## 6 Conclusions  
This study set out to bridge a critical gap in urban freight research by developing a data-driven framework to analyze the spatial interactions between heterogeneous HDT behaviors and functionally clustered urban freight zones. Different types of HDTs exhibit distinct travel patterns shaped by specialized freight functions, industrial demands, and spatial contexts. Existing studies have primarily described surface-level heterogeneities in HDT behaviors (e.g., trip volume and temporal distribution) but lack analyses of spatial interactions with urban freight zones, which are critical for understanding the hierarchical organization of urban freight systems and advancing precision management. Our work advances this field by developing a data-driven framework integrating high-frequency trajectory data from nearly 160,000 HDTs across seven types in Shanghai to characterize their functional clustering and quantify HDT-zone interactions, aiming to enhance theoretical and empirical insights into urban freight systems. The trip volume distribution serves as a quantitative proxy for Shanghai's industrial structure, revealing the city's role as a global logistics hub and manufacturing center [32; 33], with tractor trucks accounting for 38.66% (reflecting multi-modal transportation) and general cargo trucks comprising 32.96% (supporting manufacturing supply chains). The temporal distribution reveals a completely different travel pattern from passenger traffic, showing roughly the same trend and a unique single-peak midday peak [4; 34]. The slight differences are due to the differences in production plans. For instance, tractor trucks exhibit the flattest temporal distribution (Std: 0.254), reflecting their 24/7 alignment with port operations and container logistics, while fence-sided trucks show the highest diurnal variability (Std: 0.508), tied to daytime-dependent commercial and construction activities. By constructing grid-level indicators and applying hierarchical clustering, we classified Shanghai's freight zones into six functional clusters. This classification scheme represents a significant advancement over previous zone-based freight studies by revealing the heterogeneity in freight functionality across different urban areas [35; 36], such as Cluster 1's role as an international shipping hub and Cluster 4's function as a last-mile distribution capillary for urban commerce. This classification provided a foundational framework to analyze how regional roles mold HDT mobility patterns. Our spatial interaction analysis introduces a novel bidirectional verification approach that integrates HDT functionalities, application scenarios, and distribution patterns from three analytical dimensions: vehicle-type specialization, industrial spatial organization, and regulatory landscapes. Tractor trucks, with a high HHI of 0.71 in long-haul trunk route clusters, dominated areas closely associated with major ports and long-distance freight transfer hubs. General trucks, on the other hand, showed a more dispersed yet concentrated distribution in regional industrial parks. Specialized trucks, such as concrete mixers, oil tankers, and chemical tankers, operate with factory sites as focal points and employ a hub-and-spoke model of short-haul, high-frequency transport. This revealed a hierarchical division of labor in freight systems, in which tractor trucks dominated long-haul trunk routes, general trucks specialized in regional distribution, and specialized trucks served localized industrial demands. This division is not arbitrary but emerges from the interplay of industrial geography, cargo characteristics, and policy environments. Metrics like the HHI and ID entropy quantified the structural concentration and organizational diversity. This study makes three scholarly contributions. First, in terms of methodological innovation, it introduces a two-way verification mechanism between regional function and transport actor characteristics, bridging macro freight clustering with micro vehicle behavior.