  The rising adoption of plug-in electric vehicles (PEVs) leads to the
alignment of their electricity and their mobility demands. Therefore,
transportation and power infrastructures are becoming increasingly
interdependent. In this work, we uncover patterns of PEV mobility by
integrating for the first time two unique data sets: (i) mobile phone activity
of 1.39 million Bay Area residents and (ii) charging activity of PEVs in
580,000 sessions obtained in the same region. We present a method to estimate
individual mobility of PEV drivers at fine temporal and spatial resolution
integrating survey data with mobile phone data and income information obtained
from census. Thereupon, we recommend changes in PEVs charging times of
commuters at their work stations that take into account individual travel needs
and shave the pronounced peak in power demand. Informed by the tariff of
electricity, we calculate the12 monetary gains to incentivize the adoption of
the recommendations. These results open avenues for planning for the future of
coupled transportation and electricity needs using personalized data.
