Harvested reservoir computing from road traffic dynamics

Reservoir computing (RC) has gained attention as an efficient machine learning method for time series prediction because of its low computational costs and simple learning process. Herein, we propose the Harvested Reservoir Computing (HRC) framework which treats complex real-world dynamics as spontaneously emerging physical reservoirs. As an instance of HRC, we introduce Road Traffic Reservoir Computing (RTRC), whereby dynamical traffic flow patterns are harnessed as natural computational resources to predict future traffic states in experiments. Unlike conventional reservoir computing, this approach requires no explicit reservoir design, but instead "harvests" the intrinsic dynamics of traffic as a physical reservoir. Experiments using a scaled traffic model and numerical simulations on a grid road network demonstrate that the framework's prediction accuracy is highly dependent on traffic density. An optimal density range is identified within which prediction performance is maximized because of a tradeoff between nonlinearity and short-term memory. These findings highlight the potential of complex real-world dynamics as viable components within computational frameworks.