Predicting Fuel Consumption and Emissions Using GPS-Based Machine Learning Models for Gasoline and Diesel Vehicles

Fahd Alazemi, Asmaa Alazmi (), Mubarak Alrumaidhi and Nick Molden
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Fahd Alazemi: Department of Safety, Kuwait Municipality, Kuwait City 12027, Kuwait
Asmaa Alazmi: Department of Construction Project, Ministry of Public Work, South Surra 12011, Kuwait
Mubarak Alrumaidhi: Civil Engineering Department, College of Technological Studies, Public Authority for Applied Education and Training, Shuwaikh 70654, Kuwait
Nick Molden: Emissions Analytics, C R Bates Industrial Estate, 2, Stokenchurch, High Wycombe HP14 3PD, UK

Sustainability, 2025, vol. 17, issue 6, 1-18

Abstract: The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation management and environmental sustainability. This study investigates the use of real-world driving data from gasoline and diesel vehicles to model fuel consumption and exhaust emissions ( C O 2 and N O x ). The models were developed using ensemble bagged and decision tree algorithms with inputs derived from both vehicle speed and GPS speed data. The results demonstrate high predictive accuracy, with the ensemble bagged model consistently outperforming the decision tree model across all datasets. Notably, GPS speed-based models showed comparable performance to vehicle speed-based models, indicating the feasibility of using GPS data for real-time predictions. Furthermore, the combined gasoline and diesel engine dataset improved the accuracy of C O 2 emission predictions, while the gasoline-only dataset yielded the highest accuracy for fuel consumption. These findings underscore the potential of integrating GPS-based machine learning models into Intelligent Transportation Systems (ITS) to enhance real-time monitoring and policymaking. Future research should explore the inclusion of heavy-duty vehicles, additional pollutants, and advanced modeling techniques to further improve predictive capabilities.

Keywords: fuel consumption; exhaust emissions; vehicle speed; GPS speed; gasoline vehicle; diesel vehicle (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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