01557361

Component

https://doi.org/10.3141/2495-06

Policymakers, transport planners, automobile manufacturers, and others are interested in the factors that affect adoption rates of electric vehicles and other fuel-efficient vehicles. With tract-level data from the 2010 census and registered vehicle counts from Texas counties in 2010, this study investigated the impact of various built environment and demographic attributes, including land use balance, employment density, population density, median age, gender, race, education, household size, and income. Spatial autocorrelation (across census tracts) in unobserved components of vehicle counts by tract and cross-response correlation (both spatial and local–aspatial in nature) was allowed for by the estimation of models of ownership levels (vehicle counts by vehicle type and fuel economy level) with bivariate and trivariate Poisson–lognormal conditional autoregressive models. The presence of high spatial autocorrelations and local cross-response correlations was consistent in all models across all counties studied. Ownership rates for fuel-efficient vehicles were found to rise with household income, resident education levels, and the share of male residents and to fall in the presence of larger household sizes and higher job densities. The average fuel economy of each tract’s light-duty vehicles was also analyzed with a spatial error model across all Texas tracts, and this variable was found to depend most on educational attainment levels, median age, income, and household size variables, though all covariates used were statistically significant. If households registering more fuel-efficient vehicles, including hybrid electric vehicles, are also more inclined to purchase plug-in electric vehicles, these findings can assist in spatial planning of charging infrastructure as well as other calculations (such as implications for the revenue from gas tax).

01594381

15-3676

English

pp 53–64

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369565

Print

Figures (2) ; Maps; References (36) ; Tables (5)

Automobile ownership; Built environment; Demographics; Econometric models; Electric vehicle charging; Fuel consumption; Hybrid vehicles; Infrastructure; Plug-in hybrid vehicles; Spatial analysis; Urban design

Texas

Energy; Highways; Planning and Forecasting; Vehicles and Equipment

TRIS, TRB, ATRI

Dec 30 2014 1:13PM

• Assessing Goodness of Fit of Hybrid Choice Models: An Open Research Question
• Choice Set Generation for Modeling Scenic Route Choice Behavior with Geographic Information Systems
• Defining, Measuring, and Using the Lifestyle Concept in Modal Choice Research
• Differences in Travel Behavior Across Population Sectors in Jerusalem, Israel
• Evaluation Methods for Estimating Vehicle Miles Traveled with GPS Travel Survey Data
• Factors Associated with High School Students’ Delayed Acquisition of a Driver’s License: Insights from Three Northern California Schools
• Joint Econometric Analysis of Temporal and Spatial Flexibility of Activities, Vehicle Type Choice, and Primary Driver Selection
• Joint Modeling of Household Vehicle and Activity Allocation: Statistical Analysis and Discrete Choice Modeling Approach
• Modeling Intertrip Time Intervals Between Individuals’ Overnight Long-Distance Trips
• Modeling Social Network Influence on Joint Trip Frequency for Regular Activity Travel Decisions
• Reliability in the German Value of Time Study
• Value of Schedule Delays by Time of Day: Evidence from Usage Data from High-Occupancy Toll Lanes on State Road 167
• Walking Behavior: The Role of Childhood Travel Experience