Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees

Authors: Yingjiu Pan, Shuyan Chen, Fengxiang Qiao, Satish V. Ukkusuri, Kun Tang 

Journal: Science of the Total Environment


Abstract:  Nowadays, more and more conventional diesel buses are being replaced by new-energy buses in many cities in China. Although new-energy buses are more environmentally friendly compared with traditional diesel buses, they may also generate kinds of greenhouse gases as well as harmful pollutants. Currently, there exist few studies on the emission characteristics of buses with new-energy fuels, especially the liquefied natural gas (LNG) bus. The primary objective of this study is to analyze and estimate the emission rates for LNG bus in real-world driving. First, the differences in emission distribution characteristics between LNG bus and other fuel types of buses are analyzed using visualization and statistical methods. Then, a gradient boosted regression tree (GBRT) approach is applied to estimate the rates of several kinds of emissions for LNG bus, including CO, CO2, HC, and NOx, by incorporating the information of driving state in the current period and several previous periods. The performance of the developed approach is evaluated by comparing with the polynomial regression method which is widely adopted in existing literature. Experimental results demonstrate that the proposed method outperforms the competitive method for the emissions estimation of LNG bus, with the average Mean Absolute Error (MAE) reduced by 27.3%, the average Mean Absolute Percentage Error (MAPE) decreased by 33.4%, and the average Root Mean Square Error (RMSE) decreased by 22.1%. The results indicate that the proposed model is a promising approach for estimating emission rates of LNG bus. Also, this study would provide theoretical support for emission simulation tools such as MOVES, where the LNG bus emission estimation is unavailable in its current version.

Keywords:  Air quality, Emissions, Vehicle specific power, Gradient boosted regression tree, Real-traffic conditions, LNG bus