A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions

By Chandra R. Bhat. 2005

555 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Transportation

View Paper: https://www.semanticscholar.org/paper/1a768d3399f03784710233a7efaaef42b1083111

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Citations in the corpus

Estimation of Water Demand in Developing Countries (2010, background): “…choice problem (for more details on these models see Greene 2003, chapter 21; for recent approaches that may be useful in the present context see Bhat 2005).5 Nauges and Whittington 271 at International M onetary F und on S eptem ber 15, 2010 w bro.oxfordjournals.org D ow nloaded from Models…”
The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions (2008, methodology): “Examples of multiple discreteness include situations where an individual may decide to participate in multiple kinds of maintenance and leisure activities within a given time period (Bhat, 2005), or a household may own a mix of different kinds of vehicles (such as a sedan and a pick-up truck or a se”
The Impact of Demographics, Built Environment Attributes, Vehicle Characteristics, and Gasoline Prices on Household Vehicle Holdings and Use (2009, methodology): “Then, following the derivation of the Multiple Discrete Continuous Extreme Value (MDCEV) model in Bhat (2005), the probability that the household uses the first Q of K vehicle types (Q ≥ 1) for annual mileages **2 * 1 ,...,, Qmmm may be written as: * * * 1 1 2 11 1 1( , ,... ,0,0,0,...,0) (…”
A Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) Model: Formulation and Application to Non-worker Activity Time-Use and Timing Behavior on Weekdays (2010, methodology): “…t t t J g V V V V V V d d ε ε ε ε ε ε ε ε ε ε ε ε ε ε ε ε ε ε ε + + − + + − − + − + − + − ++∞ =−∞ =−∞ =−∞ =−∞ =−∞ + + − − = − + − + − + ∫ ∫ ∫ ∫ ∫ 2 1 1... ,M Md d d ε ε ε+ + (8) where J is the Jacobian whose elements are given by (see Bhat, 2005) 1 1 1 1 1 * * 1 1 [ ] [ ] ;i iih h h V V V VJ t t…”
Residential self-selection effects in an activity time-use behavior model (2009, methodology): “Some more recent studies by Bhat and colleagues do include the impact of a comprehensive set of land use attributes in their time-use analyses (see, for example, Bhat, 2005, Bhat et al., 2006, Copperman and Bhat, 2007, and Sener and Bhat, 2007).”
Household Vehicle Type Holdings and Usage: An Application of the Multiple Discrete-Continuous Extreme Value (MDCEV) Model (2006, methodology): “The specific model used here is the mixed multiple discrete-continuous extreme value (MMDCEV) model, as recently developed by Bhat (2004).”
Activity-based Travel Demand Analysis (2011, methodology): “Random utility maximization and related microeconomic theory-based approaches have been particularly popular approaches to modeling activity time allocation (see Meloni et al., 2004; Bhat, 2005; and Jara-Diaz et al., 2007, for recent examples).”
Exploring Variation Properties of Time Use Behavior on the Basis of a Multilevel Multiple Discrete–Continuous Extreme Value Model (2010, methodology): “Given values of β , γ j ih, γ j h, γ j d, γ j s, and η j ihds, the following conditional probability is derived [model derivation processes are given by Bhat (11, 12)]:”
Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem (2012, methodology): “Contents lists available at SciVerse ScienceDirect Transportation Research Part B journal homepage: www.elsevier .com/ locate/ t rb Others (Kitamura, 1988; Recker, 1995; Bhat, 2005) have argued that travel behavior can indeed be captured within a rational utility-maximizing framework.”
Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review (2014, methodology): “Paleti, Bhat, Pendyala, and Goulias (2013) investigated the spatial dependence effects in the fleet composition decision of households by using an MNP model.”
Consumer preferences and willingness to pay for advanced vehicle technology options and fuel types (2015, background): “interior solution always exists because usage of all alternatives is greater than zero (Bhat, 2005).”
Recent Progress on Endogeneity in Choice Modeling (2005, background): “Bhat (2005) discussed a random utility theory-based model for multiple discrete/continuous choice outcomes, including (potentially) choice set formation.”
A discrete–continuous model of households’ vehicle choice and usage, with an application to the effects of residential density (2008, methodology): “Applying the Kuhn-Tucker conditions, we have, Hk = H1, if m∗k > 0 Hk H1, if m∗k = 0 where Hk = x′βk + θkln ∑ l∈Nk exp( z′klγ θ ) + lnsk + (sk − 1)ln(m∗k + 1) + εk (17) The probability that the first Q of the K vehicles being chosen, P (m∗1,m ∗ 2, ..., m ∗ Q, 0, ..., 0), is then derived from the ab”