TAFV Alternative Fuels and Vehicles Choice Model Documentation

Greene, David L.

doi:10.2172/814556

Cited by 49 publications

(57 citation statements)

References 19 publications

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“…It evolved from the TAFV (Transitional Alternative Fuels and Vehicles) model [6], but has been modified to focus on hydrogen rather than on an array of alternative fuels. The model is transitional in nature, has dynamic elements (such as learning curves), and contains a highlevel coupling between hydrogen vehicle demand and refueling station coverage.…”

Section: Resultsmentioning

confidence: 99%

“…Dynamic, Behavioral, Spatial System Dynamics Model (Massachusetts Institute of Technology (MIT)) A dynamic, behavioral, spatial model using System Dynamics as a framework was under development by MIT [9] at the time of this model review (June, 2005). The model simulated in one dimension the spatial, dynamic interaction between hydrogen refueling station coverage and hydrogen vehicle demand (a dynamic we consider to be particularly important for the hydrogen transition-see Section 5.3.4), and considered technical, economic, and behavioral parameters for both supply (i.e., refueling stations) and demand (i.e., vehicle/fuel purchases).…”

Section: Complex Adaptive System (Cas) Model (Rcf Consulting and Argomentioning

confidence: 99%

“…Such acquisitions were intended to create a seed for the development of an alternative fuel market by stimulating the production and purchase of alternative-fuel vehicles, thereby creating a demand for the alternative fuel. 6 In the initial stages of introduction for any alternative fuel, fleet markets have some advantages that make them attractive as market seeds. One advantage is that fleet vehicles tend to be able to refuel in a centralized location (as compared with average consumers, who require a large number of geographically dispersed refueling stations), making it easier to create a supportive fueling station infrastructure.…”

Section: Fleet Vs Consumer-driven Approachmentioning

confidence: 99%

See 2 more Smart Citations

Lessons Learned from Alternative Transportation Fuels: Modeling Transition Dynamics

Welch¹

2006

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Section: Resultsmentioning

confidence: 99%

Section: Complex Adaptive System (Cas) Model (Rcf Consulting and Argomentioning

confidence: 99%

Section: Fleet Vs Consumer-driven Approachmentioning

confidence: 99%

See 1 more Smart Citation

Lessons Learned from Alternative Transportation Fuels: Modeling Transition Dynamics

Welch¹

2006

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“…The general cost of technology set k depends on the average utility level within set k given by the following Equation (3) and is passed to the upper level to get the probability (P(k|l)) of choosing set k in vehicle class l by the same way of calculating P(j|k) [11]:…”

Section: Ev Penetration Modulementioning

confidence: 99%

Electric Vehicle Market Penetration and Impacts on Energy Consumption and CO2 Emission in the Future: Beijing Case

Zhang

Yan

et al. 2017

Energies

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This study focuses on the development of electric vehicles (EV) in the private passenger vehicle fleet in Beijing (China), analyzes how EVs will penetrate in the market, and estimates the resulting impacts on energy consumption and CO 2 emissions up to 2030. A discrete choice model is adopted with consideration of variables including vehicle technical characteristics, fuel prices, charging conditions and support policies. Results show that by 2030, without technological breakthrough and support policies, the market share of EV will be less than 7%, with gasoline dominating the energy structure. With fast technological progress, charging facility establishment, subsidies and tax breaks, EVs will account for 70% of annual new vehicle sales and nearly half of the vehicle stock by 2030, resulting in the substitution of nearly 1 million tons of gasoline with 3.2 billion kWh electricity in 2030 and the reduction of 0.6 million tons of CO 2 emission in 2030. Technological progress, charging conditions and fuel prices are the top three drivers. Subsidies play an important role in the early stage, while tax and supply-side policies can be good options as long-term incentives.

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“…Dedicated fuel vehicles such as EV and CNG vehicles use only the alternative fuel, and flex fuel vehicles can mix conventional and alternative fuels in the same fuel tank. There has long been interest in modelling the potential consumer response to the introduction of such vehicles, with examples including Train (1983), Bunch et al (1993), Train (1993), Golob et al (1995), Kavalece (1996), Tomkins et al (1998), Greene (2001), Batley and Toner (2003), Batley et al (2004), Adler et al (2004), and Spissu et al (2009) to name but a few. There is also interest in the long term response to changes in fuel prices, see e.g.…”

Section: Introductionmentioning

confidence: 99%

A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study

Hess

Fowler²,

Adler³

et al. 2011

Transportation

118

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In the face of growing concerns about greenhouse gas emissions, there is increasing interest in forecasting the likely demand for alternative fuel vehicles. This paper presents an analysis carried out on stated preference survey data on California consumer responses to a joint vehicle type choice and fuel type choice experiment. Our study recognises the fact that this choice process potentially involves high correlations that an analyst may not be able to adequately represent in the modelled utility components. We further hypothesise that a Cross-Nested Logit structure can capture more of the correlation patterns than the standard Nested Logit model structure in such a multi-dimensional choice process. Our empirical analysis and a brief forecasting exercise produce evidence to support these assertions. The implications of these findings extend beyond the context of the demand for alternative fuel vehicles to the analysis of multi-dimensional choice processes in general. Finally, an extension verifies that further gains can be made by using mixed GEV structures, allowing for random heterogeneity in addition to the flexible correlation structures.

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TAFV Alternative Fuels and Vehicles Choice Model Documentation

Cited by 49 publications

References 19 publications

Lessons Learned from Alternative Transportation Fuels: Modeling Transition Dynamics

Lessons Learned from Alternative Transportation Fuels: Modeling Transition Dynamics

Electric Vehicle Market Penetration and Impacts on Energy Consumption and CO2 Emission in the Future: Beijing Case

A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study

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