An artificial neural network (ANN) was trained on chassis dynamometer data and used to
predict the oxides of nitrogen (NO x), carbon dioxide (CO2), hydrocarbons (HC), and carbon …

On-board diagnostics (OBD) data contain valuable information including real-world
measurements of vehicle powertrain parameters. These data can be used to gain a richer …

An accurate heavy-duty truck (HDT) fuel consumption model is essential for estimating the
truck energy consumption and evaluation of the effectiveness of energy saving strategies …

The transportation sector accounts for about 25% of global greenhouse gas emissions.
Therefore, an improvement of energy efficiency in the traffic sector is crucial to reduce the …

This study proposes a novel prediction model to accurately quantify the carbon emissions of
the trucks driving on dirt roads based on the deep learning neural network for small sample …

The most recent concern of all people on the Earth is the increase in the concentration of
greenhouse gas in the atmosphere. The concentration of these gases has risen rapidly over …

This study tested exhaust emissions and fuel consumption for a hybrid electric vehicle (HEV)
in real-world conditions using a portable emissions measurement system (PEMS). A …

The serially-correlated nature of engine operation is overlooked in the vehicular fuel and
emission modeling literature. Furthermore, enabling the calibration and use of time-series …

The increasing number of vehicles is one main cause of atmospheric environment pollution
problems. Timely and accurate long-and short-term (LST) prediction of the on-road vehicle …

This study investigates carbon dioxide emissions from light-duty diesel trucks using a
portable emission measurement system (PEMS) and a global positioning system. Two …