Emerging Markets and Myopic Decision-Making in Multi-Modal Transportation Systems- Models and Validation

This project aims 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 en-dogenous and exogenous uncertainties) in travelers. Traditional rational, utility maximization models tend not to capture these effects, particularly in short-horizon decisions that leave little time for cogitation and points of reference play a primary role in choice. The project aims to (a) learn models of trav-eler decision-making that account for risk-sensitivity and (b) develop models of market structures (e.g., ride-sharing platforms) that capture traveler valuations of mobility modes.

The proposed research extends rational models of travel decisions by lever-aging prospect theory for capturing risk-sensitivity and bounded rationality for capturing information asymmetries and myopia. These concepts will be in-tegrated into an inverse reinforcement learning framework for which we seek online algorithms in support of control/incentive design. Moreover, we will de-velop technical approaches to modeling mobility market structures that include new queuing game-theoretic models that capture risk-sensitivity. The proposed work will benefit from multi-modal transit data available via municipal and industry partners for testing and validation within a well-developed testbed.

This project will highlight areas where municipalities can adjust their man-agement strategies to supplement the sharing economy in addressing the service gap in an equitable way. Through collaborations with the Seattle Department of Transportation and industry partners Swiftly and IDAX, there is potential to translate results to practice. Graduate and undergraduate students will be heavily involved in the project and will have ample opportunity to engage with the community through our municipal partners as well as be exposed to the jobs of tomorrow's smart cities.