Demographic growth in urban areas means that modern cities face challenges in ensuring a steady supply of water and electricity, smart transport, livable space, better health services, and citizens’ safety. Advances in sensing, communication, and digital technologies promise to mitigate these challenges. Hence, many smart cities have taken a new step in moving away from internal information technology (IT) infrastructure to utility-supplied IT delivered over The Internet. The benefit of this move is to manage The vast amounts of data generated by The various city systems, including water and electricity systems, The waste management system, transportation system, public space management systems, health and education systems, and many more. Furthermore, many smart city applications are time-sensitive and need to quickly analyze data to react promptly to The various events occurring in a city. The new and emerging paradigms of edge and fog computing promise to address big data storage and analysis in The field of smart cities. Here, we review existing service delivery models in smart cities and present our perspective on adopting these two emerging paradigms. We specifically describe The design of a fog-based data pipeline to address The issues of latency and network bandwidth required by time-sensitive smart city applications.
Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the United States. These figures indicate the rapid rise of industrial cities and the urgent need to move from traditional cities to smart cities. This article provides a survey of different approaches and technologies such as intelligent transportation systems (ITS) that leverage communication technologies to help maintain road users safe while driving, as well as support autonomous mobility through the optimization of control systems. The role of ITS is strengthened when combined with accurate artificial intelligence models that are built to optimize urban planning, analyze crowd behavior and predict traffic conditions. AI-driven ITS is becoming possible thanks to the existence of a large volume of mobility data generated by billions of users through their use of new technologies and online social media. The optimization of urban planning enhances vehicle routing capabilities and solves traffic congestion problems, as discussed in this paper. From an ecological perspective, we discuss the measures and incentives provided to foster the use of mobility systems. We also underline the role of the political will in promoting open data in the transport sector, considered as an essential ingredient for developing technological solutions necessary for cities to become healthier and more sustainable.
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