Manuscript Number : IJSRSET1626174

Traffic Events Detection from Status Updated Messages of Twitter

Authors(2) :-Bathula Vasundhra, P. Sreehari

Now a Days the Social networks are also used as a source of information for events detection, with particular position to road traffic details and accidents etc. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the suitable class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value by solving a binary classification problem (traffic versus nontraffic tweets).

Authors and Affiliations

Bathula Vasundhra
PG Scholar, Department of CSE, PACE Institute of Tech and Sciences, Vallur, Ongole, Andhra Pradesh, India
P. Sreehari
Assistant Professor, Department of CSE, PACE Institute of Tech and Sciences, Vallur, Ongole, Andhra Pradesh, India

Traffic Event Detection, Tweet Classification, Text Mining, Social Sensing.

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Publication Details

Published in : Volume 3 | Issue 1 | January-February 2017
Date of Publication : 2017-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 23-29
Manuscript Number : IJSRSET1626174
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Bathula Vasundhra, P. Sreehari, " Traffic Events Detection from Status Updated Messages of Twitter, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 1, pp.23-29, January-February-2017. Journal URL : https://res.ijsrset.com/IJSRSET1626174

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