Inferring Unusual Crowd Events From Mobile Phone Call Detail Records.

Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla
Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD)
Publication Date: 
September, 2015

The pervasiveness and availability of mobile phone data offer the opportunity of discovering
usable knowledge about crowd behaviors in urban environments. Cities can
leverage such knowledge in order to provide better services (e.g., public transport
planning, optimized resource allocation) and safer cities. Call Detail Record (CDR)
data represents a practical data source to detect and monitor unusual events considering
the high level of mobile phone penetration, compared with GPS equipped
and open devices. In this paper, we provide a methodology that is able to detect
unusual events from CDR data that typically has low accuracy in terms of space and
time resolution. Moreover, we introduce a concept of unusual event that involves a
large amount of people who expose an unusual mobility behavior. Our careful consideration
of the issues that come from coarse-grained CDR data ultimately leads
to a completely general framework that can detect unusual crowd events from CDR
data effectively and efficiently. Through extensive experiments on real-world CDR
data for a large city in Africa, we demonstrate that our method can detect unusual
events with 16% higher recall and over 10 times higher precision, compared to stateof-the-art
methods. We implement a visual analytics prototype system to help end
users analyze detected unusual crowd events to best suit different application scenarios.
To the best of our knowledge, this is the first work on the detection of unusual
events from CDR data with considerations of its temporal and spatial sparseness and
distinction between user unusual activities and daily routines.