Computation harvesting in road traffic dynamics

TL;DR

This paper introduces a novel computation harvesting framework for road traffic prediction that leverages IoT data and real-world phenomena, reducing computational costs while maintaining accuracy and resilience.

Contribution

It proposes a new methodology inspired by natural systems that utilizes IoT data for large-scale, resilient, and low-cost traffic prediction without heavy reliance on electronic computation.

Findings

Computation harvesting is resilient to sensor failure and traffic variability.

The method enables real-time traffic prediction with low computational resources.

Compared to traditional methods, it achieves similar accuracy with fewer resources.

Abstract

Owing to recent advances in artificial intelligence and internet of things (IoT) technologies, collected big data facilitates high computational performance, while its computational resources and energy cost are large. Moreover, data are often collected but not used. To solve these problems, we propose a framework for a computational model that follows a natural computational system, such as the human brain, and does not rely heavily on electronic computers. In particular, we propose a methodology based on the concept of `computation harvesting', which uses IoT data collected from rich sensors and leaves most of the computational processes to real-world phenomena as collected data. This aspect assumes that large-scale computations can be fast and resilient. Herein, we perform prediction tasks using real-world road traffic data to show the feasibility of computation harvesting. First, we show that the substantial computation in traffic flow is resilient against sensor failure and real-time traffic changes due to several combinations of harvesting from spatiotemporal dynamics to synthesize specific patterns. Next, we show the practicality of this method as a real-time prediction because of its low computational cost. Finally, we show that, compared to conventional methods, our method requires lower resources while providing a comparable performance.