Road traffic reservoir computing

TL;DR

This paper explores using road traffic dynamics as a physical reservoir computing system, demonstrating its feasibility through numerical experiments on traffic prediction tasks with a simple mathematical model.

Contribution

It introduces a novel approach of leveraging road traffic as a physical reservoir for computing, expanding reservoir computing applications to real-world traffic systems.

Findings

Feasibility confirmed through numerical simulations

Effective prediction of traffic flow using the proposed method

Potential for physical implementation of traffic-based reservoir computing

Abstract

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.