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- W2163063485 abstract "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." @default.
- W2163063485 created "2016-06-24" @default.
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- W2163063485 date "2013-01-01" @default.
- W2163063485 modified "2023-10-17" @default.
- W2163063485 title "Autonomous Accident Monitoring Using Cellular Network Data" @default.
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