Intercity freight connections in China under the view of mass truck trajectories

Intercity freight connections have reshaped urban networks and posed significant urban planning challenges. Previous studies mainly used annual statistical data or online orders, which are criticized for the poor timeliness and data representativeness. This study presents a method to construct freight flow networks by analyzing large-sample truck trajectories using spatiotemporal and semantic data mining. The spatiotemporal interaction patterns and influencing factors of intercity freight connections in China are then investigated. Key findings include: (1) The proposed method enables multidimensional and dynamic investigations by constructing timely and refined freight flow networks. (2) The network structure and spatial pattern of freight connections in China show monthly stability, significant spatial heterogeneity, symmetry, and a typical scale-free property. (3) Spatial interaction patterns are related to freight transportation scale. High-freight-volume regions form complete and homogeneous networks with activity space expanding, while low-freight-volume regions exhibit heterogeneity with connections focused on core cities. (4) More than 92 % of the socioeconomic factors exhibit significant nonlinear enhancement when interacting with distance. The nonlinear interaction between economic development level and distance is the most influential, explaining 23.7 %–85.7 % of the connection strength. These findings facilitate reasonable urban policy formulation and regional industrial collaboration.