Evaluation of energy-environmental-economic benefits of CNG taxi policy using multi-task deep-learning-based microscopic models and big trajectory data

Abstract

Natural gas has been widely recognized as an economic and environmental-friendly alternative fuel in the transport sector. Many cities have implemented the policy to encourage taxis to replace gasoline with natural gas. However, few studies have comprehensively evaluated its energy-environmental-economic benefits (i.e., providing alternative energy, producing less CO2 emissions and saving fuel costs). To fill the gap, this study proposes a new multi-task deep-learning-based microscopic model for estimating vehicular carbon dioxide (CO2) emissions and fuel consumption simultaneously under natural gas and gasoline usage scenarios. Trajectories of 14,534 taxis are collected for empirical studies. Model validation results show that the proposed model outperforms five state-of-the-art baselines and achieves a very high accuracy of CO2 emission and fuel consumption estimations. Empirical results found that taxis replacing gasoline with natural gas can reduce CO2 emissions by 22.1% and reduce fuel costs by 38.3%. The results of this study have several methodological and policy implications for using natural gas in the transport sector.