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Autonomous Accident Monitoring Using Cellular Network Data
RISE, Swedish ICT, SICS. IAM.
RISE, Swedish ICT, SICS. IAM.
RISE, Swedish ICT, SICS. IAM.
Number of Authors: 3
2013 (English)Conference paper (Refereed)
Abstract [en]

Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions.

Place, publisher, year, edition, pages
2013, 8.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-24157OAI: oai:DiVA.org:ri-24157DiVA: diva2:1043236
Conference
10th International Conference on Information Systems for Crisis Response and Management (ISCRAM)
Projects
Monitoring and robust operation of cellular networks
Available from: 2016-10-31 Created: 2016-10-31

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Type fulltextMimetype application/pdf

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ReferencesLink to record
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