The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-2/W7
14 Sep 2017
 | 14 Sep 2017


Q. Li and T. Jia

Keywords: CMEM model, automobile exhaust emission, correlation coefficient, asynchronous correlation of time series, time delay error

Abstract. Carbon dioxide emissions from urban road traffic mainly come from automobile exhaust. However, the carbon dioxide emissions obtained by the instruments are unreliable due to time delay error. In order to improve the reliability of data, we propose a method to correct the measured vehicles’ carbon dioxide emissions from instrument based on the CMEM model. Firstly, the synthetic time series of carbon dioxide emissions are simulated by CMEM model and GPS velocity data. Then, taking the simulation data as the control group, the time delay error of the measured carbon dioxide emissions can be estimated by the asynchronous correlation analysis, and the outliers can be automatically identified and corrected using the principle of DTW algorithm. Taking the taxi trajectory data of Wuhan as an example, the results show that (1) the correlation coefficient between the measured data and the control group data can be improved from 0.52 to 0.59 by mitigating the systematic time delay error. Furthermore, by adjusting the outliers which account for 4.73 % of the total data, the correlation coefficient can raise to 0.63, which suggests strong correlation. The construction of low carbon traffic has become the focus of the local government. In order to respond to the slogan of energy saving and emission reduction, the distribution of carbon emissions from motor vehicle exhaust emission was studied. So our corrected data can be used to make further air quality analysis.