TY  - JOUR
PY  - 2016//
TI  - Real-time detection of traffic from Twitter stream analysis
JO  - International journal of innovative science, engineering and technology
A1  - Khandge, Ruchita
A1  - Sultan, Shekh
A1  - Khan, Firdos
A1  - Kumari, Sweety
A1  - Jadhao, Amol
SP  - 685
EP  - 691
VL  - 2
IS  - 9
N2  - Twitter has received much thoughtfulness recently.In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. An important characteristic of Twitter is its real-time nature. The system fetches tweets from Twitter by using many search criteria; processes tweets, by usinging text mining techniques and then performs the classification of tweets. To detect a target event, we devise a classifier of tweets based on features like keywords in a tweet, the number of words, and their context. Users are using Twitter to report real-life events. It focuses on detecting those events by analyzing these text stream in Twitter.The characteristics of Twitter make it a non-trivial task.The traffic detection system was employed for real-time monitoring of many areas of the road network, that allow for detection of traffic events almost in real time.     Keywords: Twitter, Traffic event detection, tweet classification, text mining, social sensing.    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. 22ndnt 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] G. Anastasi et al., "Urban and social sensing for sustainable mobility in smart cities," in Proc. IFIP/IEEE Int. Conf. Sustainable Internet ICT Sustainability, Palermo, Italy, 2013, pp. 1–4.    [5] A. Rosi et al., "Social sensors and pervasive services: Approaches and perspectives," in Proc. IEEE Int. Conf.PERCOM Workshops, Seattle,WA, USA, 2011, pp. 525–530.    [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] J. Allan, Topic Detection and Tracking: Event-Based Information Organization. Norwell, MA, USA: Kluwer, 2002.    [8] K. Perera and D. Dias, "An intelligent driver guidance tool using Location based services," in Proc. IEEE ICSDM, Fuzhou, China, 2011, pp. 246–251.    [9] 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.    [10] V. Gupta, S. Gurpreet, and S. Lehal, "A survey of text mining techniques and applications," J. Emerging Technol. Web Intell., vol. 1, no. 1, pp. 60–76, Aug. 2009.    Keywords: Twitter-Traffic-Status  <p />  <p>Language: en</p>
LA  - en
SN  - 2348-7968
UR  - http://dx.doi.org/
ID  - ref1
ER  -