Machine Learning-Driven Optimization of Driving Patterns Through for Fuel Efficiency

Reducing fuel consumption and pollution requires optimising driving practices to boost fuel economy. This article explores how driving behaviours can be optimised for increased fuel efficiency using machine learning techniques. We employ supervised learning, reinforcement learning, and clustering to assess behaviours such as braking, acceleration, and route selection by examining vehicle sensor data. While real-time decision-making tools respond to shifting traffic conditions, our framework offers fleet management systems and individual drivers tailored advise on how to reduce fuel use. By analysing driving behaviours including speed maintenance, braking, and acceleration. We develop prediction models. To identify fuel driving techniques, machine learning techniques such as clustering, supervised learning and reinforcement learning are employed. In response to shifting traffic conditions, the proposed techniques modify driving advice.