Modeling and spatial analysis of heavy-duty truck CO2 using travel activities

Heavy-duty trucks (HDTs) are vital components of the freight industry yet have faced criticism for their substantial CO2 emissions. This study, focusing on Xi'an, a crucial freight hub city in China, aims to investigate the factors influencing CO2 emission from HDTs. A unique aspect of this study is using a Latent Dirichlet Allocation (LDA) model to evaluate the potential impact of different travel activities on CO2 emissions using travel activities of HDTs extracted from extensive GPS data. Subsequently, the Random Forest (RF) model with a GeoShapley explainer was used to examine both the main and spatial effects of travel activities, road density, land use, and freight hub accessibility on CO2 emissions. The results revealed the existence of fifteen distinct travel activities among HDTs in Xi'an, eight of which clearly influence CO2 emissions. Considerable variations were observed in the magnitudes of the impact of different variables on CO2 emissions, as indicated by GeoShapley values. The density of expressways and main roads has the greatest impact on CO2 emissions, while various types of travel activities also significantly affect CO2 emissions, with the impact of different travel activities varying to some extent. Additionally, there is evident spatial heterogeneity in the impact of various variables on CO2 emissions, with larger positive GeoShapley values tending to concentrate around the 3rd Ring and expressways in Xi'an City. These findings, shedding light on the complex interplay of factors influencing CO2 emissions from HDTs, provide valuable insights for formulating environmentally sustainable management policies concerning HDTs from spatial perspectives.