A Multiple Linear Regression (MLR) Model for the Application of Electrical Vehicles in the United States

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This study focuses on the assessment of the factors affecting the adaptation of Hybrid/Electric Vehicles. The problem that arises when using conventional vehicles has some negative impacts on the environment (Greenhouse Gas emission), society (health issues), and economics (energy demand). There is a need to mitigate these effects by inducing a transportation mode with a fuel source of electricity and progressively reducing the use of gasoline. To find the socioeconomic and environmental impacts of the application of Hybrid/Electric Vehicles, the current research aims to explore potential factors that can be attributed to purchasing H/EVs to estimate their penetration in the U.S. Several multiple linear regression (MLR) models were applied to find the significant factors that impact the use of several types of Hybrid/Electric vehicles compared to conventional ones. The types of Hybrid/Electric Vehicles assessed are Plug-in Hybrid Vehicles (PHEV), Electric Vehicles (EV), Hybrid Vehicles (HEV). The models use data from the National Household Travel Survey website. R Studio software is applied to conduct statistical analysis. The results identify that the variables that have statistical significance are Fuel Expenditures and Household Income. The factors that impact the use of conventional vehicles compared to hybrid ones are MSA, Model Toyota Vehicles, Vehicles Driven in the Weekdays, Weekends, Zero Workers and One to Three Workers per Household. Furthermore, people with PHEVs, EVs, and HEVs tend to have more fuel expenditures and higher household income than conventional vehicles. Therefore, it is determined that “adults with and without children” are not significant among the models.


© Canadian Society for Civil Engineering 2022

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Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021