## 2 Literature Review  
This section summarizes the literature review, mainly focusing on data-driven approaches to prioritize and manage public complaints in various domains, from urban infrastructure to customer service and highway maintenance. [PERSON] and [PERSON] [3] proposed a scoring-based methodology to determine the priority of highway improvement projects. Categorizing 26 parameters established for the project evaluation into six groups, such as need, deficiency, continuity, benefit-cost, local opinion, and social-economic and environmental consequences, the authors evaluated the parameters and assigned weight factors to each parameter group for each project. The weight factors were determined by state transportation board members, department of transportation (DOT) agencies, and local transportation planners, obtained from a state-wide panel survey in Georgia, USA. The final score for each project was estimated by summing the weight factors, and the priority is determined based on the estimated score: higher score, higher priority. [PERSON] and [PERSON] [4] adopted an association rule mining method to identify patterns in customer complaints and improve complaint management. The authors applied the a priori algorithm to a large dataset of complaints submitted to Tehran's municipality to identify relationships among factors such as season, complaint type, and citizen demographics. By analyzing these relationships, the proposed method prioritized responses and enhanced service quality by addressing frequent and region-specific complaints. [PERSON] et al. [5] developed a novel urban management model to improve the quality of the response to citizen complaints in Tehran, Iran. To this end, the authors combined citizen relationship management (CiRM) and data mining techniques to better understand the primary needs of citizens and optimize municipal services. Employing key factors for identifying and prioritizing citizen needs, such as frequency, time interval, and satisfaction, the study categorized urban needs into primary, secondary, and uncommon groups by applying clustering methods to the key factors. The results demonstrate that the CiRM-clustering combined method can significantly enhance urban management practices by allowing municipalities to better target and address citizens' most pressing concerns. This model also addressed potential applications in broader public sector customer relationship management (CRM), offering a structured, data-driven approach to improve service delivery and citizen satisfaction. [PERSON] and [PERSON] [6] attempted to analyze urban civic complaints, such as pavement condition, traffic congestion, noise, and drainage, by applying clustering methods. The proposed analysis framework consisted of two components: (1) a dynamic grid-based clustering and (2) a location-based clustering. The dynamic grid-based clustering conducted complaints analysis based on spatial attributes of an urban area, taking into consideration the spatial connectivity of the urban street network. The location-based clustering focused on the types of complaints to group each complaint based on (1) density of complaints (DF) and (2) temporal patterns of complaints (TF). Utilizing the kernel density clustering method [5], DF was measured to quantify the concentration of a complaint to evaluate its significance. TF was employed to estimate the temporal correlation of each complaint group to determine the weighted scores. By combining DF and TF, the criticality score of each complaint group was estimated to finalize the priority. The proposed framework was applied to New York City, USA, and Bangalore, India. The results show that the proposed framework helped urban planning strategies increase civilians' satisfaction level with the service of complaints. [PERSON] et al. [7] examined three clustering algorithms, such as K-means, fuzzy C-means (FCM), and independent component analysis (ICA), to assess public needs in terms of citizen relationship management (CiRM).