The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use

Abstract

In this paper, we formulate and estimate a nested model structure that includes a multiple discrete-continuous extreme value (MDCEV) The model developed in the paper is applied to predict the impact of land use and fuel cost changes on vehicle holdings and usage of the households. Such predictions can inform the design of proactive land-use, economic, and transportation policies to influence household vehicle holdings and usage in a way that reduces the negative impacts of automobile dependency such as traffic congestion, fuel consumption and air pollution.

Examining the influence of aggressive driving behavior on driver injury severity in traffic crashes

“…Earlier results have shown that middle-aged adults with family and children are most likely to own and drive vans (see Bhat et al, 2008). Such drivers tend to have more familial and financial responsibilities, which may make them act less aggressively when driving.…”

Private household demand for vehicles on alternative fuels and drive trains: a review

“…Subsequently, via statistical techniques, the analysis will derive a value for each of these attributes and thus express the relative preferences among vehicle attributes [29,79]. Conventional discrete choice (DC) models analyse situations in which respondents are asked to choose one alternative from a set of mutually exclusive hypothetical alternatives [17,50]. Recently, multiple discretecontinuous extreme value (MDCEV) models have been introduced that deal with the existence of multiple-vehicle households, where households own and use multiple vehicles for satisfying their travel needs [16,17].…”

“…Conventional discrete choice (DC) models analyse situations in which respondents are asked to choose one alternative from a set of mutually exclusive hypothetical alternatives [17,50]. Recently, multiple discretecontinuous extreme value (MDCEV) models have been introduced that deal with the existence of multiple-vehicle households, where households own and use multiple vehicles for satisfying their travel needs [16,17]. Other recent progresses in CM aim to improve realism, by for instance adding a nochoice option [31], or by customising attribute levels based on respondent's current vehicle choices [10,31,62].…”

The impact of a congestion pricing exemption on the demand for new energy efficient vehicles in Stockholm

“…-Individuals under 30 years of age were more likely to purchase non-exempt, lighter vehicles regardless of purchase price; -Females were more likely to purchase non-exempt, light, cheap vehicles, relative to all other alternatives; -Wealthier individuals were more likely purchase more expensive vehicles, regardless of exemption eligibility or vehicle weight/size; -Individuals living closest to the city were more likely to have smaller vehicles and also to have exempt EEVs -possibly reflecting higher environmental preferences amongst these residents (Bhat et al, 2009;Kahn, 2007), increased parking demands, and higher likelihood of crossing the cordon; -Owners of exempt EEVs had fewer vehicles (see Flamm (2009)) and owners of nonexempt vehicles had more vehicles -revealing perhaps that the economic benefits of the tax exemption outweighed the practical limitations of a smaller vehicle; and finally, -Owners with more children tended to have larger vehicles, however, there was little difference in preference among larger cars due to tax exemption or purchase price.…”