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The lack of country-specific data related to the real-world fuel consumption (FC) of motor vehicles is a significant impediment to the inventory of greenhouse gas (GHG) emissions in the transport sector in Vietnam. This study aims to develop a cost-effective solution for estimating the on-road FC of motorcycles (MC) to overcome this lack. An Artificial Neural Network (ANN)-based model (R 2  &gt;0.85 and MAPE &lt; 20%) was developed to predict the MC’s instant FC rate (FR inst ) based on the experimental data that were continuously collected using the data logger installed directly on the test MC. The developed ANN-based model was utilized to estimate FR inst  according to the real-world driving characteristics that were collected using the Global positioning system (GPS) device. The discrepancy between the estimated and measured FC (in terms of liters per kilometer) was only 6.8%. Consequently, the proposed approach could help improve the consumed fuel-based GHG emission inventory in the transport sector.

A cost-effective solution to estimate fuel consumption and greenhouse gas emissions for motorcycles: a case study