Vehicle carbon emission estimation for urban traffic based on sparse trajectory data

Sparse trajectory data with non-second-by-second sampling intervals are common. However, most carbon emission estimation models for vehicles require second-by-second inputs. Additionally, some models ignore the emission generation principle, and some have complicated inputs. To address these limitations, this study proposes a vehicle carbon emission estimation method for urban traffic, based on sparse trajectory data. First, a trajectory reconstruction method based on interpolation of acceleration distribution is proposed. The results showed that the reconstructed trajectory was close to the real trajectory, and the accuracy was 2%–17% higher than that of other methods. Second, a carbon emission estimation model that considers both the emission generation principle and feasibility is proposed. The model with a goodness-of-fit of 0.887 had the best performance compared to the other models. The emission estimation results of the reconstructed sparse trajectories showed that the precision improved significantly for data with different frequencies compared to that of other reconstruction methods, e.g., 9% higher at a 30 s sampling interval.