Cellular automata (CA) models are used for simulating land use change for more than two
decades. These models have a simple structure based on a cellular partition of space, considering a finite
set of cell states (or land uses) and their interaction within a given neighborhood area, changing
throughout time under a set of transition rules. Transition rules are based on more or less sophisticated
measures of state transition: they can be more complex rules that try to incorporate the different drivers of
land use change or they can be purely probabilistic rules that take into account only the states of
neighboring cells. This last approach is often based on a measure of a transition potential that establishes
a rank for state transition for every cell. There are many drivers of land use change and accessibility is
acknowledged as being one of the most important ones. At the same time, transportation systems (thus
accessibility) are also influenced by land use change. This suggests that CA models are potentially good
tools to simulate these phenomena by considering the cross-interdependences between both. In this
presentation, we make a reflection on how accessibility can be measured, incorporated, and used to
improve CA transition rules based on transition potentials towards more representative models of land
use change. We address not only modeling requirements but also the potential of using CA models to
evaluate both the impacts of transportation policies in land use change, and vice versa.