Trip Based Fuel Consumption Prediction Using Machine Learning

Trip-based fuel consumption prediction using machine learning is a critical advancement in optimizing vehicle performance and promoting sustainable transportation. This study explores the application of various machine learning algorithms to predict fuel consumption based on trip characteristics, driver behavior, and environmental factors. By utilizing telematics and GPS data, the model captures key variables such as distance, trip duration, acceleration patterns, and traffic conditions. Through rigorous data preprocessing and feature engineering, the model is trained and validated using historical trip data, enabling it to accurately forecast fuel consumption for different scenarios. This research highlights the importance of data-driven insights in fleet management, driver awareness, and policy formulation, emphasizing the role of technology in shaping the future of transportation. The proposed approach offers a framework for continuous improvement and real-time application, paving the way for smarter, more efficient travel solutions.