  Network Signalling Data (NSD) have the potential to provide continuous
spatio-temporal information about the presence, mobility, and usage patterns of
cell phone services by individuals. Such information is invaluable for
monitoring large urban areas and supporting the implementation of
decision-making services. When analyzed in real time, NSD can enable the early
detection of critical urban events, including fires, large accidents,
stampedes, terrorist attacks, and sports and leisure gatherings, especially if
these events significantly impact mobile phone network activity in the affected
areas. This paper presents empirical evidence that advanced NSD can detect
anomalies in mobile traffic service consumption, attributable to critical urban
events, with fine spatial and temporal resolutions. We introduce two
methodologies for real-time anomaly detection from multivariate time series
extracted from large-scale NSD, utilizing a range of algorithms adapted from
the state-of-the-art in unsupervised machine learning techniques for anomaly
detection. Our research includes a comprehensive quantitative evaluation of
these algorithms on a large-scale dataset of NSD service consumption for the
Paris region. The evaluation uses an original dataset of documented critical or
unusual urban events. This dataset has been built as a ground truth basis for
assessing the algorithms performance. The obtained results demonstrate that our
framework can detect unusual events almost instantaneously and locate the
affected areas with high precision, largely outperforming random classifiers.
This efficiency and effectiveness underline the potential of NSD-based anomaly
detection in significantly enhancing emergency response strategies and urban
planning.
