## 6 Conclusions and Recommendations  
This study presents a data-driven decision-making support method aimed at improving the quality of public service to efficiently handle various public complaints about road use. To achieve this goal, this study first examined complaint processing times categorized by type (such as poor road facility condition, poor road condition, roadkill/rockslide, pothole, inadequate drainage facilities, and others) and by location (including areas around buildings, neighborhood amenities, roads, social overhead capital facilities, and railways) utilizing two years of data from the road problem reporting service. We determined processing rates for complaints (the number of civil complaints addressed per hour) for each type-location combination, applied weights between 1 and 100 based on the necessary processing time, and aggregated these weighted rates. A validation study assessing the frequency of reported locations by priority showed that the proposed method accurately reflects actual field processing of complaints. Additionally, given the importance of location in complaint handling, the georeferencing technique seems appropriate for pinpointing complaint locations for each report and integrating this into the decision-making framework.  
The DBSCAN clustering technique was used to organize complaint types and locations into groups, resulting in four final priority clusters (high, neutral, low, and very low), determined via the silhouette score test. Additionally, we applied the K-means clustering technique to the same data, cross-validated the results with those obtained from the DBSCAN technique, and confirmed their consistency. Additionally, understanding the importance of location in handling complaints, we applied georeferencing methods to pinpoint civil complaint locations for every report, integrating this into our analytical and decision-making system. This strategy, particularly if integrated into future complaint-processing prioritization techniques, has the potential to greatly improve the usefulness and relevance of georeferencing methods.  
Figure 5: Frequency of reported civil complaint locations by each priority level.  
Despite the possibility of containing minor errors identified during the analysis, the dataset used in this research was direct observations from complaint processing sites and may include minor data entry errors. Nevertheless, the dataset is considered highly valuable as it reflects a variety of real-world situations encountered during complaint handling.  
Previous research in this field proposed a method for automatically extracting issues from complaint-related photos and assigning priority rankings using machine learning techniques. Enhancing this model by integrating reported civil complaint locations could lead to the development of more precise artificial intelligence (AI)-based prioritization decision models, representing a promising direction for future research.  
Conceptualization, J.L., W.J. and J.K.; methodology, J.L. and W.J.; software, J.L.; validation, J.K., W.J. and J.L.; formal analysis, J.L.; investigation, W.J.; resources, J.K.; writing--original draft preparation, J.L.; writing--review and editing, W.J.; visualization, J.K.; supervision, W.J.; project administration, J.K.; funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Institute of Civil Engineering and Building Technology. The APC was funded by Korea Institute of Civil Engineering and Building Technology.  
Not applicable.  
Data Availability Statement: Data is unavailable to be shared as it contains personal information that might cause potential privacy issues.  
The authors declare no conflicts of interest.