Work

Autonomous Accident Monitoring Using Cellular Network Data

Year: 2013

Type: conference-paper

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.

Source: KTH Publication Database DiVA (KTH Royal Institute of Technology)

Authors: Olof Görnerup, Per Kreuger, Daniel Gillblad

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W2163063485

FWCI: 0.402

Cites: 19

Cited by: 1

Related to: 10