Real-Time traffic event detection from social media

Di Wang, Ahmad Al-Rubaie, Sandra Stinčić Clarke, John Davies

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Smart communities are composed of groups, organizations, and individuals who share information and make use of that shared information for better decision making. Shared information can come from many sources, particularly, but not exclusively, from sensors and social media. Social media has become an important source of near-instantaneous user-generated information that can be shared and analyzed to support better decision making. One domain where social media data can add value is transportation and traffic management. This article looks at the exploitation of Twitter data in the traffic reporting domain. A key challenge is how to identify relevant information from a huge amount of user-generated data and then analyze the relevant data for automatic geocoded incident detection. The article proposes an instant traffic alert and warning system based on a novel latent Dirichlet allocation (LDA) approach ("tweet-LDA"). The system is evaluated and shown to perform better than related approaches.

Original languageBritish English
Article number9
JournalACM Transactions on Internet Technology
Volume18
Issue number1
DOIs
StatePublished - Nov 2017

Keywords

  • Incremental learning
  • Latent dirichlet allocation (lda)
  • Text mining
  • Traffic alert system
  • Tweet mining

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