From flows to events: spatiotemporal insights into Bologna’s mobility using mobile networks
TL;DR
This paper conducts extensive exploratory analysis, develops a Z-score anomaly detection method capable of capturing event-driven disruptions, and designs a regression model achieving a regional-scale MAPE around 10% in inbound-flow forecasting.
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.