What if traffic could compute? This may sound strange, but researchers at Tohoku University's WPI-AIMR have unveiled a bold new idea: using road traffic itself as a computer.

Researchers at the Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, have proposed a novel artificial intelligence (AI) framework that treats road traffic itself as a computing resource. The approach, called Harvested Reservoir Computing (HRC), opens up a path toward energy-efficient AI systems that reuse the dynamics already existing in our environment instead of relying solely on power-hungry dedicated hardware.

Their AI framework, called Harvested Reservoir Computing, taps into the natural dynamics of traffic flow to enable energy-efficient AI - turning everyday motion into computational power without energy-hungry hardware.

In recent years, machine learning and deep learning have been widely applied to traffic forecasting, demand prediction, and various forms of social infrastructure management. However, these approaches typically require massive computational power and large energy consumption. Reservoir computing (RC), and its extension to real-world physical systems - physical reservoir computing (PRC) - have attracted attention as promising alternatives.

Building on this concept, Professor Hiroyasu Ando and colleagues propose HRC, a framework that "harvests" complex physical dynamics present in the natural and social environment and uses them directly for computation. As a proof of concept, the team systematically evaluated the performance of Road Traffic Reservoir Computing (RTRC), which exploits traffic flow on road networks as a computational reservoir.

Combining controlled traffic experiments using 1/27-scale autonomous miniature cars with numerical simulations of grid-shaped urban road networks, the researchers discovered a striking feature: prediction accuracy is not highest under free-flow or heavily congested conditions. Instead, it peaks just before congestion begins, at a critical, medium-density state where traffic dynamics are most diverse and informative. In this regime, the traffic system naturally processes incoming information, allowing accurate forecasts of future traffic states with minimal computational overhead.

Importantly, this method requires no new specialized hardware. By reusing existing traffic sensors and observational data, it has the potential to support high-precision traffic prediction and adaptive signal control while significantly reducing energy consumption compared with conventional AI approaches.

The study suggests that social infrastructure such as roads can be reinterpreted as "large-scale, continuously operating computers." Beyond traffic management, the concept may enable future applications in smart mobility, urban planning, and energy management, where environmental dynamics are leveraged as part of the computational process.

"These results demonstrate that computation does not have to be confined to silicon chips," says Ando. "By recognizing and harnessing the rich dynamics already present in our environment, we may build AI systems that are both powerful and sustainable."

The research also contributes a new perspective to the development of AI foundation technologies: rather than endlessly scaling up hardware, it may be possible to scale intelligence by integrating physical systems and data in innovative ways.

The findings were published online in Scientific Reports on November 27, 2025.