Machine Learning in Transportation

Abstract

The number of vehicles and people on the planet is growing, and this has made traffic management more complex and requires accuracy and efficiency that go beyond human intervention. This work explores the use of machine learning (ML) algorithms in intelligent transportation systems, with a focus on traffic control. It looks at how ML might help with issues caused by increasing traffic, such as accidents and congestion. In an effort to lessen traffic- related problems, the suggested ML-based Traffic Management System (TMS) keeps an eye on cars, adjusts traffic lights, finds congestion, and provides other routes. In order to construct an intelligent transport system that uses machine learning (ML) algorithms, stimulate efficient traffic data management, and improve contemporary traffic management systems, the article outlines the principles of operation, obstacles encountered, and suggestions offered.