In this capstone project, I used agent-based modeling in Python to simulate human patterns of movement within a transportation system, focusing on mode choice between private vehicles and public transit. I added a coefficient of bias to investigate the emergent behavior of the system when transit mode choices were influenced by the proportions of other riders, finding that interpersonal bias in this model can exacerbate exisiting disparities between groups of travelers.
The full report on this project can be read here.