@article{ref1,
title="Real-time detection of traffic from Twitter stream analysis",
journal="International journal of innovative science, engineering and technology",
year="2016",
author="Khandge, Ruchita and Sultan, Shekh and Khan, Firdos and Kumari, Sweety and Jadhao, Amol",
volume="2",
number="9",
pages="685-691",
abstract="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, &quot;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, &quot;ET: Events from tweets,&quot; 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,&quot;Measurement and analysis of online social networks,&quot; in Proc. 7th ACM SIGCOMM Conf. Internet Meas., San Diego, CA USA, 2007, pp. 29–42.  [4] G. Anastasi et al., &quot;Urban and social sensing for sustainable mobility in smart cities,&quot; in Proc. IFIP/IEEE Int. Conf. Sustainable Internet ICT Sustainability, Palermo, Italy, 2013, pp. 1–4.  [5] A. Rosi et al., &quot;Social sensors and pervasive services: Approaches and perspectives,&quot; in Proc. IEEE Int. Conf.PERCOM Workshops, Seattle,WA, USA, 2011, pp. 525–530.  [6] T Sakaki, M. Okazaki, and Y.Matsuo, &quot;Tweet analysis for real-time event detection and earthquake reporting system development,&quot; 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, &quot;An intelligent driver guidance tool using Location based services,&quot; in Proc. IEEE ICSDM, Fuzhou, China, 2011, pp. 246–251.  [9] T. Sakaki, Y. Matsuo, T. Yanagihara, N. P. Chandrasiri, and K. Nawa, &quot;Real-time event extraction for driving information from social sensors,&quot; In Proc. IEEE Int. Conf. CYBER, Bangkok, Thailand, 2012, pp. 221–226.  [10] V. Gupta, S. Gurpreet, and S. Lehal, &quot;A survey of text mining techniques and applications,&quot; J. Emerging Technol. Web Intell., vol. 1, no. 1, pp. 60–76, Aug. 2009.  Keywords: Twitter-Traffic-Status <p /> <p>Language: en</p>",
language="en",
issn="2348-7968",
doi="",
url="http://dx.doi.org/"
}