|
Abstract
|
Social networks have been, at a recent time utilized as a source of information for event detection, with particular referral to road traffic congestion and for the car accidents. In this paper, we present a real-time monitoring system for traffic event detection from the twitter stream analysis. The system fetches tweets from the twitter according to several search criteria; processes tweets, by applying the text mining techniques; and finally performs the classification of tweets. The aim is to assign the suitable class label to each tweet, as relevant to a traffic event or not. The traffic detection system was exploited for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, generally before online traffic news web sites. We employed the support vector machine as a classification model, and we attain an accuracy value of 95.75% by solving a binary classification problem (traffic versus non-traffic tweets). We would also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification issue and obtaining accuracy value of 88.89%.
Keywords- Twitter; Traffic event detection; tweet classification; text mining, social sensing.
Keywords: Twitter-Traffic-Status
This article may be viewed at:
http://www.isroset.org/pub_paper/IJSRCSE/9-ISROSET-IJSRCSE-2018-2.pdf
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]. F. Atefeh and W. Khreich, "A survey of techniques for event detection in Twitter," Comput. Intell., vol. 31, no. 1, pp. 132– 164, 2015
[2]. P. Ruchi and K. Kamalakar, "ET: Events from tweets," in Proc. 22nd Int. Conf. World Wide Web Comput., Rio de Janeiro, Brazil, 2013, pp. 613–620.
[3]. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, "Measurement and analysis of online social networks," in Proc. 7th ACM SIGCOMM Conf. Internet Meas., San Diego, CA, USA, 2007, pp. 29–42.
[4]. The Smarty project. [Online]. Available: http://www.smarty.toscana.it/
[5]. 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.
[6]. 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.
[7]. 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.
[8]. T. Sakaki, Y. Matsuo, T. Yanagihara, N. P. Chandrasiri, and K. Nawa, "Real-time event extraction for driving information from social sensors," in Proc. IEEE Int. Conf. CYBER, Bangkok, Thailand, 2012,pp. 221– 226.
[9]. N. Wanichayapong, W. Pruthipunyaskul, W. Pattara-Atikom, and P. Chaovalit, "Social-based traffic information extraction and classification," in Proc. 11th Int. Conf. ITST, St. Petersburg, Russia, 2011, pp. 107– 112.
[10]. A. Schulz, P. Ristoski, and H. Paulheim, "I 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: SpringerVerlag, 2013, pp. 22–33.
[11]. P . Agarwal, R. V aithiyanathan, S. Sharma, and G. Shro, "Catching the long-tail: Extracting local news events from Twitter," in Proc. 6th AAAI ICWSM, Dublin, Ireland, Jun. 2012, pp. 379–382.
Language: en |