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A Mixed Ensemble Learning and Time-Series Methodology for Category-Specific Vehicular Energy and Emissions Modeling

Author

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  • Ehsan Moradi

    (Department of Civil Engineering, McGill University, Montreal, QC H3A 0C3, Canada)

  • Luis Miranda-Moreno

    (Department of Civil Engineering, McGill University, Montreal, QC H3A 0C3, Canada)

Abstract

The serially-correlated nature of engine operation is overlooked in the vehicular fuel and emission modeling literature. Furthermore, enabling the calibration and use of time-series models for instrument-independent eco-driving applications requires reliable forecast aggregation procedures. To this end, an ensemble time-series machine-learning methodology is developed using data collected through extensive field experiments on a fleet of 35 vehicles. Among other results, it is found that Long Short-Term Memory (LSTM) architecture is the best fit for capturing the dynamic and lagged effects of speed, acceleration, and grade on fuel and emission rates. The developed vehicle-specific ensembles outperformed state-of-the-practice benchmark models by a significant margin and the category-specific models outscored the vehicle-specific sub-models by an average margin of 6%. The results qualify the developed ensembles to work as representatives for vehicle categories and allows them to be utilized in both eco-driving services as well as environmental assessment modules.

Suggested Citation

  • Ehsan Moradi & Luis Miranda-Moreno, 2022. "A Mixed Ensemble Learning and Time-Series Methodology for Category-Specific Vehicular Energy and Emissions Modeling," Sustainability, MDPI, vol. 14(3), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1900-:d:743846
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    References listed on IDEAS

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    1. Gennaro Nicola Bifulco & Francesco Galante & Luigi Pariota & Maria Russo Spena, 2015. "A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems," Sustainability, MDPI, vol. 7(10), pages 1-18, October.
    2. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
    3. Jose, Victor Richmond R. & Winkler, Robert L., 2008. "Simple robust averages of forecasts: Some empirical results," International Journal of Forecasting, Elsevier, vol. 24(1), pages 163-169.
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    Cited by:

    1. Muhammed A. Hassan & Hindawi Salem & Nadjem Bailek & Ozgur Kisi, 2023. "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

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