| Summary: | On-road carbon dioxide (CO<sub>2</sub>) emissions from light-duty diesel trucks (LDDTs) are greatly affected by driving conditions, which may be better predicted with the sequence deep learning model as compared to traditional models. In this study, two typical LDDTs were selected to investigate the on-road CO<sub>2</sub> emission characteristics with a portable emission measurement system (PEMS) and a global position system (GPS). A deep learning-based LDDT CO<sub>2</sub> emission model (DL-DTCEM) was developed based on the long short-term memory network (LSTM) and trained by the measured data with the PEMS. Results show that the vehicle speed, acceleration, VSP, and road slope had obvious impacts on the transient CO<sub>2</sub> emission rates. There was a rough positive correlation between the vehicle speed, road slope, and CO<sub>2</sub> emission rates. The CO<sub>2</sub> emission rate increased significantly when the speed was >5 m/s, especially at high acceleration. The correlation coefficient (R<sup>2</sup>) and the root mean square error (RMSE) between the monitored CO<sub>2</sub> emissions with PEMS and the predicted values with the DL-DTCEM were 0.986–0.990 and 0.165–0.167, respectively. The results proved that the model proposed in this study can predict very well the on-road CO<sub>2</sub> emissions from LDDTs.
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