A sudden surge in demand in traffic networks disrupts the equilibrium conditions upon which these networks are planned and operated. Lack of understanding of the population's strategic choices under extreme demand may result in paradoxical outcomes, such as evacuations aiming to save lives instead resulting in mass casualties on the road or opening up of new roads increasing rather than decreasing travel time. This project will devise systems and procedures for managing the strategic choices of populations (e.g., whether to evacuate or shelter in place, which escape routes to take) and the actions of the authorities (e.g., which zones to evacuate and in which sequence, where to route the traffic, whether to close some roads or open extra lanes in a given direction). The tools resulting from this project will enable better response systems to assist local authorities in managing extreme demand, such as when entire counties have to be evacuated to protect the residents from a wildfire. The project will develop a modeling and simulation tool chain to predict traffic bottleneck locations and their severity together with expected travel times and delays, thus determining the spectrum of outcomes, identifying worst cases, and enabling the authorities to make informed decisions.

The technical approach is rooted in population games, which model the dynamics of strategic noncooperative interactions among large populations of agents competing for resources. The project, however, will depart from the equilibrium focus of the existing theory and will offer transient analysis tools that account for not only the strategy revisions of the agents, but also a host of cyber and physical dynamics, such as queueing dynamics in traffic flow, responsive signal control at intersections, information dissemination to agents, and evolution of hazards, such as fire propagation. The research tasks to enable the project's vision of a "cyber-physical population game theory" include characterizing transient behavior with system-theoretic methods, accounting for uncertainty in strategy revision models, extending the theory to a continuum of user preferences, rethinking the stochastic processes underlying the dynamical models, modifying the theory for short-term horizons for time-critical operations, learning dynamical models from data, and formulating extensive form games between a population and a single agent, motivated by the population response to evacuation orders. In addition, the project will identify control actions (such as responsive signal policies, road closures, disabling certain turns) to close the data-decision-action loop and steer the dynamics towards desirable outcomes and avoiding unsafe ones.