Road traffic reservoir computing
This work addresses the need for low-cost, physically implementable computing systems, but it is incremental as it applies an existing reservoir computing concept to a new domain (road traffic).
The authors tackled the problem of applying reservoir computing to real-world systems by proposing a method that uses road traffic dynamics as a reservoir, and they numerically confirmed its feasibility with prediction tasks using a traffic flow model.
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.