Traffic Prediction for Intelligent Transportation System Using Machine Learning

• Published in: International journal for research in applied science and engineering technology 2022-08, Vol.10 (8), p.922-925
• Main Authors: V. M., Fareeduddeen, Sreerambabu, Dr. J., Riyaz, M. Mohammed
• Format: Article
• Language: English
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2022.46306

This Document aims to develop a tool for predicting correct and timely traffic flow info. Traffic surroundings involves everything which will have an effect on the traffic flowing on the road, whether or not it’s traffic signals, accidents, rallies, even repairing of roads which will cause a jam. If we've got previous info that is extremely close to approximate regarding all the higher than and many more lifestyle things which may have an effect on traffic then, a driver or rider will create an knowing decision. Also, it helps within the way forward for autonomous vehicles. within the current decades, traffic information are generating exponentially, and that we have stirred towards the large information ideas for transportation. Available prediction ways for traffic flow use some traffic prediction models and are still dissatisfactory to handle real-world applications. This reality impressed us to figure on the traffic flow forecast problem build upon the traffic information and models. It is cumbersome to forecast the traffic flow accurately as a result of the info on the market for the transportation is insanely vast. during this work, we tend to planned to use machine learning, genetic, soft computing, and deep learning algorithms to analyse the big-data for the transportation with much-reduced quality. Also, Image process algorithms are concerned in traffic sign recognition, that eventually helps for the correct training of autonomous vehicles