## 6 Conclusions  
EVs are regarded as an environmentally friendly mode of transportation and have been implemented in public transit and taxi services worldwide. To maintain the operational efficiency of electric taxis, a properly designed charging station deployment plan is essential. In order to assist in the charging station deployment design process, this paper proposes a heuristic algorithm called Improved K-means iterated with Queuing Theory (IKQT), which considers charging waiting time and the number of unserved charging demand to enhance the deployment of charging stations. The GPS trajectory data from the Shanghai Qiangsheng taxi company were then adopted for model validation. The proposed deployment plan can reduce the total waiting time of all charging services by approximately 6%. The IKQT algorithm offers a robust framework that can be replicated and adapted to various urban settings, highlighting its methodological replicability. However, as an empirical study, there are several avenues for improvement and future research. Firstly, exploring a wider range of \(\alpha\) and \(\beta\) values for weight calculation could enhance the algorithm's efficiency and convergence in each iteration. Furthermore, developing dynamic influence factors for \(\alpha\) and \(\beta\) based on specific formulas could further refine the weight calculation process. Additionally, augmenting the model with additional factors is crucial to improve its comprehensiveness. One significant factor to consider is the variability in traffic conditions across different areas of the city and at different times of the day. Incorporating this variability into the distance calculation, potentially through one or more weight factors, could provide a more accurate representation of the real-world charging demand patterns. In conclusion, while the IKQT algorithm presents a promising approach for optimizing charging station deployment, there are several limitations that warrant attention and provide opportunities for refinement and expansion. Firstly, the current methodology solely considers the straight-line distance between each demand point and the corresponding charging station, rather than the actual travel distance, which may not accurately reflect real-world conditions. This limitation could potentially lead to suboptimal deployment plans. Secondly, the research lacks empirical validation through actual surveys (e.g., questionnaires) or field studies, relying instead on assumptions and data from a single taxi company. This limits the generalizability of the findings and the robustness of the proposed deployment plan. To address these limitations, future research should endeavor to incorporate more realistic travel distance calculations, conduct extensive empirical studies to validate the findings, and engage with relevant stakeholders to gather a comprehensive understanding of charging demand. By doing so, the IKQT algorithm can be further refined and expanded, ultimately leading to more effective and efficient charging station deployment plans that better serve the needs of electric taxi services in diverse urban contexts. **Author Contributions:** Conceptualization, L.L. and S.L. ; methodology, L.L. and C.C. ; software, L.L. ; validation, Y.Z., H.D. and S.L. ; formal analysis, L.L. and Y.Z. ; investigation, H.D. ; resources, C.C. ; data curation, L.L. and S.L. ; writing--original draft preparation, L.L. ; writing--review and editing, H.D. ; visualization, Y.Z. ; supervision, C.C. ; project administration, S.L. ; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is sponsored by the China Communication Construction Company Ltd. R&D program with the grant number 2019-ZJKJ-ZDZX02. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.