Unmanned last-mile logistics, as an emerging technological approach, has seen preliminary applications in practice. However, systematic and quantitative research on its implementation effectiveness and optimization strategies remains insufficient. This study aims to quantify the actual demand for unmanned last-mile logistics on campus and evaluate the social, economic, and ecological benefits across different automation levels and scenarios, providing scientific guidance for campus logistics planning. Taking the Tsinghua University campus as a typical application scenario, a logistics demand measurement model is developed by integrating the YOLOv8 and ByteTrack algorithms to automatically identify and count delivery riders from surveillance data. On this basis, combined with questionnaire data, a simulation is conducted using the AnyLogic platform to analyze the impact of unmanned last-mile logistics under various scenarios. The results indicate that unmanned last-mile logistics offers significant advantages in improving delivery efficiency and reducing energy consumption. However, it also leads to increased customer waiting times, and the service level still needs to be improved. Strategies such as increasing robot or rider speed, adding more robots, and expanding delivery hubs can enhance delivery efficiency, reduce customer waiting times, and lower energy consumption. Based on the simulation results, it is found that at least 40 robots are required on campus to complete delivery tasks. Furthermore, the layout of 4-5 delivery hubs, each equipped with 15-20 robots, is found to be highly cost-effective. It is also recommended that robot and rider speeds be controlled between 13-15 km/h to balance safety and efficiency.