Significant research has been conducted on how environmental attributes influence people’s decisions to walk. In much of this
research, however, environmental attributes are averaged for neighborhoods or census geographies for sampled populations. Moreover, the
effect of an agent’s walking choices on other actors is not adequately represented by either objective or perceived measures in the literature.
Macro-level patterns of walkability arise from interactions across actors and urban environments. The agent-based approach allows for
modeling individual uses of the environment by treating the populations as objects that can interact with the environment and other people.
This study builds on previous research on pedestrian movement and geographic information system (GIS) measures of the built environment
using the agent-based approach to explore the dynamics of the built environment and people’s decision-making processes concerning walking. The results show that models that take individual perspective into account and include social interaction can better capture characteristics
of the built and social environment that influence people’s walking choices. This method lays out a new framework for assessing macro-level
patterns of walkability across a city using micro-level data.