Retrieval effectiveness of proper name search methods
Information Processing and Management: an International Journal
Chatter on the red: what hazards threat reveals about the social life of microblogged information
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Extracting events and event descriptions from Twitter
Proceedings of the 20th international conference companion on World wide web
Semantic Traffic-Aware Routing Using the LarKC Platform
IEEE Internet Computing
Summarizing sporting events using twitter
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Applying semantic web technologies for diagnosing road traffic congestions
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
AllAboard: visual exploration of cellphone mobility data to optimise public transport
Proceedings of the 19th international conference on Intelligent User Interfaces
STAR-CITY: semantic traffic analytics and reasoning for CITY
Proceedings of the 19th international conference on Intelligent User Interfaces
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The advent of real-time traffic streaming offers users the opportunity to visualise current traffic conditions and congestion information. However, real-time information highlighting the underlying reason for tail-backs remains largely unexplored. Broken traffic lights, an accident, a large concert, or road-works reveal important information for citizens and traffic operators alike. Providing such information in real-time requires intelligent mechanisms and user interfaces in order to (i) harness heterogeneous data sources (volume, velocity, variety, veracity) and (ii) make derived knowledge consumable so users can visualize traffic conditions and congestion information making better routing decisions while travelling. This work focuses on surfacing relevant information and explaining the underlying reasons behind traffic conditions. To this end, static data from event providers, planned road works together with dynamically emerging events such as a traffic accidents, localized weather conditions or unplanned obstructions are captured through social media to provide users real-time feedback to highlight the causes of traffic congestion.