Abstract
Plug-in electric vehicles (PEVs) are among the most promising strategies for reducing transportation-related emissions and mitigating their impacts on both the environment and public health. Historically, PEV adoption has been slowed by three key barriers: range anxiety, limited charger availability, and high purchase costs. Recent advances — including improvements in battery technology, tax incentives, and subsidized charging programs — have begun to ease these challenges, leading to steadily increasing adoption rates. Planning for the mobility needs of PEVs is particularly important due to the vulnerability of the power grid to outages that can cascade drastically. Yet a common limitation in current PEV analyses is their narrow focus on mobility patterns, often restricted to estimating simple variables like arrival or departure times. Few studies have incorporated individual mobility needs at a metropolitan scale into planning for electricity demand management. To address this gap, this study simulated daily travel patterns for the entire Bay Area population using TimeGeo, a mobile phone-based urban mobility model. TimeGeo identifies home, work, and other activity locations for individuals based on the timing and frequency of their phone calls. These data can be used to simulate the travel behavior of PEV owners, combined with vehicle usage rates drawn from U.S. Census data and the California Plug-in Electric Vehicle Driver Survey.
