Predicting the impact of public events and mobility in Smart Cities

• Published in: IET smart cities 2024-12, Vol.6 (4), p.253-275
• Main Authors: Bellodi, Elena, Zese, Riccardo, Petrovich, Carlo, Frascella, Angelo, Bertasi, Francesco
• Format: Article
• Language: English
• Subjects: artificial intelligence; data analytics and machine learning; data structures; governance; mobility; planning and policy; policy support system; smart cities; smart cities applications
• ISSN: 2631-7680; 2631-7680
• DOI: 10.1049/smc2.12087

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. The authors approach two issues related to people mobility, based on data available for public events and acquired from an Italian mobile phone network: (i) predicting the expected people attendance at a public event; (ii) predicting future people density in a specific geographical area based on the density in that area and in the neighbouring ones.