Decentralized AI Tackles Urban Traffic Congestion with Cardano Blockchain

A new research study proposes a decentralized system that combines artificial intelligence, Internet of Things sensors, reinforcement learning and the Cardano blockchain to manage traffic in smart cities. The framework, called DRLCB, is designed to predict congestion, detect incidents, respond to changing weather and identify cyberattacks against connected devices.

The researchers argue that this combination could help cities make faster, more secure and more explainable traffic-management decisions as urban road networks become increasingly dependent on connected cameras, sensors, vehicles and roadside infrastructure.

According to the study, the DRLCB framework uses a closed-loop perception-and-control architecture. Data are first converted into predictions about the traffic environment, which then become the state information used by reinforcement-learning agents. These agents select actions intended to reduce congestion and improve traffic flow.