###### Abstract  
The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, frequency, etc., but there is a lack of in-depth analyses of the spatial interaction between freight travel and freight functional clustering, which restricts a systematic understanding of freight systems. Against this backdrop, this study develops a data-driven framework to analyze HDT behavioral heterogeneity and its spatial interactions with a freight functional zone in Shanghai. Leveraging the high-frequency trajectory data of nearly 160,000 HDTs across seven types, we construct a set of regional indicators and employ hierarchical clustering, dividing the city into six freight functional zones. Combined with the HDTs' application scenarios, functional characteristics, and trip distributions, we further analyze the spatial interaction between the HDTs and clustered zones. The results show that HDT travel patterns are not merely responses to freight demand but complex reflections of urban industrial structures, infrastructure networks, and policy environments. By embedding vehicle behaviors within their spatial and functional contexts, this study reveals a layered freight system in which each HDT type plays a distinct role in supporting economic activities. This research provides a new perspective for deeply understanding the formation mechanisms of HDT trip distributions and offers critical evidence for promoting targeted freight management strategies. heavy-duty truck; trajectory data; urban logistics; functional clustering; freight trip 202  
productivity while mitigating negative externalities such as traffic congestion, air pollution, and energy consumption [3]. Heavy-duty trucks (HDTs), a critical component of urban freight, encompass a diverse range of vehicle types, ranging from dump trucks and tractors to refrigerated trucks and car carriers. Each type serves distinct economic sectors and industrial functions, operating under unique constraints [4]. More importantly, different types of HDTs exhibit starkly contrasting travel patterns, varying in trip demand intensity and spatial distribution, which directly influence traffic congestion, energy consumption, and air pollution [5; 6]. However, current urban freight management policies, such as emission standards, vehicle access restrictions, and electrification mandates, often adopt a one-size-fits-all approach, failing to adequately account for these differences in policy formulation [7]. Achieving such differentiation is non-trivial, as it first requires a deep understanding of the operation behaviors of different HDT types. Urban regions are characterized by heterogeneous economic and freight activities, which define their unique freight functional identities [8; 9]. For example, coastal ports specialize in bulk commodity transportation, hazardous chemicals industries rely on specialized vehicles, and commercial districts depend on flexible last-mile deliveries. These regional functions not only shape the spatiotemporal patterns of specific HDT types but are also reflected in the distinct travel characteristics of these vehicles. Conversely, analyzing HDT travel patterns can reveal the underlying freight demands and functional clustering of urban areas, creating a reciprocal relationship between regional economics and transportation behavior. Despite this interplay's importance, existing research has primarily focused on descriptive statistics of HDT trips (e.g., volume, frequency, and distance) without fully exploring how their spatiotemporal dynamics interact with specialized urban functions, like industrial clusters, commercial corridors, or logistics hubs [10]. This gap limits our ability to decipher the generative mechanisms of HDT trips and hinders the development of targeted policies.