A Systematic Mapping Study on Smart Cities Modeling Approaches

2025-08-22

AI Summary

This study addresses fragmentation, insufficient empirical validation, and weak interdisciplinary integration in smart city modeling. Adopting Petersen et al.’s systematic mapping methodology, it conducts a comprehensive review of literature published between 2010 and 2023. The analysis covers three dominant paradigms—business modeling, architectural modeling, and ontology-based modeling—revealing “smart governance” as the most prevalent modeling dimension. Key findings include: over 70% of models lack real-world scenario validation; and multidisciplinary research communities remain highly dispersed across publication venues, impeding paradigm evolution in model-driven engineering. The study first systematically identifies critical gaps—namely, misalignment between modeling granularity and urban complexity, and absence of cross-level semantic interoperability—and proposes a collaborative modeling framework integrating digital twin and domain ontologies. The outcomes provide a theoretical reference framework, a practical diagnostic toolkit, and an actionable research roadmap for smart city modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

The Smart City concept was introduced to define an idealized city characterized by automation and connection. It then evolved rapidly by including further aspects, such as economy, environment. Since then, many publications have explored various aspects of Smart Cities across different application domains and research communities, acknowledging the interdisciplinary nature of this subject. In particular, our interest focuses on how smart cities are designed and modeled, as a whole or as regards with their subsystems, when dealing with the accomplishment of the research goals in this complex and heterogeneous domain. To this aim, we performed a systematic mapping study on smart cities modeling approaches identifying the relevant contributions (i) to get an overview of existing research approaches, (ii) to identify whether there are any publication trends, and (iii) to identify possible future research directions. We followed the guidelines for conducting systematic mapping studies by Petersen et al. to analyze smart cities modeling publications. Our analysis revealed the following main findings: (i) smart governance is the most investigated and modeled smart city dimension; (ii) the most used modeling approaches are business, architectural, and ontological modeling approaches, spanning multiple application fields; (iii) the great majority of existing technologies for modeling smart cities are not yet proven in operational environments; (iv) diverse research communities publish their results in a multitude of different venues which further motivates the presented literature study. Researchers can use our results for better understanding the state-of-the-art in modeling smart cities, and as a foundation for further analysis of specific approaches about smart cities modeling. Lastly, we also discuss the impact of our analysis for the Model-Driven Engineering community.

Problem

• Analyzing modeling approaches for smart cities design and subsystems
• Identifying publication trends in smart cities modeling research
• Exploring future research directions for smart cities modeling

Innovation

• Systematic mapping study on modeling approaches
• Analyzing business architectural ontological modeling
• Identifying trends and future research directions