Optimize Fleet Operations using Samsara Telematics and Machine Learning

Authors: Vamshi Krishna Malthummeda

DOI: https://doi.org/10.37082/IJIRMPS.v13.i5.232757

Short DOI: https://doi.org/g9657g

Country: United States

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Abstract: Fleet operations optimization is very crucial for logistics, transportation and supply chain management companies. It helps with reducing costs, increasing efficiency, enhancing safety, and improving customer satisfaction. This paper proposes Predictive Analytics framework which integrates Samsara telematics ecosystem (which collects real-time data on GPS location, speed, engine status, fault codes, and other sensor data) with Databricks ML pipelines to enable predictive maintenance, route optimization, driver performance analysis, and safety enhancements. The implementation of Predictive Analytics Framework (PAF) resulted in significant reduction of fuel consumption, emissions, accident likelihood, vehicle downtime and insurance costs. The implementation of PAF had significant improvement in customer satisfaction due to on-time delivery. Predictive analytics framework helps organizations by predicting the downtime/failure of the vehicles way ahead and reducing the emergency repair costs, extending the vehicle life span with efficient vehicle usage, improving the driver safety risk scoring and provides AI-powered route planning which minimizes fuel consumption and mileage[

Keywords: Machine Learning, Databricks, Predictive Maintenance, Samsara, Telematics, REST API, Feature Engineering

Paper Id: 232757

Published On: 2025-10-17

Published In: Volume 13, Issue 5, September-October 2025

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