Massive urbanization and resource scarcity are global phenomena for which researchers around the world see urban transformation into a smart city as the most viable solution. Yet urban planners face many hurdles in transforming cities. These include economic factors such as the high cost of infrastructure development and maintenance, social paradigms, governance, and environmental issues that limit government ambitions. To design an optimal strategy for creating a smart city, the various barriers must be identified and prioritized. The various barriers to the successful implementation of the smart city mission were identified using an extensive literature review and expert opinions. To understand the complex interdependencies among these barriers, the interpretive structural modeling method (ISM) was used. MICMAC analyzed the ISM model to classify the barriers. Indian cities vary greatly in terms of infrastructure, size, population, and available facilities. Therefore, the impact of the barriers and their interdependencies also vary. The author has extended the study and used the fuzzy approach MICMAC to improve the model by incorporating the variations observed when the city category changes. This study will help urban developers and planners to identify the relationship between barriers and design strategic planning to make the city smarter.

大规模城市化和资源稀缺是全球现象，世界各地的研究人员将城市转型为智慧城市视为最可行的解决方案。然而，城市规划者在改造城市时面临许多障碍。其中包括经济因素，例如基础设施开发和维护的高成本、社会范式、治理以及限制政府雄心的环境问题。为了设计创建智慧城市的最佳策略，必须确定各种障碍并确定其优先级。通过广泛的文献回顾和专家意见，确定了成功实施智慧城市使命的各种障碍。为了理解这些障碍之间复杂的相互依赖关系，使用了解释性结构建模方法（ISM）。MICMAC 分析了 ISM 模型以对障碍进行分类。印度城市在基础设施、规模、人口和可用设施方面差异很大。因此，障碍的影响及其相互依存性也各不相同。作者扩展了研究并使用模糊方法MICMAC通过结合城市类别变化时观察到的变化来改进模型。这项研究将帮助城市开发商和规划者确定障碍和设计战略规划之间的关系，以使城市更智能。作者扩展了研究并使用模糊方法MICMAC通过结合城市类别变化时观察到的变化来改进模型。这项研究将帮助城市开发商和规划者确定障碍和设计战略规划之间的关系，以使城市更智能。作者扩展了研究并使用模糊方法MICMAC通过结合城市类别变化时观察到的变化来改进模型。这项研究将帮助城市开发商和规划者确定障碍和设计战略规划之间的关系，以使城市更智能。