Adoption and Diffusion of Electric Vehicles in Maryland
Driven by environmental awareness and new regulations for fuel efficiency, electric vehicles (EVs) have significantly evolved in the last decade. Although EV penetration has been slow so far, substantial increases in sales have been observed. This is particularly true in the U.S., where cumulative sales reached 1.44 million units sold in January 2019.
Therefore, reliable predictions are needed as a basis for strategic decisions for both private companies and public policies in favor of EVs. Forecasting the demand for EVs is a difficult task, and the predictions published so far either haven fallen short or have been too optimistic.
This research project proposes an innovative approach that is mainly based on three concepts:
• Substitution: Which may happen by replacing an Internal Combustion Vehicle (ICV) - gasoline or any other engine - with an EV.
• Diffusion: The approach is based on the classic Bass models and its extensions for which new technology is first adopted by a subgroup of innovators, who are followed by imitators.
• Dynamics: Diffusion not only depends on the context at a particular moment
but also on what occurred in the previous periods.
The models will be developed using data collected in the State of Maryland from a diverse population.
Universities and Sponsoring Organizations Involved
University of Maryland; U.S. DOT Office of the Secretary/Research
Dr. Cinzia Cirillo, University of Maryland, firstname.lastname@example.org
Funding Sources and Amounts
U.S. DOT $100,000; University of Maryland, $50,000
Expected Completion Date
Expected Research Outcomes
The results from this project will help scholars and decision makers understand the potential of new vehicle technology and guide data driven policies.
Expected Equity Impacts and Benefits of Implementation
The analysis conducted for different segments of the populations will shed light on the factors hindering low- and medium-income population access to EV technology.
Electric Vehicles, Social Network, Diffusion Models, Stated Preference Survey