Understanding and estimating the carbon dioxide emissions for urban buses at different road locations: A comparison between new-energy buses and conventional diesel buses.

Public transport buses are heavy-duty vehicles that travel through the city from morning till night, which emits a large number of greenhouse gases. Understanding and estimating the characteristics of carbon emissions for transit buses are critical in achieving a low-carbon transportation system. In this study, the changes in carbon dioxide (CO2) emissions generated from new-energy buses as well as traditional diesel buses at bus stations, intersections, and road segments are compared using statistical analysis approaches; then the factors significantly affecting the emission rates are identified based on correlation analysis and feature selection methods. Finally, a gradient boosted regression tree (GBRT) model is proposed to conduct estimations for CO2 emission rates of buses. The results indicate that different sensitivities to various influencing factors exist in the carbon dioxide emissions of different types of buses. In addition, the VT-Micro regression method and Random forest technique were utilized to compare with the developed GBRT model. According to the comparison results, the estimation errors of GBRT fluctuate in a smaller range, suggesting that the GBRT model outperforms traditional approaches in emission estimation of carbon dioxide. Also, the deep understanding of the emission characteristics for both new-energy buses and conventional diesel buses helps to plan and dispatch buses with different fuel types according to local traffic conditions.

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

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.