Examining Influencing Factors of Truck Trip Generation Using Spatial Econometric Models

Accurately estimating truck trip generation (TTG) is essential for improving the effects of freight planning and informing strategic decisions regarding land use and environmental management. However, prior research has inadequately examined the intricacies of TTG and its spatial correlations with the urban contexts. This study addresses this gap by employing spatial econometric models to disentangle the influence of the built environment on TTG using data from Guangzhou, China. We utilized a spatial regression framework, with TTG estimates derived from a freight facility–based generation model as the key dependent variable. This approach allowed us to scrutinize the impact of various built environment factors on TTG, including land-use patterns within traffic analysis zones (TAZs), road infrastructure quality, and the presence of external freight hubs. Our findings indicate that the spatial Durbin model (SDM) outperforms three alternative models in elucidating TTG dynamics. The study indicates that airports and seaports exert both direct and indirect influences on TTG, with intriguing shifts between positive and negative correlations. Warehousing facilities were found to have a markedly positive correlation with TTG. Additionally, industrial and retail land uses were identified as promoters of TTG, while simultaneously inhibiting TTG in adjacent areas. In contrast, residential land, as well as primary and secondary road networks, exhibited a negative correlation with TTG. Our research underscores the necessity for policymakers to consider the significant impact of large-scale external transportation infrastructure, particularly those with high warehouse density, on TTG. It is recommended that authorities devise spatial planning and traffic management strategies that are tailored to specific times and regions, ensuring a more nuanced and effective approach to cargo transportation management.