Development of a Deep Learning-Based Model for Dynamic Monitoring and Analysis of Vehicle on-Road CO2 Emissions

In order to improve the monitoring accuracy of CO2 emissions from vehicular roads, the study developed a dynamic monitoring model based on temporal convolutional network (TCN) with long-short time memory network (LSTM) using deep learning techniques. The experimental data include 10,000 emission data from highways, urban roads and suburban roads, and the results show that the TCN-LSTM model has an MSE of 4.1 and an MAE of 2.5 for highways, an MSE of 5.3 and an MAE of 3.3 for urban roads, and a coefficient of determination of R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> of 0.92 for suburban roads, and the model's errors under different vehicle speeds and road conditions are less than 2.7 g/km, verifying its high accuracy and strong generalization ability.