Applied Mathematics Seminar
- Thursday, October 22, 2015 from 3:00pm to 4:00pm
- Wilson Hall - view map
Yiyi Wang from Department of Civil Engineering, MSU, will present an Applied Mathematics Seminar on October 22nd, 3:10-4:00 in Wilson Hall 1-144. She will talk on "Hybrid Electric Vehicle Ownership And Fuel Economy Across Texas: Application Of Spatial Models."
Abstract: Policymakers and automobile industries are quite interested in understanding the factors affecting adoption rates of fuel efficient vehicles and hybrid electric vehicles (HEVs). In an effort to answer these potential policy questions, this study investigates the demographic characteristics such as age, gender, race, education, household size, and income, affecting the propensities of the individuals (at census tract level) towards buying vehicles with different fuel economy levels or HEVs across four highly populous counties in Texas (Bexar, Dallas, Harris, and Travis counties) of Texas. Accounting for the spatial autocorrelation, and local (aspatial) and spatially-lagged cross-response correlation, the census tract level adoption rates of HEVs and vehicles with different fuel economy levels are estimated by bivariate (response levels: HEV and non-HEV counts) and trivariate (response levels: fuel efficient, regular and fuel inefficient vehicle counts) Poisson-lognormal conditional autoregressive models. Response variables are extracted from vehicle registration data (for the year 2010) maintained by the Texas Department of Motor Vehicles, and demographic characteristics are obtained from the U.S. Census 2010 database and American Community Survey estimates. The high spatial autocorrelations and local cross-response correlations as well as higher incidence of more educated males and higher- income, but smaller households in owning HEVs and other fuel-efficient vehicles are found to be consistent in both models and across all counties. Considering a hypothesis that the early adopters of HEVs might have high inclination towards purchasing plug-in HEVs, the findings of study may also be valuable in spatial planning of charging infrastructure for plug-in HEVs.