## 2 Relevant Literature
### Common Statistical and Technological Tools
These tools enable city planners, policymakers, and researchers to model various urban systems, including transportation, land use, energy consumption, and microclimate, providing valuable insights for decision-making in smart city projects. As smart cities grew in complexity and scale, the volume, velocity, and variety of data generated by urban systems increased exponentially. This data explosion led researchers to recognize the need for more sophisticated tools that are capable of handling and extracting insights from massive datasets. Consequently, big data analysis emerged as a critical component in smart city studies, offering the ability to process and analyze vast amounts of structured and unstructured data from diverse sources such as IoT sensors, social media, and municipal databases. The adoption of big data analytics in smart city research has enabled a more comprehensive and real-time understanding of urban dynamics [50], facilitating data-driven decision-making, predictive modeling, and the development of innovative solutions to complex urban challenges [51]. This transition to big data analysis represents a significant leap forward in smart city research, allowing for more nuanced, accurate, and timely insights into urban systems and their interactions [52]. The range of tools used in smart city projects is extensive and constantly growing, aiding cities in incorporating technology into their urban environments for improved efficiency and control [53]. The progression of smart city research has led to the integration of Internet of Things (IoT) technologies [54], building upon the foundation laid by big data analytics. As researchers recognized the potential of real-time data collection and analysis, the IoT emerged as a crucial component in smart city studies. IoT devices, including sensors, actuators, and connected systems, enable the continuous gathering of granular data across various urban domains such as transportation, energy consumption, waste management, and environmental monitoring. This network of interconnected devices provides a constant stream of real-time data, enhancing the depth and breadth of information available for analysis. However, the large volume of data produced by these devices is often unstructured and presents challenges in analysis [55]. This is where ML has become an indispensable tool in smart city studies, enabling researchers to extract meaningful patterns and insights from the vast amounts of data generated by urban systems [56]. ML offers practical methods for analyzing the vast and diverse data cities collect [57]. Such tools analyze large amounts of data from diverse sources, such as IoT devices, social media, and public records [58; 59]. Through modern implementations, algorithms, patterns, trends, and relationships previously unobservable can be identified to assist city planners and policymakers in their decision-making [60]. For example, a practical ML application is a route navigation system that predicts traffic congestion based on real-time and historical data. The results may also assist in traffic management by suggesting changes to traffic signals or directing vehicles to different lanes to avoid congestion. ML models in energy management can more accurately forecast energy requirements based on weather conditions and past trends [61], helping to optimize the functioning of smart grids and respond to the consumption needs of consumers to improve service and prevent energy waste [62,63]. ML applications can also anticipate pollution levels by analyzing air quality, weather, and traffic data. This allows preventive measures to be taken to improve the health of citizens. In addition, ML can also be used for anomaly detection, monitoring continuous data streams for any unusual changes that may signal infrastructure failures, security breaches, or even pandemics [64,65]. A joint ML function can detect relatively minor changes in water pressure to indicate a leak and its location, prompting more rapid physical inspection and resolution.