## 2 Literature Review  
Historically, studies on freight travel behavior have predominantly relied on data from travel surveys [11; 12; 13], driver logs [14], and internal enterprise records [15]. While these sources offer foundational insights, they often suffer from small sample sizes, recall bias, and limited temporal and spatial coverage, leading to analyses that may lack robustness and representativeness. For example, self-reported data are susceptible to social desirability bias, wherein respondents may underreport undesirable behaviors or overreport favorable ones [16]. Moreover, the process of collecting such data is typically time-consuming and resource-intensive, posing significant challenges for large-scale, dynamic freight system analysis. The advent of Global Positioning System (GPS) trajectory data presents a promising alternative [17]. With its high-frequency sampling and wide spatial coverage, trajectory data enable the accurate identification of vehicle movement patterns and behavioral trends across space and time [18]. This technological advancement offers a more comprehensive, scalable, and reliable basis for analyzing freight activity, thereby overcoming many of the limitations associated with traditional survey-based methods. Despite these technological advancements, current research exhibits two critical limitations that this study specifically addresses. First, many existing studies continue to treat HDTs as a homogeneous group, neglecting the significant heterogeneity in their travel behavior [12]. While some researchers have employed activity-based models to classify freight movement [19; 20], such models often fail to incorporate distinctions by vehicle type. Given that vehicle types are closely tied to specific industries, cargo characteristics, and operational contexts, this oversight limits the explanatory power and applicability of these models. Recent work has begun to address this gap by leveraging trajectory data to explore the spatial heterogeneity of HDT activities. For instance, research by [PERSON] et al. [21] demonstrates that HDT movements are influenced by a variety of industrial, transport infrastructure, and land use factors, which vary considerably across urban space. Results show that logistics enterprise density has the most significant positive impact on the intensity of HDT activities, followed by secondary and ordinary road density, while the distance to parking lots is significantly negatively correlated. Further analysis by [PERSON] et al. [22] emphasizes the unequal distribution of the trip generation intensity across zones, highlightingthe need for finer-grained and vehicle-type-specific analyses. Similarly, findings from [PERSON] et al. [23] reveal distinct multiday trip-chaining patterns that are unique to specific truck categories, underscoring the necessity of incorporating vehicle-type distinctions into freight behavior research. Second, most previous studies have focused predominantly on the single dimension of vehicle heterogeneity. They have seldom delved into how the behavior of HDTs is simultaneously shaped by the functional roles of the urban areas they serve. This bidirectional relationship is crucial for parsing the urban freight transport system, yet it remains largely unexplored in the current literature. Recent advances in spatial freight analytics have underscored the critical role of urban regional functions in shaping freight dynamics. A study by [PERSON] et al. [24] applied GPS trajectory clustering to identify key loading/unloading hotspots in Turin, Italy, revealing that high-priority freight zones correlate with retail density and traffic bottlenecks, thereby linking freight activity to urban spatial functionality. Similarly, research by [PERSON] et al. [25] mapped shared freight networks in Hong Kong onto traffic analysis zones (TAZs), isolating critical freight areas (KFAs) characterized by high levels of connectivity and cargo throughput.