The adoption of plug-in electric vehicles (PEVs) is considered to be a potential solution to reduce transportation-related emissions. People’s vehicle choice and driving behavior will have important implications for the realized emissions reductions from PEVs. Therefore, PEV-related policy studies require good understanding of human behavior. Traditional approaches to analyze travel behavior are mostly to build analytic models based on assumptions because of the limited accuracy and information of data. With the development of sensor technology, there are more methods than ever to collect accurate and informative behavioral data, so the crucial consideration is how to creatively use these data to better understand people’s behavior. This dissertation proposed some data-driven approaches to simulate behavior and provided a discussion of the implications for three PEV-related topics.
The first study explored the potential of greenhouse gas (GHG) reductions that can be achieved with adoption of PEVs in California by simulating vehicles’ emissions based on tracing data. It was found that assigning the right model of PEVs to drivers can help to reduce annual GHG emissions by 65%, compared to everyone driving a Toyota Corolla.
The second study presented a tool to evaluate the spatial distribution of fast charging demand and to assess how much a charger in a certain location would be used based on travel diary. Scenario analysis illustrated that en-route fast charging demand will shift from primarily inside metro areas to long distance corridors outside metro areas as the battery size increases.
The third study estimated the value of Clean Air Vehicle (CAV) decals by simulating the frequency of PEV owners’ access to high occupancy vehicle/toll (HOV/T) lanes based on survey data. The results indicated that the CAV Decals Program is one of the most attractive incentive policies, but there is spatial heterogeneity of CAV decal value across different regions.