In this project, we aim to create high-fidelity models, validated with real-world data, of mixed- mode travel decisions and emerging mobility markets. A growing subset of travelers make decisions informed by apps that optimize (mixed-mode) routes based on user-defined preferences. Locally optimized solutions tend to cause inefficiencies that are exacerbated by risk-sensitivity (arising from endogenous and exogenous uncertainties) in travelers.
