Application of a adaptive neuro-fuzzy technique for projection of the greenhouse gas emissions from road transportation

In the past, different forecasting models have been proposed to predict greenhouse gas (GHG) emissions. However, most of these models are unable to handle non-linear data. One of the most widely known techniques, the Adaptive Neuro-fuzzy inference system (ANFIS), can deal with nonlinear data. Its ability to predict GHG emissions from road transportation is still unexplored. This study aims to fulfil that gap by adapting the ANFIS model to predict GHG emissions from road transportation by using the ratio between vehicle-kilometers and number of transportation vehicles for six transportation modes (passenger cars, motorcycle, light trucks, single-unit trucks, tractors, and buses) from the North American Transportation Statistics (NATS) online database over a period of 22 years. The results show that ANFIS is a suitable method to forecast GHG emissions from the road transportation sector.