SODA

Autonomous Accident Monitoring Using Cellular Network Data

Görnerup, Olof and Kreuger, Per and Gillblad, Daniel (2013) Autonomous Accident Monitoring Using Cellular Network Data. In: 10th International Conference on Information Systems for Crisis Response and Management (ISCRAM), 12-15 May. (In Press)

[img]
Preview
PDF - Accepted Version
1445Kb

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.

Item Type:Conference or Workshop Item (Paper)
ID Code:5466
Deposited By:Olof Görnerup
Deposited On:13 Mar 2013 10:15
Last Modified:13 Mar 2013 10:15

Repository Staff Only: item control page