JARVITS: A Novel Deep Learning Io T Traffic Control System for Real-time Detection and Signal Optimization

Overview

In the status quo, traffic signal 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 rush hour. Furthermore, the constant presence of unexpected emergencies renders pre-determined systems ineffectual. By combining traditional traffic controllers with modern technologies like Internet of Things (Io T) devices and computer vision, traffic control systems can be greatly improved. Yet there are currently no systems that can affordably fulfill this task. By optimizing traffic signal duration, this allows for both a reduction in delay time for vehicles and a reduction of greenhouse gases emitted. Considering the Intergovernmental Panel on Climate Change’s August 2021 report on the current dramatically worsening state of the climate crisis, there is a compelling need for such a traffic control system to optimize throughput and thus greatly reduce vehicle’s greenhouse gas emissions. This research presents a novel deep learning traffic control system, called JARVITS (Just A Rather Very Intelligent Traffic System) that can be used for accurate real-time vehicle detection and signal control. Compared to previous methods, JARVITS offers a complete solution, with a physical vehicle detection algorithm and traffic signal optimizer. This study can largely be divided into two subsections: (1) the Io Ttraffic control system and (2) traffic control optimization. Lastly, a realistic virtual simulation created using Pygame is used to model traffic conditions and demonstrate that this research effectively improves traffic flow for an intersection.