3 research outputs found
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More Public Charging Infrastructure Alone Will Not Increase Electric Vehicle Sales
Plug-in electric vehicles (PEVs), including battery electric vehicles and plug-in hybrid electric vehicles, are an important technology for decarbonizing transportation and reducing urban air pollution. A lack of public charging infrastructure is frequently cited as a primary barrier to continued, widespread PEV market growth. Public and private stakeholders are investing in public charging infrastructure, in part because they hope the presence of more infrastructure will encourage consumers to purchase PEVs. However, public charging infrastructure can only affect PEV sales if people—especially those who are not already PEV owners—see it, and by seeing it become more likely to consider purchasing a PEV. Researchers at UC Davis examined this relationship. They used data from a survey administered in the first quarter of 2021 of approximately 3,000 California car-owning residents, as well as data on PEV registrations and public charger locations. They modeled the relationships between multiple variables
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Long Distance Travel and Destination AttractivenessÂ
This report provides a summary of analyses using data of long distance tours by each household from an 8-week California Household Travel Survey travel log. The first analysis, uses Structural Equations Models (SEM) and a simpler variant called Path Analysis on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. The second analysis, uses Latent Class Cluster Analysis (LCCA) to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour and they are done by different types of households. The methods used here to identify the typology of long distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings
Recommended from our members
Long Distance Travel and Destination AttractivenessÂ
This report provides a summary of analyses using data of long distance tours by each household from an 8-week California Household Travel Survey travel log. The first analysis, uses Structural Equations Models (SEM) and a simpler variant called Path Analysis on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. The second analysis, uses Latent Class Cluster Analysis (LCCA) to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour and they are done by different types of households. The methods used here to identify the typology of long distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings