## 6 Conclusions  
This study reveals the core value of user spatiotemporal trajectory data in path planning. By employing kernel density estimation (KDE) to unify the modeling of user activity hotspots, parcel physical characteristics (weight-volume-urgency), and path regularity, it demonstrates that behavior-driven PLC site selection can effectively identify activity hotspots, bridges the temporal and spatial gaps between users and couriers, prioritizing PLCs aligned with users' routine mobility patterns, and reduce detour distances (e.g., a 68% reduction for User A). This research challenges traditional static site selection models for neglecting user preferences and provides theoretical support for the \"human-centric\" design of logistics. Validation in a campus scenario demonstrates the model's potential applicability in complex urban environments. For instance, the dynamic matching mechanism between high-density hotspots and PLC networks can be extended to transportation hubs or commercial districts, enabling integrated \"commute-pickup\" services and offering new insights for multimodal logistics coordination in smart cities. However, limitations persist. First, the assumption of a homogeneous 2D space overlooks real-world complexities such as road networks and building density, potentially overestimating route efficiency in dense urban areas. Second, the small sample size and localized data (Shenzhen University) limit generalizability. Third, fixed KDE bandwidths may not adapt well to varying activity ranges, risking suboptimal hotspot identification in heterogeneous environments. Future research should focus on the following: (1) Enhanced Environmental Complexity: Studies should integrate 3D Geographic Information Systems (3D-GIS) with real-time traffic data to simulate multi-level indoor and outdoor route planning, thereby improving model accuracy in complex urban scenarios. (2) Expanded Data Diversity: Subsequent work will incorporate multi-source data (e.g., Amap API commuting traffic, UAV delivery trajectories) and broaden user samples to cover diverse age and occupational groups (e.g., commuters, elderly populations) to validate the model's generalizability. Additionally, exploring parameter adaptability in specialized scenarios such as emergency medical supply deliveries will be prioritized. (3) Technological Integration and Optimization: Plans include introducing reinforcement learning (RL) to dynamically adjust KDE bandwidth, replacing the current fixed Silverman rule, to better adapt to varying user activity patterns. Furthermore, integrating IoT devices for real-time package monitoring (e.g., temperature-sensitive pharmaceuticals) will enable the development of an intelligent decision-making system linking \"demand-path-facility\" interactions. (4) Deepened Sustainability: Further quantification of the model's impact on carbon emissions will be pursued--for instance, estimating fuel savings through reduced detour distances or analyzing the contribution of PLC shared usage to urban resource efficiency. These findings will provide a basis for governments to formulate carbon credit policies. In summary, this study proposes a path planning framework that integrates user spatiotemporal behavior analysis with kernel density estimation, reinforcing the existing literature on the collaborative optimization of user preferences and logistics efficiency. Compared to cost-oriented models, this framework incorporates user behavior preference metrics into the decision-making process, achieving a paradigm shift from \"resource optimization\" to \"user-resource win-win\". While challenges remain in scalability and environmental realism, the proposed model offers a foundational framework for smart city logistics. It has promising application potential in areas such as e-commerce distribution, multimodal transport integration, and carbon-neutral urban planning. Conceptualization, H.Z., L.Z. and P.L. ; methodology, H.Z., L.Z. and P.L. ; software, H.Z. ; validation, H.Z. and L.Z. ; formal analysis, H.Z. and P.L.