Knowledge of the core structure and inherent dynamics of urban freight transport systems is important for the development of policies, aimed at improving the livability and sustainability of cities. The past decade has witnessed a great deal of efforts into analyzing the geographic structure of urban freight transport systems. However, in-depth studies on the system core structure and underlying dynamics are still absent. This study contributes to the field by analyzing large scale freight truck trip data from Chinese cities, using complex network analysis. We empirically reconstruct and characterize the urban freight truck mobility networks and reveal the underlying spatial interaction patterns. We develop a spatial network growth model which explains how hub-and-spoke core structure of urban freight transport systems are formed. The developed model captures the essential interaction dynamics of freight locations, and explains the effects of spatial distance, economic size and business pattern replication. Inspired by the model, we provide policy implications for land-use planning, transportation planning and sustainable urban development.

Accurate estimation of intercity heavy truck mobility flows is of vital importance to urban planning, transportation management and logistics operations. The inaccessibility of big data related to intercity transport systems and the heterogeneity of trucking activities pose challenges for the reliable estimation. Recently, the advance of Artificial Intelligence (AI) provides a potential solution to this problem. However, most previous studies focused on the estimation of inter-regional passenger mobility. In-depth studies of estimating intercity heavy truck mobility flows by using deep learning techniques are still scarce. To fill in the gaps, we construct a deep neural network based on the Deep Gravity framework, an advanced predictive model for human mobility. We collect a wide range of data related to heavy truck movements, freight locations, road networks and land uses to train the model, and validate its high performance by comparing to traditional gravity model. Furthermore, we use an explainable AI technique to interpret how the city features contribute to the determination of intercity heavy truck movements, and the results can provide valuable policy implications for logistics operations, businesses and urban planning.

Knowledge of the hierarchical organization of urban heavy truck flows is important for understanding the structure of urban freight system and underlying interactions dynamics, providing insights to assess and develop freight policies. The complexity and dynamic nature of urban freight system pose significant challenges in comprehensively capturing structured arrangement of heavy truck movements. In this paper, we uncover the hierarchical organization of urban heavy truck flows by using complex network theory. We use large-scale heavy truck GPS data and urban freight location point-of-interest (POI) data to construct urban heavy truck mobility networks, and detect their community structure. The empirical results suggest different sets of locations are closely linked to each other to form multiple clusters. By integrating the categories of locations, we reveal the cluster-specific industry concentration and industry-specific location roles, informing evidence-based policy formulation. To capture the interaction dynamics of locations, we develop a spatial network growth model that considers the spatial agglomeration of industrial clusters and interaction pattern of locations. The model provides a mathematical tool to simulate the formation process of real-world networks for logistics planning and management.