Work

From flows to events: spatiotemporal insights into Bologna’s mobility using mobile networks

Year: 2026

Type: article

Abstract: Mobile network data represent one of the most powerful emerging sources for large-scale mobility analysis. This paper proposes a complete data-driven pipeline to understand, detect, and predict population movement patterns within a region using anonymized origin–destination data provided by mobile phone operators. The dataset, covering 60 consecutive days in 2019, reports millions of daily flows between census areas together with demographic information. We conduct extensive exploratory analysis, develop a Z-score anomaly detection method capable of capturing event-driven disruptions, and design a regression model achieving a regional-scale MAPE around 10% in inbound-flow forecasting. Finally, we develop a decision-support application for a tourist municipality in the region enabling local authorities to gain insights into movement flows and make informed decisions for planning, management, and response to mobility-related events.

Source: Journal of Ambient Intelligence and Humanized Computing

DOI: 10.1007/s12652-026-05127-x

Authors: Nicola Bicocchi, Michele Arioli, Marco Mamei, Carlo Petrovich

Institutions: University of Modena and Reggio Emilia, National Agency for New Technologies, Energy and Sustainable Economic Development

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,490