Two-Stage Model for Jointly Revealing Determinants of Noncompensatory Conjunctive Choice Set Formation and Compensatory Choice

Kaplan, Sigal; Bekhor, Shlomo; Shiftan, Yoram

doi:10.3141/2134-18

Cited by 13 publications

(5 citation statements)

References 22 publications

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“…The model specification assumes that the utility maximization choice depends on the attributes of the apartments included in the choice set. According to Kaplan et al, the inclusion of interaction terms between individual characteristics and apartment attributes for this particular case study results in only a minimal increase of the model's goodness of fit (8). Therefore, parsimony considerations suggest that the current model specification should not include interaction terms between individual characteristics and apartment attributes.…”

Section: Datamentioning

confidence: 86%

“…Results indicate that (a) the hypothesized model has an excellent goodness of fit, thus supporting the proposed behavioral paradigm; (b) the compensatory evaluation of the aggregate apartment groups at the choice set formation stage is related to neighborhood characteristics and individual socioeconomic characteristics; (c) accounting for the correlations between the multinomial and the ordered thresholds greatly improves the model's goodness of fit; and (d) the utility-based choice from the viable choice set is related to apartment structural features, number of roommates, presence of electrical appliances, and location amenities. Although the results derive from a particular survey interface, the survey design and the apartment database used were based on actual data drawn from real estate databases, and therefore the analysis is sufficiently general to provide information on local student residential choice in the study area (8,25).…”

Section: Conclusion and Further Researchmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Compensatory–Noncompensatory Choice Sets in Semicompensatory Models

Kaplan

Bekhor

Shiftan

2012

Transportation Research Record: Journal of the Transportation R

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Semicompensatory models represent a choice process consisting of an elimination-based choice set formation on satisfaction of criterion thresholds and a utility-based choice. Current semicompensatory models assume a purely noncompensatory choice set formation and therefore do not support multinomial criteria that involve trade-offs between attributes at the choice set formation stage. This study proposes a novel behavioral paradigm consisting of a hybrid compensatory–noncompensatory choice set formation process, followed by compensatory choice. The behavioral paradigm is represented by a mathematical model that combines multinomial-response and ordered-response thresholds with a utility-based choice. The proposed model is applied to a stated preference experiment of off-campus rental apartment choices by students. Results demonstrate the applicability and feasibility of incorporating multinomial-response thresholds into semicompensatory models.

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Section: Datamentioning

confidence: 86%

Section: Conclusion and Further Researchmentioning

confidence: 99%

Hybrid Compensatory–Noncompensatory Choice Sets in Semicompensatory Models

Kaplan

Bekhor

Shiftan

2012

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…Cantillo and de Dios Ortúzar (2005) argued that thresholds, which can be expressed as a function of socio-economics characteristics of the individual and the conditions under which the choice process takes place, can be used to determine whether a choice is in the choice set or not. The discussion on generating choice sets with respect of individual characteristics and choice scenarios is also presented in a list of recent studies, such as Kaplan et al (2009Kaplan et al ( , 2011, Castro et al (2013), Vij et al (2013). However, those studies only consider the choice scenarios where the universal set is manageable.…”

Section: Potentials In Activity-based Modeling and Policy Decision Mamentioning

confidence: 98%

Learning daily activity patterns with probabilistic grammars

Lee

2015

Transportation

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Daily activity pattern is the reflection and abstraction of actual individual activity participation on daily basis. It carries information on activity type, frequency and sequence. Preference of daily activity patterns varies among population, and thus can be interpreted as personal life styles. This paper advances studies on human daily activity patterns by providing new perspective and methodology in the modeling and learning of daily activity patterns using probabilistic context-free grammars. In this paper, similarities between daily activity pattern-which is defined as activity sequence-and language are explored. We developed context-free grammars to parse and generate daily activity patterns. To replicate people's heterogeneity in selecting daily activity patterns, we introduced probabilistic context-free grammars and proposed several formulations to estimate the probability of a context-free grammar with daily activity patterns observed in household travel survey. We conducted experiments on the proposed formulations, finding that under proper context-free grammar and problem formulation, the estimated probabilistic contextfree grammar is able to reproduce the observed pattern distribution in household travel survey with satisfactory precision. Practically, the proposed methodology sheds light on the issue of generating stochastic and accessibility-dependent choice sets for daily activity pattern models in certain activity-based modeling frameworks.

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“…The estimation of the two-stage models is computationally intensive and their severely restrictive assumptions impede their practical application. Several studies (see Kaplan et al 2009Kaplan et al , 2012 are relaxing the assumptions embedded in these models with respect to the number of alternatives and choice sets, the representation of threshold selection and independently and identically distributed error terms across alternatives at the choice stage. Bierlaire et al (2010) show on simple examples that CMNL model is not adequate for modelling the choice set generation process consistently with Manski's framework.…”

Section: Literature Reviewmentioning

confidence: 99%

Constrained nested logit model: formulation and estimation

et al. 2017

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A model of traveller behaviour should recognise the exogenous and endogenous factors that limit the choice set of users. These factors impose constraints on the decision maker, which constraints may be considered implicitly, as soft constraints imposing thresholds on the perception of changes in attribute values, or explicitly as hard constraints. The purpose of this paper is twofold: (1) To present a constrained nested logit-type choice model to cope with hard constraints. This model is derived from the entropymaximizing framework. (2) To describe a general framework to deal with (dynamic) nonlinear utilities. This approach is based on Reproducing Kernel Hilbert Spaces. The resulting model allows the dynamic aspect and the constraints on the choice process to be represented simultaneously. A novel estimation procedure is introduced in which the utilities are viewed as the parameters of the proposed model instead of attribute weights as in the classical linear models. A discussion on over-specification of the proposed model is presented. This model is applied to a synthetic test problem and to a railway service choice problem in which users choose a service depending on the timetable, ticket price, travel time and seat availability (which imposes capacity constraints). Results show (1) the relevance of incorporating constraints into the choice models, (2) that the constrained models appear to be a better fit than the counterpart unconstrained choice models; and (3) the viability of the approach, in a real case study of railway services on the Madrid-Seville corridor (Spain).

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Two-Stage Model for Jointly Revealing Determinants of Noncompensatory Conjunctive Choice Set Formation and Compensatory Choice

Cited by 13 publications

References 22 publications

Hybrid Compensatory–Noncompensatory Choice Sets in Semicompensatory Models

Hybrid Compensatory–Noncompensatory Choice Sets in Semicompensatory Models

Learning daily activity patterns with probabilistic grammars

Constrained nested logit model: formulation and estimation

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