## 1 Introduction  
According to the National Bureau of Statistics of China, in 2024, the volume of express deliveries in China surpassed 170 billion, representing a 21% increase from the previous year. The burgeoning volume of logistics in China has exerted substantial pressure on terminal distribution, particularly due to the temporal and spatial mismatches between delivery personnel and recipients [1]. Consequently, within the domain of logistics delivery, ensuringtimely and accurate parcel distribution in the last mile remains a persistent challenge in this critical phase. Parcel lockers (PLCs), characterized by their innovative \"distribution and self-pickup\" paradigm, have emerged as a pivotal solution to mitigate the temporal constraints between courier services and recipients [2], by reducing delivery time to enhance the convenience of user pickup [3], and are highly favored by both delivery companies and recipients [4]. This model not only alleviates the strain on terminal distribution but also capitalizes on its contactless delivery feature, which was instrumental during the COVID-19 pandemic [5]. Notably, data indicates that the deployment of PLCs in China surged to 2 million units in 2021, marking a staggering year-over-year growth of 159.07%. As the number of parcel lockers (PLCs) grows, the density of these lockers within urban spaces increases, and their distribution expands throughout urban areas. Consequently, facilitating efficient user path planning to optimize parcel locker location selection is becoming increasingly important. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability, and tracking user needs and dynamically planning user paths to determine the optimal PLC location is a highly practical trend [6]. Research suggests that users prefer the convenience of self-pickup along their regular routes [7]. This indicates that users who collect their packages via their regular paths can increase their satisfaction and willingness to use the parcel lockers. However, in the context of smart cities, developing user-centric PLC location selection methods faces dual challenges: user paths are influenced by dynamic preferences (such as detour willingness) and external constraints (such as time windows), resulting in uncertainty; PLC deployment requires balancing facility costs, capacity limitations, and service coverage rates. This study aims to address the following core issues: (1) how to extract regular activity hotspots from user spatiotemporal trajectories and quantify their correlation with PLC site selection; (2) how to dynamically integrate parcel physical attributes (weight-volume-urgency) into route planning decisions; (3) how to balance user convenience with logistics resource utilization through algorithmic design. Consequently, this paper proposes a user-centric path planning model that integrates kernel density estimation (KDE) and spatiotemporal behavior analysis techniques to optimize parcel locker location selection decisions in campus scenarios. The main findings are as follows:  
(1) Spatiotemporal behavior analysis-based KDE modeling reduces users' detour distance for package pickup by an average of 68% (case of User A), demonstrating the optimization potential driven by behavioral data. (2) The package urgency parameter (\(\alpha\)) significantly influences PLC selection strategy, with users tending to sacrifice distance for timeliness in high-urgency scenarios (\(\alpha\geq 3\)). (3) The model maintains stability in synthetic city-level data, indicating its scalability. The contributions of this study include innovatively embedding user spatiotemporal behavior and package attributes into the KDE framework, breaking through the static assumptions of traditional location selection models. Its practical value includes providing technical pathways for \"last-mile\" low-carbon solutions (reduced detours = lower carbon emissions) and personalized services.