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.

翻译：网络信令数据（NSD）能够持续提供关于个体手机服务使用、移动轨迹及存在模式的时空信息。此类信息对于大范围城市区域的监测以及决策支持服务的实施具有不可估量的价值。通过实时分析，NSD可实现城市重大事件的早期检测，包括火灾、重大事故、人群踩踏、恐怖袭击以及体育与休闲集会等，尤其是在这些事件显著影响事发区域移动电话网络活动的情况下。本文通过实证表明，先进的NSD能够以精细的时空分辨率检测因城市重大事件导致的移动流量服务消耗异常。我们提出了两种从大规模NSD提取的多元时间序列中进行实时异常检测的方法，该方法采用了一系列源自无监督机器学习异常检测前沿技术的改进算法。我们的研究基于巴黎地区大规模NSD服务消耗数据集，对这些算法进行了全面的量化评估。评估使用了一个由已记录的城市重大或异常事件构成的原创数据集，该数据集被构建为评估算法性能的基准真值。实验结果表明，我们所提出的框架能够近乎即时地检测异常事件，并以高精度定位受影响区域，其性能显著优于随机分类器。这种高效性与有效性凸显了基于NSD的异常检测在显著提升应急响应策略与城市规划方面的潜力。