Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

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This paper studies a dynamic capacitated arc routing problem for battery replacement in an e-bike sharing system, where workers replace batteries for underpowered e-bikes along street segments dynamically. The objective is to replace as many batteries as possible and minimize pickup failures. The temporal dependency of the routing decisions, the conflict of the workers’ operations, and the stochastic and dynamic nature of user demands all make this a difficult problem. To cope with these difficulties, a “Partition-First, Route-Second” bi-level solution framework is adopted to describe the problem in two different time scales. On the large time scale, a spatiotemporal partitioning method, which divides the road network into nonoverlapping subzones, is proposed to decompose the problem. On the small time scale, this paper models the routing decision process of individual worker as a Markov Decision Process. We adopt a lookahead policy that simulates future information and decisions over some horizons to evaluate the long-term influence of current feasible decisions. A Monte Carlo Tree Search algorithm is also used to improve the simulation efficiency. By performing numerical computation experiments on a test case study and comparing with some benchmarking policies, we demonstrate the effectiveness and efficiency of the suggested method.