Modeling the unobserved heterogeneity effects of the factors influencing the fuel consumption of heavy-duty diesel trucks under real road conditions: A preliminary investigation in China

Abstract

Heavy-duty diesel truck (HDDT) is one of the major sources of air pollution and energy consumption. To reduce the estimation bias and improve the interpretability, the random parameters logit (RPL) model was employed to examine the effects of influencing factors on fuel consumption of HDDTs in the real world. The unobserved heterogeneity effects varying across the samples on fuel efficiency were extracted from the long-term daily trip-based data. In order to further illustrate the advantages of the RPL model in explaining the impacts of factors, a fixed parameters logit model with twenty parameters was constructed and compared. The Akaike information criterion and the Bayesian information criterion were used to select a more reasonable model structure. The findings show that the RPL model performs better and the unobserved heterogeneity would affect the effects of factors of rolling without engine load proportion and temperature and, consequently, map the level of fuel consumption. This reveals the variability of the fuel consumption among the samples. Driving compensation effects were also identified in this study (i.e., the drivers tend to perform the fuel-saving operations in adverse driving circumstances and vice versa). The methodology proposed in this paper can provide a new insight for researchers to identify the instability of energy-related factors under real road conditions. Future research could be implemented to assess the similar effects of alternative fuel vehicles.