Effective Management of Delays at Road Intersections Using Smart Traffic Light System

Rapid industrialization coupled with increased human population in urban regions has led to a rise in vehicle usage. The demand for space (road) by motorists for transportation has risen. Unfortunately, infrastructural development has not been at par with vehicular growth thus resulting in congestion along major roads. Traffic lights have been used for years to manage traffic flow. While they serve a good purpose, their underlining principle of operation is to a significant degree inefficient as traffic congestion still prevails and remains a major concern till date. This study seeks to tackle this challenge by proffering a Smart Traffic Management System (STMS) based on image detection. The system incorporates cameras which dynamically capture road situation as images, run them through an image processing algorithm to obtain traffic density then automatically adjust the service times at intersections. To measure the effectiveness of the approach, mathematical models were formulated, analytical comparison as well as experimental simulations were done. Results show that SMTS out-performed the Round-Robin algorithm used by traditional traffic lights, by reducing service interruptions, cutting delay times by at least 50%, while remaining equally fair to all roads at the intersection. This system and its constituent components fall under the Edge computing paradigm as real time data capture, analysis and decisions are made by an embedded computer.