Oak Ridge National Laboratory researchers are developing a computational framework for connected-vehicle technologies that facilitates vehicle-to-vehicle communication, as well as communication between vehicles and traffic control systems.
The system will help vehicles exchange information such as location, speed, and destination, to create individualized instructions for drivers. "By telling drivers the optimal speed, the best lane to drive in, or the best route to take, we can eliminate stop-and-go driving and improve safety," says Oak Ridge Urban Dynamics Institute deputy director Andreas Malikopoulos.
The system relies on decentralized control algorithms that govern how vehicles communicate locally in order to optimize traffic flow across a city. The decentralized control algorithms are effective because all of the vehicles in a city cannot communicate information to a central control center due to the massive amount of data that would be involved.
The project's first phase will validate the framework through simulation, and the second phase will connect the team's communication framework with a transportation analysis simulation system that uses data analytics to simulate traffic conditions and predict congestion. In addition, as part of the second phase, researchers will begin exploring questions related to cybersecurity and possible incentives for drivers to follow connected vehicle instructions.
From Oak Ridge National Laboratory
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