Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Science, Engineering and Technology Print ISSN: 2395-1990 | Online ISSN : 2394-4099 (www.ijsrset.com) doi : https://doi.org/10.32628/IJSRSET 418 Traffic Prediction for Intelligent Transportation Systems Using Machine Learning Mrs. P. Manjula 1 , Balusu Sai Laxmi Niveditha 2 , Jarpula Gopi 2 , Kammapati Srikanth 2 , Marem Vamshi 2 1 Assistant Professor, 2 B.Tech. Scholar Department of Computer Science and Engineering, J.B. Institute of Engineering and Technology, Moinabad Mandal, Hyderabad, Telangana, India A R T I C L E I N F O A B S T R A C T Article History: Accepted: 01 April 2023 Published: 12 April 2023 The goal of this project is to provide a platform for forecasting accurate and timely traffic data. Traffic conditions include things that can affect road traffic speeds, such as: B. Traffic lights, accidents, protests and even road repairs that can cause traffic jams. Motorists or drivers should make informed decisions when they have very accurate prior knowledge of all of the above approximations and more real-world conditions that may affect traffic. I can. can be lowered. It is also useful for the development of self-driving cars. Transportation data has increased dramatically over the past decades and is evolving towards the concept of transportation big data. Current traffic prediction approaches use specific traffic prediction models that are still inadequate to handle real-world situations. Therefore, we tackled the problem of traffic prediction using traffic data and models. Due to the vast amount of data available in transportation systems, it is difficult to accurately predict traffic flows. In this study, we wanted to use machine learning, genetics, soft computing, and deep learning techniques to evaluate vast amounts of data in transportation systems while greatly reducing complexity. In addition, it uses image processing algorithms to recognize traffic signs and ultimately help train self-driving cars properly. Keywords: Traffic Environment, Deep Learning, Machine Learning, Genetic Algorithm, Big Data, Image Processing. Publication Issue Volume 10, Issue 2 March-April-2023 Page Number 418-422 I. INTRODUCTION Various economic sectors, authorities and individual passengers need accurate and relevant information about traffic flows. It helps passengers and drivers make better travel decisions to minimize traffic congestion, increase efficiency in transportation operations and reduce carbon footprint. Intelligent transportation systems (ITS) are being developed and deployed to improve the accuracy of traffic flow predictions. It is considered a key factor in the success of modern public transport systems, passenger information systems and traffic control systems. [1]. Both real-time traffic data and historical information collected from various sensor sources such as inductive loops, radar, cameras, mobile global positioning systems, crowdsourcing, and social media are required to determine traffic flow. Traffic data is growing