  Reservoir computing derived from recurrent neural networks is more applicable
to real world systems than deep learning because of its low computational cost
and potential for physical implementation. Specifically, physical reservoir
computing, which replaces the dynamics of reservoir units with physical
phenomena, has recently received considerable attention. In this study, we
propose a method of exploiting the dynamics of road traffic as a reservoir, and
numerically confirm its feasibility by applying several prediction tasks based
on a simple mathematical model of the traffic flow.
