<p>PPP-RTK combines the advantages of RTK (Real-Time Kinematic) and PPP (Precise Point Positioning), enabling high-precision positioning with rapid convergence, and is currently considered a state-of-the-art GNSS positioning technology. With the advancement of GNSS applications, clustered-user scenarios have gradually become more common. However, traditional PPP-RTK primarily focuses on single-user positioning and does not take into account the atmospheric similarities within a user cluster, leading to suboptimal positioning performance. To further enhance PPP-RTK positioning performance in clustered-user scenarios, we propose a clustered-user PPP-RTK (CU-PPP-RTK) model that enables joint processing of multi-user data, achieving better overall positioning performance than traditional single-user PPP-RTK (SU-PPP-RTK). To evaluate the performance of the proposed model, we conducted comprehensive experiments based on real-world clustered-user scenarios and datasets. The results show that under the condition without atmospheric constraints, the initial convergence of CU-PPP-RTK is faster than that of SU-PPP-RTK, and the initial convergence speed of CU-PPP-RTK improves as the number of users increases. However, when atmospheric constraints are applied, both models exhibit similar initial convergence performance. Meanwhile, CU-PPP-RTK demonstrates superior re-convergence performance compared to SU-PPP-RTK. Specifically, when some users experience re-initialization due to data interruptions or cycle slips, CU-PPP-RTK is able to achieve fast (without atmospheric constraints) of even instantaneous convergence (with atmospheric constraints). Leveraging this advantage, we applied CU-PPP-RTK to the deformation monitoring of the Hong Kong-Zhuhai-Macao Bridge, effectively addressing the challenges of re-convergence at monitoring stations. The above conclusion indicates that CU-PPP-RTK has great potentiality for widespread application in clustered-user scenarios.</p>