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Traditional urban management is unable to keep pace with the intensification of urbanization. Challenges related to urban public management include unreasonable economic management means, unreasonable legal management means, lack of definition surrounding the government’s function in urban public management, and lags in urban management technology and equipment. This impacts the effectiveness of urban public management, hindering the formation of a harmonious and mutually beneficial social atmosphere or inclusive, sustainable urban development.
The smart communities mission aims to support towns that offer basic facilities, a good standard of living for residents, smart technologies, and a clean and sustainable environment. This article uses artificial intelligence (AI) and big data (BD) to explore strategies for optimizing sustainable development of smart city public management, hoping to provide valuable reference for relevant research.
Many scholars have studied urban management. Grossi et al. (2020) studied smart city management from the perspective of public management. Their work analyzed the basic concept of information service implementation in urban management. They also described the application of information services in urban management. Leonteva et al. (2018) compared urban management frameworks, explored the quantification of the framework, and studied its measurement indicators and weight distribution according to expert opinions. Biswas et al. (2019), after analyzing problems within the management of smart cities, offered suggestions on using digital means to manage smart cities.
According to Brandt et al. (2018), there are several obstacles in the use of smart buildings. These include the high price of developing software, lack of adaptability, mismanagement, safety, human resources, and energy consumption. Future urban ecosystems will be impacted by four primary factors: (1) growing populations; (2) industrialization; (3) infrastructure improvements; and (4) transformation of communication systems.
Du et al. (2018) discussed how to select existing algorithms when monitoring smart city applications. These efforts improve urban planning, governance, structural health monitoring, water pipe networks, traffic monitoring, and environmental monitoring. Cheela et al. (2021) formulated an indicative strategy for the development of urban waste management systems, planning an integrated solid waste management system for smart cities. Saberifar (2020) discussed the influencing factors of urban management intelligent organization model design, proposing the strategy of model design.
BD has been widely used in urban management. Xiao et al. (2021) explored the modern method of urban management with the use of blockchain and BD. Nica (2021) conducted an empirical study on urban BD analysis and sustainable governance network in integrated smart city planning and management. A city’s intelligence must be evaluated both qualitatively and quantitatively. Evaluations include the way people live and govern, how people provide and receive energy resources, how people move and use transportation, types of business, and how they improve roads.
Chen et al. (2019) introduced the composition, characteristics, and application of BD in the transportation field. His work proposed a visual model of a self-organizing feature map neural network based on the graph theory. It provided technical support for guiding urban road planning and improving urban management level. Chen et al. (2019) analyzed the role of urban planning and urban management in achieving sustainable development goals. According to Wu et al. (2020), urban planning is a method to achieve objectives through improved approaches and tactics.
A separate matter is city administration. While urban planning is a strategy, city administration is a set of steps. Engin et al. (2020) made an interdisciplinary synthesis of the development, opportunities, and challenges of urban management and planning under the ongoing “digital revolution.” After analyzing problems in urban noise management, Navarro et al. (2017) proposed a BD framework for the correct analysis of massive noise monitoring data. Susmitha and Jayaprada (2017) used BD to analyze the planning and management of smart cities, describing BD’s role in solving challenges faced by smart cities.