Abstract
Novel computational models were developed to enhance Eco-driving in intelligent transportation systems (ITS). Initially, the relationship between fuel consumption, speed, acceleration, and jerk was analyzed. Two kinds of mathematical models integrating speed, acceleration, and jerk were established. Next, the correlation between model components and fuel consumption was analyzed using Pearson Correlation Coefficients (PCC) to further optimize the models. Finally, the predictive performance of the proposed models and the widely used Vehicle Specific Power (VSP) and the Virginia Tech Microscopic (VT-micro) models were compared using mean absolute percentage error (MAPE), root-mean-square error (RMSE), correlation coefficient (R), and coefficient of determination (R 2). The results indicated that the fuel consumption prediction models considering jerk were superior to the VSP and VT-Micro models: MAPE decreased by 32.8 and 8.9%, RMSE decreased by 41.1 and 15.3%, R increased by 4.2 and 1.3%, and R 2 increased by 23.5 and 4.0%, respectively. External validation using independent datasets confirmed the effectiveness of the proposed models.
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