This Smart and Connected Communities project will develop a unified model-based and data-driven framework for modeling and controlling three core components of personal mobility in intelligent urban transportation: parking, ride-sharing services, and traffic flow on urban arterial roads. Data from recently installed technologies such as smart parking meters and probe-based sensors (e.g., wifi sensors) can be leveraged in, e.g., designing dynamic parking pricing and to help guide drivers to available parking spaces using smart-device apps. Such data will be available via collaborations with the Departments of Transportation (DOT) in Los Angeles and Seattle. Furthermore, we will engage several commercial districts in Los Angeles and Seattle, which are significant stakeholders in new parking systems and serve very diverse communities. Coupling new technologies with methodological innovations, this project aims to reduce negative impacts of traffic congestion on the environment, quality of life, productivity, and physical and financial well-being. In-situ experimental trials will allow for the impact of policy changes suggested by the research to be assessed. Collaborations with municipal partners will also be leveraged to create opportunities for workforce development that avail students to domain experts and aid in research-capacity building.

In pursuit of utilizing new data-driven technologies, this project will develop stochastic decision models for parking, ride-sharing, and traffic flow in urban environments. The team will explore: i) Stochastic Game-theoretic(SG) and Markov Decision Process (MDP)-based models to capture vehicle behavior as well as the traffic dynamics as an ensemble behavior; ii) Synthesis of correct-by-design decision algorithms using formal methods and convex optimization; iii) Integration and testing of the proposed algorithms in a relevant urban environment. Developed models will capture individuals' behaviors as well as overall urban traffic dynamics and will be validated against real data. This project will explore new ways to merge formal methods with optimization-based MDP and SG synthesis for decision-making policies that incorporate unique set of specifications at an individual and ensemble level. Formal methods techniques allow for qualitative specifications (e.g., access, fairness, etc.) to be encoded into objectives for which the proposed correct-by-design innovations will lead to provable guarantees needed to ensure sustainable policy development. Finally, with collaborators at the Seattle DOT, experimental trials that go beyond data collection will be conducted in selected Seattle commercial districts to test and validate designed control and parking policy changes.