenryck

2019

Preprint

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Over the last decade, machine learning has been applied to many engineering and scientific disciplines, often achieving high predictive accuracy. Recent years, however, have witnessed an increased focus on interpretability and the use of machine learning to inform policy analysis and decision making. This paper applies machine learning to examine travel behavior and, in particular, on modeling changes in travel modes when individuals are presented with a novel (on-demand) mobility option. It addresses the following question: Can machine learning be applied to model individual taste heterogeneity (preference heterogeneity for travel modes and response heterogeneity to travel attributes) in travel mode choice?This paper first develops a high-accuracy classifier to predict mode-switching behavior under a hypothetical Mobility-on-Demand Transit system (i.e., stated-preference data), which represents the case study underlying this research. We show that this classifier naturally captures individual heterogeneity available in the data. Moreover, the paper derives insights on heterogeneous switching behaviors through the generation of marginal effects and elasticities by current travel mode, partial dependence plots, individual conditional expectation plots. The paper also proposes two new model-agnostic interpretation tools for machine learning, i.e., conditional partial dependence plots and conditional individual partial dependence plots, specifically designed to examine response heterogeneity. The results on the case study show that the machine-learning classifier, together with model-agnostic interpretation tools, provides valuable insights on travel mode switching behavior for different individuals and population segments. For example, the existing drivers are more sensitive to additional pickups than people using other travel modes, and current transit users are generally willing to share rides but reluctant to take any additional transfers. These insights can be used to inform transportation planning and the design of new mobility services.

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“…Thus, reducing computation time is a primary future research agenda. For instance, we could learn from a maximum approximate composite marginal likelihood approach, which has been applied to a similar modeling framework (Bhat 2011;Bhat, Astroza, and Bhat 2016). Resolving such computational issues would make this model far more useful for analyzing recreational behaviors of the heterogeneous population and thereby providing more meaningful welfare estimates for local policies in question.…”

Section: Discussionmentioning

confidence: 99%

“…In an empirical application to recreational forest visitation, their model statistically dominated other conventional approaches and revealed within-group taste heterogeneity for forest coverage. Bhat, Astroza, and Bhat (2016) introduced a finite discrete mixture of normals into their multiple discretecontinuous probit model. Their simulation exercise revealed that ignoring the continuous or discrete component of the mixing led to biased parameter estimates, and their empirical application for leisure trips further showed heterogeneity for cost and urban land cover in each latent segment.…”

Section: Introductionmentioning

confidence: 99%

Discrete and Continuous Preference Heterogeneity in a Kuhn-Tucker Model: Beach Recreational Demand

Kabaya

Kuriyama

2021

Land Economics

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On allowing a general form for unobserved heterogeneity in the multiple discrete–continuous probit model: Formulation and application to tourism travel

Cited by 22 publications

References 40 publications

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach

Discrete and Continuous Preference Heterogeneity in a Kuhn-Tucker Model: Beach Recreational Demand

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