Dynamic Capture and Estimation of Energy-Emission Benefits for Electric Taxicabs with Spatiotemporal Big Data and Deep-Learning-Based Microscopic Model

Abstract

Reducing carbon emissions to cope with climate change and short of energy have become a global trend, so it is urgent to accurately measure energy consumption and emissions. As taxi occupies the second highest proportion of domestic roads, it is necessary to study the emission of new energy. However, existing studies often consider the whole region, with low accuracy and no true value, resulting in difficult verification of conclusions. This paper proposed a micro-energy consumption and carbon emission model for taxicabs based on trajectory data and deep learning method to dynamically simulate the real-time energy consumption of taxicabs with different energy sources. First, this paper detected the correlation between driving state and energy consumption carbon emission in portable emissions measurement system (PEMS) environment. Then, a deep learning-based framework was built to learn the vehicle’s energy consumption carbon emission pattern. In particular, Gated Recurrent Unit (GRU) neural network is used to learn current and historical driving habits and the influence of external environment on energy consumption, while life cycle assessment (LCA) method is used to obtain the emission patterns of vehicles with different energy types in the whole life cycle. The measured data are obtained in Wuhan, the precision of our model is higher than that of the existing model. At the same time, we also applied it to the taxicab monthly trajectory dataset to obtain the spatial-temporal energy consumption emission patterns of different types of energy. The results show that pure electric vehicle (PEV) has obvious greenhouse gas emission reduction effect, compared with gasoline and compressed natural gas (CNG) vehicles, the emission reduction is 12.03% and 12.07% respectively, but the total energy consumption achieves little advantage. This model will lay a foundation for the formulation of regional road network emission inventory, so as to provide support for the government to make relevant decisions.