Traffic detection using tweets on Twitter social network

Now a days social networking sites are becoming the real time information channel over time. With the help of portable devices many users are able to share the real life events on the internet. This feature of the social networking sites made them more popular and valuable. Basically these social networking sites are used for the maintaining social relationship, finding friends and users with the similar interest . The text message shared by user in social networks that message is called Status Update Message. In that SUM the text, meta-information like timestamp, name of the user, geographic coordinates, links of the other resources are present. The SUM's considered in a specific area may provide the proper information. Social networks is a source of information for event detection such as road traffic congestion and car accidents. Existing system present a real-time monitoring system for traffic event detection from twitter. The system fetches or collect tweets from twitter and then processes tweets using text mining techniques. Lastly performs the classification of tweets. The system aim is to assign the appropriate class label to each tweet, whether it is related to a traffic event or not. System used the support vector machine as a classification model. The proposed system uses the semi-supervised approach, which gives training using traffic related dataset. We propose a clustering approach for classification of the tweets in traffic related and non- traffic related tweets. We employ a Euclidean distance to calculate the similarity between the tweets. Keywords: Tweet classification, Traffic event detection, Data mining, text mining, and social sensing. Keywords: Twitter-Traffic-Status Language: en SafetyLit note: We believe that the inclusion of references in this case falls under fair use. Why? 1) This article was published as open access and the journal and copyright owner is acknowledged; 2) a link to full text is provided; 3) reproduction of this reference list is relevant to a commentary on what is and is not original thought and 4) the items in this reference list are part of the data upon which an investigation are based. References 1. Eleonora D' Andrea, Pietro Ducange, Beatrice Lazzerini, Member, IEEE, and Francesco Marcelloni, Member, IEEE ,"Real-Time Detection of Traffic FromTwitter Stream Analysis",IEEE transaction on intelligent transportation system, VOL. 16, NO. 4, AUGUST 2015. 2. Rui LI, Kin Hou Lei, Ravi Khadiwala, Kevin Chen-Chuan Chang "TEDAS: a Twitter Based Event Detection and Analysis System", IJCSIT 2014. 3. Harshita Rajwani, Srushti Somvanshi, AnujaUpadhye," Dynamic Traffic Analyzer Using Twitter", International Journal of Science and Research (IJSR) 2014. 4. Vikram Singh and Balwinder Saini "An Effective Tokenization Algorithm for Information Retrieval System" CS and IT-CSCP 2014 5. Maximilian Walther and Michael Kaisser,"Geo-spatial Event Detection in the Twitter Stream", P. Serdyukov et al. (Eds.): ECIR 2013, LNCS 7814, pp. 356367, 2013 . springer Verlag Berlin Heidelberg 2013. 6. T. Sakaki, M. Okazaki, and Y.Matsuo, "Tweet analysis for real-time event detection and earthquake reporting system development," IEEE Trans. Knowl. Data Eng., vol. 25, no. 4, pp. 919-931, Apr. 2013. 7. M. Krstajic, C. Rohrdantz, M. Hund, and A. Weiler, "Getting there first: Real-time detection of real-world incidents on Twitter" in Proc. 2nd IEEE Work Interactive Vis. Text Anal. -Task-Driven Anal. Soc. Media IEEE VisWeek," Seattle, WA, USA, 2012. 8. A. Schulz, P. Ristoski, and H. Paulheim, " see a car crash: Real-time detection of small scale incidents in microblogs," in The Semantic Web: ESWC 2013 Satellite Events, vol. 7955. Berlin, Germany: Springer- Verlag, 2013, pp. 22-33. 9. J. Yin, A. Lampert, M. Cameron, B. Robinson, and R. Power, "Using social media to enhance emergency situation awareness," IEEE Intell. Syst. , vol. 27, no. 6, pp. 52-59, Nov./Dec. 2012. 10. K. Boriboonsomsin, M. Barth, W. Zhu, and A. Vu, "Ecorouting navigation multisource historical and real-time traffic information," IEEE Trans. Intell. Transp. Syst. , vol. 13, no. 4, pp. 1694-1704, Dec. 2012.