A smart city leverages the Internet of Things (IoT) and sensors to collect the available wealth of raw data from various urban surroundings. This huge volume of unstruc- tured data gleaned in real time needs to be effectively analysed and utilised to spot trends, which can give city planners the information that is highly responsive to the needs of the citizens. The massive amount of information presented in this data is difficult to be viewed and processed by humans. Here, the information retrieval via machine learning (ML) helps in extracting knowledge or structured data from the unstructured form by recognising the underlying pattern. It produces a sum- marised tabular output in a relational database, which helps one to optimise the given set of services for enhanced functioning and sustainability of the city, such as pre- dicting parking spots for drivers, helping first responders, and locating dangerous intersections.
The factors responsible for surging interest in ML are powerful computational processing and cost-effective data storage options, which allow training models that gain experience by quickly and accurately analysing huge chunks of complex data. ML combined with the IoT helps to realise the vision of a more livable and resilient city that is capable of quickly responding to the critical challenges prompted by an outrageous urban population, encompassing traffic congestion, environment deterioration, sanitation issues, energy crises, thwart crime, healthcare, and many more. It can automate municipal operations and advance smart city initiatives at large.
In this chapter, a comprehensive list of applications is curated to understand the nuts-and-bolts of ML in the domain of the smart city. The chapter walks through the recent applied examples alongside familiarising with the key research developments in the context of ML-assisted smart cities. Ultimately, the chapter concludes by men- tioning the major challenges faced by the implication of ML as a smart city use case. On that account, we are focusing on various examples of ML in a smart city.