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The increase in climate-related disasters such as wildfires, hurricanes, tornadoes, and floods, has given us many examples that the way traffic networks operate in real life can be difficult to predict and manage. A sudden surge in demand within a traffic network disrupts the equilibrium conditions upon which it was planned and operates. A lack of understanding drivers’ strategic choices in extreme circumstances often results in paradoxical outcomes. Evacuations meant to save lives might result in mass casualties on the road. Building and opening new roads sometimes increases, rather than decreases travel time.
New systems and procedures are needed for managing the strategic choices available to populations and what actions authorities should take. Should people evacuate or shelter in place? What escape routes should they use? Which zones do authorities need to evacuate and in what sequence? Where should authorities route traffic? Should some roads be closed? Should extra lanes be opened in a given direction?
Professor Nuno Martins (ECE/ISR) is the principal investigator for “Population Games for Cyber-Physical Systems: New Theory with Tools for Transportation Management under Extreme Demand,” a three-year, $390K NSF collaborative research cyber-physical systems grant that will build tools for these situations. These tools will enable better response systems for assisting local authorities in managing extreme demand. Martins and his team will develop a modeling and simulation tool chain that can predict traffic bottleneck locations and their severity together with expected travel times and delays. This tool chain will help to determine the spectrum of outcomes, identify worst cases, and enable the authorities to make informed decisions.
Martins’ approach is rooted in “population games,” which model the dynamics of strategic noncooperative interactions among large populations of agents competing for resources. The project departs from the equilibrium focus of existing theory to 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 project also 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 away from unsafe ones.
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January 3, 2022
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