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Smart city article details

Title	Predicting The Impact Of Public Events And Mobility In Smart Cities
ID_Doc	42750
Authors	Bellodi E.; Zese R.; Petrovich C.; Frascella A.; Bertasi F.
Year	2024
Published	IET Smart Cities, 6, 4
DOI	http://dx.doi.org/10.1049/smc2.12087
Abstract	The ubiquitous presence of smartphones and the ever-expanding Internet of Things are generating a treasure trove of data on human movement. We harness the power of Artificial Intelligence to extract knowledge within this data, in particular for predicting people flows and density in a Smart City. This predictive ability holds immense potential for a multitude of applications, from optimising people flow to streamlining event planning, while offering a powerful tool for pre-emptive identification of situations that may lead to crowd disasters. In this paper, we tackle two crucial aspects of people mobility using data from public events and an Italian mobile phone network: to predict both event attendance and future crowd density in specific areas. The event details (location, time etc.) are automatically gathered and stored in a structured format. Next, we handle these problems are treated in a “supervised learning” setting, and various state-of-art Machine Learning techniques are tested to find the best model for each task. The obtained models will be encapsulated into a Policy Support System contributing to foster planning actions of mobility services. © 2024 The Author(s). IET Smart Cities published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Author Keywords	artificial intelligence; data analytics and machine learning; data structures; governance; mobility; planning and policy; policy support system; smart cities; smart cities applications

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