Inferring Unusual Crowd Events from Mobile Phone Call Detail Records

  • Yuxiao Dong
  • Fabio Pinelli
  • Yiannis Gkoufas
  • Zubair Nabi
  • Francesco Calabrese
  • Nitesh V. Chawla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)


The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behavior in urban environments. Cities can leverage such knowledge to provide better services (e.g., public transport planning, optimized resource allocation) and safer environment. 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 propose a methodology that is able to detect unusual events from CDR data, which 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 state-of-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.


Mobile Phone Unusual Event Defense Advance Research Project Agency Existence Probability Current Crowd 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yuxiao Dong
    • 1
  • Fabio Pinelli
    • 2
  • Yiannis Gkoufas
    • 2
  • Zubair Nabi
    • 2
  • Francesco Calabrese
    • 2
  • Nitesh V. Chawla
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Notre DameNotre DameUSA
  2. 2.IBM ResearchMulhuddartIreland

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