The development of a smarter mode of transportation using ai to reduce travel and waiting times

Abstract

Smarter mode of transportation (SMT) rely heavily on real-time traffic management. Safety on the roads is only one of many benefits brought forth by advancements in dynamic traffic management systems for congested metropolitan areas. In this study, we present the design and implementation of a Bayesian Belief Networks (BBN) traffic control system that is both flexible and reliable. The use of knowledge-based systems as a framework for making decisions in real-time has gained widespread acceptance. Since traditional dynamic controllers relied on sensors with their own set of drawbacks, we may employ vision sensors (such as cameras) to get around these problems. Computing based on images and videos is very useful for gauging traffic volumes. The present traffic management system at the road junction was found wanting, thus a new system was developed and put into place to alleviate the congestion. Lab VIEW and MATLAB are used to measure how well the suggested framework works. Extensive simulations utilizing the suggested method show that it reduces waiting time and speeds up movement on average compared to controllers employing traditional sensors.