Leveraging Geospatial Analysis and Machine Learning for Optimal Green Vehicle Assignment

This paper presents a data-driven approach that uses geospatial analysis and machine learning to assign the right vehicles to the right regions, reducing fuel consumption, costs, and transportation-related CO 2 emissions. Applied to a large retail fleet, the methodology achieved an 8% improvement in fuel efficiency and emissions, while also identifying how older vehicles can be strategically deployed to enhance their performance.