The process of collecting traffic data has long been considered as one of the important components of a city, because traffic data are used not only for traveler information systems but also as an input to support traffic control on urban streets. In the foreseeable future, we envision that in a smart and connected city the current roadway-based infrastructure for traffic data collection, such as loops, microwave sensors, or laser detectors, will be replaced by vehicle-based data. Such a paradigm shift would result in significant cost savings as well as the generation of a new form of traffic data that is more powerful than the existing one. It will make our traffic management systems more efficient and our cities more livable. The aim of this EArly-concept Grant for Exploratory Research (EAGER) project is to expedite the process that would lead to such a paradigm shift. The project envisions a system in which drivers interact with traffic control by providing their time-dependent speed and positioning information to the system as "votes" in exchange for possible priority service. Involving system users in system control through "voting" will engender interest in cutting-edge systems engineering techniques, especially for K-12 students. The research will provide significant advances in understanding opportunities for traffic management systems in smart and connected communities, where interaction between traffic control and individual drivers is practicable. Such interaction, a feature currently available only to special-class vehicles, would fundamentally change conventional theory for algorithm development. It could impact user-equilibrium and system optimum theories that govern route choice behavior of drivers and optimal system control at the network level. This project explores a new class of algorithms that directly utilize spatial data, as opposed to spot data used currently, to support traffic control algorithms. The new algorithms will differ from existing ones in that "qualitative information" about traffic states will be employed as an input to traffic control (e.g. "a very long queue" instead of the actual queue length). Such information can be readily inferred from speed and positioning data of individual vehicles. The interaction between individual vehicles and system control will provide system users with opportunities to inform the system of the value of their trips. Such capabilities will engender a need to rethink user equilibrium and other models of travel behavior.