Establishment of a novel DNNSS-MOVES prediction model for carbon emissions of trucks driving on dirt roads

Abstract

This study proposes a novel prediction model to accurately quantify the carbon emissions of the trucks driving on dirt roads based on the deep learning neural network for small sample (DNNSS) and the motor vehicle emission simulator (MOVES). The application range of the MOVES model was extended to the transportation of asphalt mixtures on the temporary road. The model correction method was also established based on the rolling resistance coefficient (C) and the correction coefficient (μ). By comparing the measured fuel consumption of the truck with the carbon emissions calculated by the MOVES model, the values of C × μ can be back-calculated. DNNSS was constructed for estimating the C × μ. The Adaptive Moment Estimation (Adam) algorithm was used to dynamically adjust the learning rate and accelerate the convergence of the network; the Dropout function was used to alleviate overfitting; and the Rectified Linear Unit (ReLU) function was used as the activation function to solve the gradient vanishing problem. The test results showed that the vehicle speed and load greatly influence the C × μ. The DNNSS algorithm was better at predicting the C × μ. The proposed DNNSS-MOVES model was more accurate than the conventional methods.