Autonomous accident monitoring using cellular network data

• 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

Anomaly detection

Cellular networks

Crisis management

Emergency response

Mobility

Bayesian networks

Carrier mobility

Inference engines

Information systems

Sensor networks

Traffic congestion

Bayesian inference

Cellular network

Large scale sensor network

Mobile communication networks

Vehicular traffic scenarios

Accidents

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)