ARCTO: AIoT System for Reducing Carbon Emissions Using Traffic Optimization

In the status quo, traffic control systems operate on predetermined patterns and instructions devised from past data. While this method functions effectively for traffic under normal conditions, it becomes heavily congested and inefficient during instances of high traffic, which leads to a multitude of temporal, economic, health, and environmental harms. However, by combining traditional traffic controllers with modern technologies such as Internet of Things devices and computer vision, these issues can be effectively addressed. This research presents a novel, affordable Artificial Intelligence of Things traffic control system that enables accurate real-time vehicle detection and signal control. This work is split into two sections: (1) an AIoT physical system that can scan traffic conditions in real-time and (2) a realistic traffic simulator with a custom optimization algorithm. Combined, this research provides up to 35% greater throughput, 50% reduced waiting time, and 50% reduction in greenhouse gas emission reductions in comparison to nonoptimized algorithms used in the status quo. The implementation of this work leads to various temporal, economic, environmental, and health benefits; in addition to providing comparable emission reduction as the complete replacement of all internal combustion engine vehicles with battery electric vehicles, while significantly reducing vehicle travel time, systems installation time, and cost.