Human Understanding in Mobility and Automation in Networks (HUMAN) Lab at UCLA investigates the principles of network systems that interact with human and emergent decision-making challenges. I envision an autonomous decision-making that incorporates multiple transportation modes and reconciles conflicting interests of stakeholders. By implementing a coordinated mobility system, we can achieve better societal goals, such as enhanced accessibility and equity, reduced congestion and emissions, and improved user satisfaction. Methodologically, I am interested in network optimization, econometric choice modeling, deep/reinforcement learning, and game theory. 

As outlined in my talk, I propose analytical approaches that integrate econometric behavioral models into the core network optimization problem. Three different applications are considered, spanning multiple time horizons: 1) real-time operation of behavior-aware high-capacity ride-pooling services, 2) strategic decision-making for a transportation network company and policy evaluation, and 3) long-term infrastructure planning and prediction of travel demand patterns.