Artificial Intelligence and Machine Learning Approaches in Smart City Services

Introduction

Artificial intelligence (AI) approaches such as machine learning (ML), deep learning (DL), are embrace a much of promises for handling activities and automation in smart cities. The aim of the smart cities is to offer efficient services to the public by implementing modern data analytics and technologies (Ullah et al., 2020). There are several benefits of smart cities such as enhance city planning and development, E-government services delivered faster and at a lower operating to citizens, enhance safety and security, manage waste for energy and fuel, manage waste water to be treated, smart meters, smart grid and many more (Pradeep et al., 2019).

By 2030, AI is predicted to allow over 50% of smart cities services, amongst which urban solutions for mobility, significantly contributing to sustainability, vitality, resilience, and social welfare of urban life. Using AI and ML in smart cities will improve the personalize services delivery, help makes expectation and forthcoming services predictions. Also, simulate the utilization of different strategies beforehand applied (Pradeep et al., 2019). Moreover, it will enhance the city’s finance management via predict target and expenditure management. ML and DL in smart city would completely influence the environment through waste, traffic, and energy management. Furthermore, it would increase production of employees with the assistance of effective services and products which will increase the economic capital growing of the city’s (Elmustafa et al., 2021).

Figure 1.

Classification of AI technologies in a smart city

AI allowing smart cities solutions including noise and traffic congestion, more efficient energy, water and waste management, reduced pollution. It is the science of simulating intelligence in machines and program them to emulate human actions. As shown in Figure 1, AI technologies in smart cities could be classified into AI/ML, natural speech, expert system, expert systems, robotics and language processing (Elmustafa et al., 2021). The major goals of AI are learning, reasoning and perception. The different growing field of AI used in many fields including medical, robot control, finance, remote sensing, semantic web, computer vision, virtual reality, game theory and more fields. AI is predicted to support sustainable development of future smart cities (Ullah et al., 2020). AI approaches such as ML and DL enable to provide many intelligent services in smart cities which could be classified in to seven categories as follows:

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  For governance including tailored funding provisioning, urban planning, disaster management, and prevention (Mansour, 2021).

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  For live ability, healthcare, safety, and security including smart monitoring, noise management, personalized healthcare, and enhanced cyber security (Mansour, 2021).

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  For education and citizen participation including actionable and validated knowledge support for making decisions (Nada, 2022).

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  For economy including resources efficiency and enhanced affordability through, customers tailored solutions, effective supply chain and sharing services (Aswathy, 2022).

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  For mobility and logistics including smart routing and parking assistance, supply chain resiliency and traffic management, autonomous and sustainable mobility (Nada, 2022).

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  For infrastructure including optimized infrastructure deployment, use and maintenance as well as transportation, energy grids, waste and water management, and urban lighting (Aswathy, 2022).

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  For the environment including air quality management, biodiversity preservation and urban farming (Nada, 2022).