Market Price and Income Elasticities of New Vehicle Demands

McCarthy, Patrick

doi:10.2307/2109802

Cited by 99 publications

(53 citation statements)

References 13 publications

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“…Empirical evidence supports economic theory on this point. The overall price elasticity of demand for automobiles is generally agreed to be in the vicinity of -1.0 (see, e.g., Kleit, 1990;McCarthy, 1996). A survey of a dozen econometric studies of vehicle make and model choice produced a much larger consensus price elasticity estimate of -2.8 at 50 percent market share (Greene, 1994 (10) Assuming that fuel economy (MPG) will remain roughly constant over a vehicle's lifetime and that consumers use the current fuel price as the value for future fuel prices, the present value of fuel costs equals the current fuel cost per mile times discounted future vehicle miles of travel.…”

Section: Deductive Estimation Of Afv Model Coefficientsmentioning

confidence: 99%

“…Using equation (20) Luggage Space. The coefficient of luggage space is calculated from an assumed average value per cubic foot, A, and an average luggage volume, v. The value of luggage space per cubic foot is estimated based on other studies.…”

Section: R W H Fm Wh F M Rmentioning

confidence: 99%

“…Theory also indicates that the price elasticity of choice among technology sets should be less elastic than the overall price elasticity of demand for automobiles. There is a broad consensus in the econometric literature that the price elasticity of demand for automobiles is approximately -1.0 (e.g., Kleit, 1990;McCarthy, 1996;Bordley, 1994).…”

Section: Price Elasticitiesmentioning

confidence: 99%

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TAFV Alternative Fuels and Vehicles Choice Model Documentation

Greene

2001

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Section: Deductive Estimation Of Afv Model Coefficientsmentioning

confidence: 99%

Section: R W H Fm Wh F M Rmentioning

confidence: 99%

Section: Price Elasticitiesmentioning

confidence: 99%

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TAFV Alternative Fuels and Vehicles Choice Model Documentation

Greene

2001

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“…Since discrete choice analysis was introduced to solve transportation related problems, hundreds of studies have applied it in various fields (Bolduc, 1999). These studies have focused on analysing the behavior of the decision-making process such as modal choice (Bhat, 2000;Bolduc, 1999;Cohen and Harris, 1998;Commins and Nolan, 2011;Dissanayake and Morikawa, 2005;Ewing et al, 2004;Habib, 2012;Train, 1980), choice of car type (Choo and Mokhtarian, 2004;McCarthy, components of the utilities of various alternatives are independently and identically distributed (IID). This hypothesis creates several advantages in applications because calculation of choice probabilities, presentation, and interpretation of the parameters are easy (Baltas, 2007;Train, 2009).…”

Section: Introductionmentioning

confidence: 99%

Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model

Can

2013

Transportation Research Part A: Policy and Practice

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The purpose of this study is to examine how the characteristics of domestic tourists and attributes of travel modes influence the tourists' modal choice to Nha Trang, Vietnam by applying the multinomial probit model. The analysis is based on primary data surveyed from tourists visiting Nha Trang in March, 2011. A total of 402 valid samples were used from 554 initial samples. The study provides several important findings concerning tourists' modal choice. Travel time per kilometer, per-kilometer travel cost to income ratio, mode quality variables, and income are key elements in explaining the tourists' modal choice decision. In addition, tourists with a lower income tend to be more sensitive to change in per-kilometer cost. Furthermore, the high-income tourists are much more likely to choose plane or train rather than coach. Understanding the tourists' modal choice behavior may help tourism transport companies to develop appropriate marketing strategies.

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“…Discrete choice analysis has been used to investigate a range of transport related problems (Bolduc, 1999), all of which pertain to the behaviour of the decision-making process, such as modal choice (Bolduc, 1999;Bhat, 2000;Cohen and Harris, 1998;Commins and Nolan, 2011;Dissanayake and Morikawa, 2005;Ewing et al, 2004;Habib, 2012;Train, 1980), choice of car type (Choo and Mokhtarian, 2004;McCarthy, 1996), tourists' mode choice (Can, 2013;Jialing et al, 2013;Fesenmaier, 1988;Nicolau and Mas, 2006;Train, 1998), traveller latent perspective (Daly et al, 2012;Fleischer, 2012), survey quality to perceptual and attitudinal questions , and heterogeneous decision rules .…”

Section: Introduction and Past Studiesmentioning

confidence: 99%

Temporal and Parametric Study of Traveller Preference Heterogeneity Using Random Parameter Logit Model

Anwar

Tieu²,

Berryman³

et al. 2014

IJTTE

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Abstract:In travel demand models, traditional objective attributes (TOAs) are very commonly used as explanatory variables. Nowadays, it is understood that latent variables (LVs) also significantly influence travellers' behaviour. A hybrid choice modelling approach allows LVs in mode choice utility functions to be addressed. Specifically, a hybrid random parameter logit (HRPL) model has been developed to explore these influences. In this study, a traditional RPL (TRPL) model is compared with an HRPL model. For the later model, a two-step approach (also known as sequential approach) is implemented to incorporate LVs in choice models.Step 1 is the estimation of a MIMIC (multiple indicators and multiple causes) model; a type of regression model with a latent dependent variable(s).Step 2 is the estimation of a choice model with random parameters; information from the first step is incorporated in the second step. The paper analyses and compares the results of applying these models to a real urban case study using two datasets: 2008/09 and 2010/11 household travel survey (HTS) of Sydney Statistical Division (SSD), and also evaluates the predicted changes of mode choice probabilities based on hypothetical scenarios. Our results show that the HRPL model is superior to TRPL models that ignore the effect of LVs on traveller choice. The minimal changes in the parameter coefficients between the two datasets for each model suggest that the changes in traveller choice behaviour are gradual. Three hypothetical scenarios are simulated to forecast the changes that would be relevant to transport policy responses.

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Market Price and Income Elasticities of New Vehicle Demands

Cited by 99 publications

References 13 publications

TAFV Alternative Fuels and Vehicles Choice Model Documentation

TAFV Alternative Fuels and Vehicles Choice Model Documentation

Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model

Temporal and Parametric Study of Traveller Preference Heterogeneity Using Random Parameter Logit Model

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