LNG Bus Emissions Prediction Using Neural Network

Being a key feature in any developed society, transportation systems play a vital role to satisfy mobility and accessibility needs. Urbanization, an expanding transport sector and timely access to any specific location are the pivotal factors that contribute to the negative impact on environment. Vehicular emissions such as carbon monoxide (CO), nitrogen oxides (NO_X), hydrocarbons (HC), and carbon dioxide (CO₂) are anthropogenic in nature. This study corroborates the use of a neural network model for the prediction of vehicular emissions based on actual road data of LNG buses measured by portable emissions measurement system, running on line no. 51 in Zhenjiang, China. The data encompasses speed, acceleration, road grade, and passenger load as the inputs and indicative emissions (CO₂, NOx, CO, and HC) as outputs. This research aims at LNG transit bus emissions modeling based on vehicular and road parameters to measure vehicle specific power correlation with vehicular emissions.