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Ontime traffic
Ontime traffic











ontime traffic

Towards detecting influenza epidemics by analyzing Twitter messages. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM’12). Detecting offensive tweets via topical feature discovery over a large scale Twitter corpus. Guang Xiang, Bin Fan, Ling Wang, Jason I.

ontime traffic

In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. Tweetmotif: Exploratory search and topic summarization for Twitter.

  • Brendan O'Connor, Michel Krieger, and David Ahn.
  • ontime traffic ontime traffic

    In Proceedings of the 2012 International Workshop on Smart Health and Wellbeing (SHB’12). Towards large-scale Twitter mining for drug-related adverse events. Jiang Bian, Umit Topaloglu, and Fan Yu.Identifying health-related topics on Twitter. Smith, Christophe Giraud-Carrier, and Carl L. Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. You are what you tweet: Analyzing Twitter for public health. In Proceedings of the NIPS Workshop on Applications for Topic Models: Text and Beyond. Applications of topics models to analysis of disaster-related Twitter data. Kirill Kireyev, Leysia Palen, and Kenneth M.IEEE Transactions on Knowledge and Data Engineering 25, 4, 919-931. Tweet analysis for real-time event detection and earthquake reporting system development. Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo.In Proceedings of the International Conference on Social Computing (SocialCom’13). Multi-tweet summarization of real-time events. In Proceedings of the 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems. Real-time event extraction for driving information from social sensors. Takeshi Sakaki, Yutaka Matsuo, Tadashi Yanagihara, Naiwala P.In Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics. A generation method of filtering rules of Twitter via smartphone based Participatory Sensing system for tourist by interactive GHSOM and C4.5. Using social media to enhance emergency situation awareness. Jie Yin, Andrew Lampert, Bella Robinson, and Robert Power.IEEE Transactions on Intelligent Transportation Systems 16, 4, 2269-2283. Real-time detection of traffic from Twitter stream analysis. Eleonora D'Andrea, Pietro Ducange, Beatrice Lazzerini, and Francesco Marcelloni.Transportation Research Part C 67, 321-342. From Twitter to detector: Real-time traffic incident detection using social media data. Yiming Gu, Zhen (Sean) Qian, and Feng Chen.Illinois Department of Transportation.Journal of the Institute of Telecommunications Professionals 5, 4, 34-38. The system is evaluated and shown to perform better than related approaches. The article proposes an instant traffic alert and warning system based on a novel latent Dirichlet allocation (LDA) approach (“tweet-LDA”).

    #ONTIME TRAFFIC HOW TO#

    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. This article looks at the exploitation of Twitter data in the traffic reporting domain. One domain where social media data can add value is transportation and traffic management. Social media has become an important source of near-instantaneous user-generated information that can be shared and analyzed to support better decision making. Shared information can come from many sources, particularly, but not exclusively, from sensors and social media. Smart communities are composed of groups, organizations, and individuals who share information and make use of that shared information for better decision making.













    Ontime traffic