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

Ehsan Moradi and Luis Miranda-Moreno
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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

Sustainability, 2022, vol. 14, issue 3, 1-26

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.

Keywords: vehicular emissions; eco-driving; recurrent neural networks; ensemble learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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