## 2 Relevant Literature
### Common Statistical and Technological Tools  
Indeed, scientific tools using advanced methods to analyze data and improve decision-making remain crucial for the success of smart cities. Robust statistical tools have played a crucial role in smart city studies, providing researchers with the means to analyze and interpret complex urban data [37; 38]. Initially, scholars relied on basic descriptive statistics and regression analyses to understand the patterns and relationships within urban systems. For example, the analysis of relationships and the impact of different factors, such as population density, urbanization, economic productivity, public infrastructure investments, daily fluctuations, and environmental conditions, all rely heavily on statistical constructs [39; 40]. Researchers and planners require the analysis to determine the primary drivers of urban change and the creation of well-crafted contingency plans [41]. For instance, regression analysis can help identify the relationship between the level of investment in public transportation infrastructure and a reduction in traffic congestion or air pollution [42]. As smart city research evolved, more advanced statistical techniques such as time series analysis, spatial statistics, and multivariate analysis were adopted to handle the increasing complexity of urban data. The development of computational power and specialized software packages further enhanced the capabilities of statistical analysis in smart city studies, enabling researchers to process larger datasets and perform more sophisticated analyses. The transition from purely statistical approaches to geographic information systems (GISs) occurred as researchers recognized the importance of spatial context in urban data. GISs have emerged as a popular tool in smart city research [43], enabling researchers and urban planners to visualize, analyze, and interpret spatial data essential for urban planning and management [44]. The GIS has been successfully utilized in transportation development, aiding cities in managing traffic and public transportation [45]. It has also been applied in land use planning and in helping cities balance development and conservation while identifying suitable areas for various urban activities. As smart city research evolved, scholars recognized the need for more dynamic modeling of urban systems, leading to the integration of simulation techniques with the GIS. This combination allowed researchers toleverage the spatial context provided by the GIS while incorporating temporal dynamics and predictive modeling through simulation [46; 47]. Therefore, simulation tools play a crucial role in smart city planning and development. These solutions allow city planners, policymakers, and researchers to model complex urban systems, predict outcomes, and make data-driven decisions [48]. These models provide insight into potential traffic and public transport changes when new developments are introduced [49]. Simulation can help cities avoid cost inefficiencies and mistakes by testing different strategies. For instance, some of the urban simulation solutions used in smart city projects include UrbanSim, which is an open-source system for land use and transportation planning. Another simulation tool, CityEngine ([[https://www.esri.com/en-us/arcgis/products/arcgis-cityengine/overview](https://www.esri.com/en-us/arcgis/products/arcgis-cityengine/overview)]([https://www.esri.com/en-us/arcgis/products/arcgis-cityengine/overview](https://www.esri.com/en-us/arcgis/products/arcgis-cityengine/overview))), is a 3D modeling software for urban environments. SUMO (Simulation of Urban MObility) is a traffic simulation package for large road networks. MATSim (Multi-Agent Transport Simulation) is a framework for agent-based transport simulations. CitySim is a tool for urban energy modeling, and ENVI-met is a microclimate model for simulating surface-plant-air interactions in urban settings.