Machine learning vehicle fuel efficiency prediction
Abstract To address the challenges associated with fuel consumption in vehicles with low fuel efficiency, several factors must be recognized. Identifying the key factors of fuel efficiency prediction is crucial for making accurate decisions. Therefore, we propose a comprehensive framework that uses...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96999-0 |
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| author | So-rin Yoo Jae-woo Shin Seoung-Ho Choi |
| author_facet | So-rin Yoo Jae-woo Shin Seoung-Ho Choi |
| author_sort | So-rin Yoo |
| collection | DOAJ |
| description | Abstract To address the challenges associated with fuel consumption in vehicles with low fuel efficiency, several factors must be recognized. Identifying the key factors of fuel efficiency prediction is crucial for making accurate decisions. Therefore, we propose a comprehensive framework that uses machine learning to predict fuel efficiency by integrating various vehicle information. The proposed method comprises a predictive model and analysis framework utilizing key vehicle attributes, such as fuel type, engine displacement, and vehicle grade, to enhance prediction accuracy. We conducted a comparative study using six machine-learning models. To evaluate the machine learning model, MSE (Mean Square Error), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R-squared ( $$R^2$$ Score) were used. We experimented with SHAP(Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and odds ratio analysis to evaluate the impact of various factors on fuel efficiency. We confirmed that the proposed method can predict fuel efficiency. Extra Trees Regressor and Random Forest Regressor demonstrated high prediction accuracy, particularly excelling in capturing nonlinear relationships. We also underscore the importance of identifying markers to support decision-making, offering critical insights into the key factors impacting fuel efficiency predictions. |
| format | Article |
| id | doaj-art-7c1ad49db840465aa74bbe4e9a5b036c |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7c1ad49db840465aa74bbe4e9a5b036c2025-08-20T02:55:35ZengNature PortfolioScientific Reports2045-23222025-04-0115112010.1038/s41598-025-96999-0Machine learning vehicle fuel efficiency predictionSo-rin Yoo0Jae-woo Shin1Seoung-Ho Choi2Department of AI Application, Hansung UniversityDepartment of IT Business Administration, Hanshin UniversityCollege of Liberal Arts, Faculty of Basic Liberal Art, Hansung UniversityAbstract To address the challenges associated with fuel consumption in vehicles with low fuel efficiency, several factors must be recognized. Identifying the key factors of fuel efficiency prediction is crucial for making accurate decisions. Therefore, we propose a comprehensive framework that uses machine learning to predict fuel efficiency by integrating various vehicle information. The proposed method comprises a predictive model and analysis framework utilizing key vehicle attributes, such as fuel type, engine displacement, and vehicle grade, to enhance prediction accuracy. We conducted a comparative study using six machine-learning models. To evaluate the machine learning model, MSE (Mean Square Error), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R-squared ( $$R^2$$ Score) were used. We experimented with SHAP(Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and odds ratio analysis to evaluate the impact of various factors on fuel efficiency. We confirmed that the proposed method can predict fuel efficiency. Extra Trees Regressor and Random Forest Regressor demonstrated high prediction accuracy, particularly excelling in capturing nonlinear relationships. We also underscore the importance of identifying markers to support decision-making, offering critical insights into the key factors impacting fuel efficiency predictions.https://doi.org/10.1038/s41598-025-96999-0Vehicle FuelFuel consumptionMachine learningFuel MarkerFuel Framework |
| spellingShingle | So-rin Yoo Jae-woo Shin Seoung-Ho Choi Machine learning vehicle fuel efficiency prediction Scientific Reports Vehicle Fuel Fuel consumption Machine learning Fuel Marker Fuel Framework |
| title | Machine learning vehicle fuel efficiency prediction |
| title_full | Machine learning vehicle fuel efficiency prediction |
| title_fullStr | Machine learning vehicle fuel efficiency prediction |
| title_full_unstemmed | Machine learning vehicle fuel efficiency prediction |
| title_short | Machine learning vehicle fuel efficiency prediction |
| title_sort | machine learning vehicle fuel efficiency prediction |
| topic | Vehicle Fuel Fuel consumption Machine learning Fuel Marker Fuel Framework |
| url | https://doi.org/10.1038/s41598-025-96999-0 |
| work_keys_str_mv | AT sorinyoo machinelearningvehiclefuelefficiencyprediction AT jaewooshin machinelearningvehiclefuelefficiencyprediction AT seounghochoi machinelearningvehiclefuelefficiencyprediction |