  The amount of sun cast on roads and parking lots determines the charging
opportunities for solar vehicles and impacts the efficiency of conventional
vehicles. Estimates of solar energy potential on urban surfaces to assess
parking and driving conditions need to account for the shadows cast by
surrounding trees and buildings. However, though existing GIS tools can
calculate solar potential on surfaces that have buildings and trees, these
tools do not estimate the conditions beneath trees and do not consider the
seasonal changes in deciduous trees. We introduce a new approach to address
these factors using pixel substitution and a light penetration factor. In this
paper, we describe how to integrate these techniques into a workflow for
computing solar potential estimates for parking and driving conditions. We
demonstrate the methodology in an urban setting in North Carolina that includes
a mixture of urban structures and trees. We provide code samples so that this
workflow is easily repeatable. The solar maps produced with our method are a
useful resource for planning solar vehicle parking and routing, and identifying
shaded conditions for conventional vehicles.
