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Traffic Events Detection from Status Updated Messages of Twitter


Bathula Vasundhra, P. Sreehari
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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).

Bathula Vasundhra, P. Sreehari

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

  1. Atefeh and W. Khreich, "A survey of techniques for event detection in Twitter," Comput. Intell., vol. 31, no. 1, pp. 132–164, 2015.
  2. 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. 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. 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. 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. 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. Allan, Topic Detection and Tracking: Event-Based Information Organization. Norwell, MA, USA: Kluwer, 2002.
  8. Perera and D. Dias, "An intelligent driver guidance tool using location based services," in Proc. IEEE ICSDM, Fuzhou, China, 2011, pp. 246–251.
  9. 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. Chen and H. H. Cheng, "A review of the applications of agent technology in traffic and transportation systems," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 2, pp. 485–497, Jun. 2010. 11A. Gonzalez, L. M. Bergasa, and J. J. Yebes, "Text detection and recognition on traffic panels from street-level imagery using visual appearance," IEEE Trans. Intell. Transp. Syst., vol. 15, no. 1, pp. 228–238, Feb. 2014.
  11. Wanichayapong, W. Pruthipunyaskul, W. Pattara-Atikom, and P. Chaovalit, "Social-based traffic information extraction and classifi- cation," in Proc. 11th Int. Conf. ITST, St. Petersburg, Russia, 2011, pp. 107–112.
  12. M. d’Orey and M. Ferreira, "ITS for sustainable mobility: A survey on applications and impact assessment tools," IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 477–493, Apr. 2014.
  13. Boriboonsomsin, M. Barth, W. Zhu, and A. Vu, "Eco-routing navigation system based on multisource historical and real-time traffic information," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 4, pp. 1694–1704, Dec. 2012.
  14. Hurlock and M. L. Wilson, "Searching twitter: Separating the tweet from the chaff," in Proc. 5th AAAI ICWSM, Barcelona, Spain, 2011, pp. 161–168.
  15. Weng and B.-S. Lee, "Event detection in Twitter," in Proc. 5th AAAI ICWSM, Barcelona, Spain, 2011, pp. 401–408.
  16. Weiss, N. Indurkhya, T. Zhang, and F. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information. Berlin, Germany: Springer-Verlag, 2004.
  17. Hotho, A. Nürnberger, and G. Paaß, "A brief survey of text mining," LDV Forum-GLDV J. Comput. Linguistics Lang. Technol., vol. 20, no. 1, pp. 19–62, May 2005. ‘
  18. 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.
  19. Ramanathan and T. Meyyappan, "Survey of text mining," in Proc. Int. Conf. Technol. Bus. Manage., Dubai, UAE, 2013, pp. 508–514.
  20. W. Berry and M. Castellanos, Survey of Text Mining. New York, NY, USA: Springer-Verlag, 2004.
  21. Takemura and K. Tajima, "Tweet classification based on their lifetime duration," in Proc. 21st ACM Int. CIKM, Shanghai, China, 2012, pp. 2367–2370.
  22. The Smarty project. Online]. Available: http://www.smarty.toscana.it/
  23. 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.
  24. 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.
  25. Chew and G. Eysenbach, "Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak," PLoS ONE, vol. 5, no. 11, pp. 1–13, Nov. 2010.
  26. De Longueville, R. S. Smith, and G. Luraschi, "OMG, from here, I can see the flames!: A use case of mining location based social networks to acquire spatio-temporal data on forest fires," in Proc. Int. Work. LBSN, 2009 Seattle, WA, USA, pp. 73–80.
  27. 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.
  28. Agarwal, R. Vaithiyanathan, 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.
  29. Abel, C. Hauff, G.-J. Houben, R. Stronkman, and K. Tao, "Twitcident: fighting fire with information from social web streams," in Proc. ACM 21st Int. Conf. Comp. WWW, Lyon, France, 2012, pp. 305–308.
  30. Li, K. H. Lei, R. Khadiwala, and K. C.-C. Chang, "TEDAS: A Twitterbased event detection and analysis system," in Proc. 28th IEEE ICDE, Washington, DC, USA, 2012, pp. 1273–1276.
  31. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: An update," SIGKDD Explor. Newsl., vol. 11, no. 1, pp. 10–18, Jun. 2009.
  32. Habibi, Real World Regular Expressions with Java 1.4. Berlin, Germany: Springer-Verlag, 2004.
  33. Zhou and Z.-W. Cao, "Research on the construction and filter method of stop-word list in text preprocessing," in Proc. 4th ICICTA, Shenzhen, China, 2011, vol. 1, pp. 217–221.
  34. Francis and H. Kucera, "Frequency analysis of English usage: Lexicon and grammar," J. English Linguistics, vol. 18, no. 1, pp. 64–70, Apr. 1982.
  35. F. Porter, "An algorithm for suffix stripping," Program: Electron. Library Inf. Syst., vol. 14, no. 3, pp 130–137, 1980.
  36. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Inf. Process. Manage., vol. 24, no. 5, pp. 513–523, Aug. 1988.
  37. M. Aiello et al., "Sensing trending topics in Twitter," IEEE Trans. Multimedia, vol. 15, no. 6, pp. 1268–1282, Oct. 2013. 39C. Shang, M. Li, S. Feng, Q. Jiang, and J. Fan, "Feature selection via maximizing global information gain for text classification," Knowl.-Based Syst., vol. 54, pp. 298–309, Dec. 2013.
  38. H. Patil and M. Atique, "A novel feature selection based on information gain using WordNet," in Proc. SAI Conf., London, U.K., 2013, pp. 625–629
  39. A. Hall and G. Holmes. "Benchmarking attribute selection techniques for discrete class data mining," IEEE Trans. Knowl. Data Eng., vol. 15, no. 6, pp. 1437–1447, Nov./Dec. 2003.
  40. Uguz, "A two-stage feature selection method for text categorization by ? using information gain, principal component analysis and genetic algorithm," Knowl.-Based Syst., vol. 24, no. 7, pp. 1024–1032, Oct. 2011.
  41. Aphinyanaphongs et al., "A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization," J. Assoc. Inf. Sci. Technol., vol. 65, no. 10, pp. 1964–1987, Oct. 2014.
  42. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel Methods: Support Vector Learning, B. Schoelkopf, C. J. C. Burges and A. J. Smola, Eds. Cambridge, MA, USA, MIT Press, 1999, pp. 185–208.
  43. H. John and P. Langley, "Estimating continuous distributions in Bayesian classifiers," in Proc. 11th Conf. Uncertainty Artif. Intell., San Mateo, CA, 1995, pp. 338–345.
  44. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA, USA: Morgan Kaufmann, 1993.
  45. W. Aha, D. Kibler, and M. K. Albert, "Instance-based learning algorithms," Mach. Learn., vol. 6, no. 1, pp. 37–66, Jan. 1991.
  46. Frank and I. H. Witten, "Generating accurate rule sets without global optimization," in Proc. 15th ICML, Madison, WI, USA, 1998, pp. 144–151.
  47. Pagallo and D. Haussler, "Boolean feature discovery in empirical learning," Mach. Learn., vol. 5, no. 1, pp. 71–99, Mar. 1990.
  48. Derrac, S. Garcia, D. Molina, and F. Herrera, "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms," Swarm Evol. Comput., vol. 1, no. 1, pp. 3–18, Mar. 2011.
  49. Wilcoxon, "Individual comparisons by ranking methods," Biometrics Bull. , vol. 1, no. 6, pp. 80–83, Dec. 1945.
  50. Becker, M. Naaman, and L. Gravano, "Beyond trending topics: Real-world event identification on Twitter," in Proc. 5th AAAI ICWSM, Barcelona, Spain, 2011, pp. 438–441.

Publication Details

Published in : Volume 3 | Issue 1 | January-February - 2017
Date of Publication Print ISSN Online ISSN
2017-02-28 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
23-29 IJSRSET1626174   Technoscience Academy

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.
URL : http://ijsrset.com/IJSRSET1626174.php

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