A comprehensive model of regional electric vehicle adoption and penetration. (February 2017)

• Record Type: Journal Article
• Authors: Javid, Roxana J.; Nejat, Ali
• Is Part Of: Transport policy. Volume 54(2017)
• Page Start: 30
• Page End: 42
• Publication Date: 2017-02
• Subjects: Plug-in electric vehicles -- Adoption behavior modeling -- Regional penetration level -- Sustainable transportation -- Charging stations -- Socioeconomic factors; Transportation and state -- Periodicals; Transportation -- Rates -- Periodicals; 388
• DOI: 10.1016/j.tranpol.2016.11.003
• Languages: English
• ISSNs: 0967-070X

Abstract

Abstract: This study focused on the adoption of Plug-in Electric Vehicles (PEVs) as a policy towards having a more sustainable transportation with lower Greenhouse Gas (GHG) emissions. The current paper aimed to explore potential factors that can be attributed to purchasing PEVs in order to estimate their penetration in 58 California counties. A Multiple Logistic Regression Analysis was applied to the 2012 California Household Travel Survey dataset, which includes both PEV and conventional car buyers' information, as well as some other secondary data sources. The model developed a broad set of factors including demographic and travel-related characteristics, socioeconomic variables, and infrastructural and regional specifications. The results identified that a household's income, maximum level of education in the household, the buyer's car sharing status, charging stations density, and gas price in the region can significantly impact PEV adoption. The model was validated using data from the 2012 Household Travel Survey conducted in the Delaware Valley region. With sufficient data availability, the methodology can be applied to evaluate changes in vehicle fleet composition and the levels of emissions in response to transportation policies. The model is believed to have a wide range of applications in electricity utilizing, gasoline/diesel retailing, and battery and automotive manufacturing. Additionally, the model can assist policy makers and transportation planners to optimize their infrastructural investments by identifying counties where the response of drivers to added charging station would be maximized, implying that larger benefits can be achieved. Highlights: Income, education, charging station, and gas price are affective in PEV adoption. Travel time and vehicle in household are insignificant in PEV adoption. Level of PEV penetration in a county is associated with infrastructure.