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Big Data in Engineering Applications pp 265–278Cite as

Traffic Condition Monitoring Using Social Media Analytics

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Part of the book series: Studies in Big Data ((SBD,volume 44))

Abstract

Scientist and practitioner seek innovations that analyze traffic big data for reducing congestion. In this chapter, we propose a framework for traffic condition monitoring using social media data analytics. This involves sentiment analysis and cluster classification utilizing the big data volume readily available through Twitter microblogging service. Firstly, we examine some key aspects of big data technology for traffic, transportation and information engineering systems. Secondly, we consider Parts of Speech tagging utilizing the simplified Phrase-Search and Forward-Position-Intersect algorithms. Then, we use the k-nearest neighbor classifier to obtain the unigram and bigram; followed by application of Naїve Bayes Algorithm to perform the sentiment analysis. Finally, we use the Jaccard Similarity and the Term Frequency-Inverse Document Frequency for cluster classification of traffic tweets data. The preliminary results show that the proposed methodology, comparatively tested for accuracy and precision with another approach employing Latent Dirichlet Allocation is sufficient for predicting traffic flow in order to effectively improve the road traffic condition.

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References

  1. Lu, H-P., Sun, Z., & Qu, W. (2015). Big data-driven based real-time traffic flow state identification and prediction. Discrete Dynamics in Nature and Society, 2015, Article ID 284906, 1–11.

    Google Scholar 

  2. Villars, R. L., Olofson, C. W., & Eastwood, M. (2011). Big data: What it is and why you should care. IDC.

    Google Scholar 

  3. Vlahogianni, E. I, Park, B. B., & van Lint, J. W. C. (2015). Big data in transportation and traffic engineering. Transportation Research Part C: Emerging Technologies, 58(Part B), 1–161.

    Article  Google Scholar 

  4. Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going? Transportation Research Part A: Policy and Practice, 41(5), 367–381.

    Google Scholar 

  5. Wang, X., & Li, Z. (2016). Traffic and transportation smart with cloud computing on big data. International Journal of Computer Science and Applications, 13(1), 1–16.

    MathSciNet  Google Scholar 

  6. Philander, K., & Zhong, Y. (2016). Twitter sentiment analysis: Capturing sentiment from integrated resort tweets. International Journal of Hospitality Management, 55, 16–24.

    Article  Google Scholar 

  7. Korkontzelos, I., Nikfarjam, A., Shardlow, M., Sarker, A., Ananiadou, S., & Gonzalez, G. H. (2016). Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. Journal of Biomedical Informatics, 62, 148–158.

    Article  Google Scholar 

  8. Burscher, B., Vliegenthart, R., & de Vreese, C. H. (2016). Frames beyond words: Applying cluster and sentiment analysis to news coverage of the nuclear power issue. Social Science Computer Review, 34(5), 530–545.

    Article  Google Scholar 

  9. Abidin, A. F., Kolberg, M., & Hussain, A. (2015). Integrating Twitter traffic information with Kalman filter models for public transportation vehicle arrival time prediction. In M. Trovati, R. Hill, A. Anjum, S. Y. Zhu & L. Liu (Eds.), Big-data analytics and cloud computing (pp. 67–82).

    Chapter  Google Scholar 

  10. Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh conference of International language Resources and Evaluation (LREC’ 10).

    Google Scholar 

  11. Go, A., Huang, L., & Bhayani, R. (2009). Twitter sentiment analysis. Stanford University, Stanford California, USA, CS224N - Final Year Project.

    Google Scholar 

  12. Wang, J., Gu, Q., & Wang, G. (2013). Potentila power and problems in sentiment mining of social media. International Journal of Strategic Decision Science, 4(2), 16–26.

    Article  Google Scholar 

  13. Kumar, A., & Sebastian, T. M. (2012). Sentiment analysis on Twitter. International Journal of Computer Science Issues, 9(4:3), 372–378.

    Google Scholar 

  14. He, J., Shen, W., Divakaruni, P., Wynter, L., & Lawrence, R. (2013). Improving traffic prediction with tweet semantics. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China.

    Google Scholar 

  15. Grosenick, S. (2012). Real-time traffic prediction improvement through semantic mining of social networks. Unpublished master thesis. University of Washington, Washington.

    Google Scholar 

  16. Elsafoury, F. A. (2013). Monitoring urban traffic status using twitter messages (pp. 1–46).

    Google Scholar 

  17. Azam, N., Abulaish, M., & Haldar, N. A.-H. (2015). Twitter data mining for events classification and analysis. In Second International Conference on Soft Computing and Machine Intelligence.

    Google Scholar 

  18. Broder, A. Z., Glassman, S. C., Manasse, M. S., & Zweig, G. (1997). Syntactic clustering of the web. Computer Networks and ISDN Systems, 29(8), 1157–1166.

    Article  Google Scholar 

  19. van Dongen, S. (2000). Graph clustering by flow simulation. Utrecht, Netherlands: University of Utrecht.

    Google Scholar 

  20. McHugh, D. (2014). Traffic prediction and analysis using a big data and visualisation approach. Ireland: Blanchardstown.

    Google Scholar 

  21. Tejaswin, P., Kumar, R., & Gupta, S. (2015). Tweeting traffic: Analyzing Twitter for generating real-time city traffic insights and predictions. In CODS-IKDD ’15, Bangalore, India.

    Google Scholar 

  22. Rais, K. (2014). Twitter analysis.

    Google Scholar 

  23. cran.r-project.org. https://cran.r-project.org/web/packages/.

  24. Eckert, K. (2008). Simplified phrase search algorithm.

    Google Scholar 

  25. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.

    Google Scholar 

  26. Samworth, R. J. (2012). Optimal weighted nearest neighbour classifiers. Annals of Statistics, 40(5), 2733–2763.

    Article  MathSciNet  MATH  Google Scholar 

  27. Hu, M., & Liu, B. (2004). Mining opinion features in customer review. In Proceedings of the 19th National Conference on Artificial Intelligence, AAAAI’04.

    Google Scholar 

  28. Kang, H., Yoo, S. J., & Han, D. (2012). Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis. Expert Systems with Applications, 39, 6000–6010.

    Article  Google Scholar 

  29. Spärck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11–21.

    Article  Google Scholar 

  30. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(4–5), 993–1022.

    MATH  Google Scholar 

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Authors and Affiliations

  1. Concordia Institute for Information and Systems Engineering (CIISE), Montreal, Canada

    Taiwo Adetiloye & Anjali Awasthi

Authors
  1. Taiwo Adetiloye
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  2. Anjali Awasthi
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Correspondence to Taiwo Adetiloye .

Editor information

Editors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. University of Southern Queensland, Springfield, Queensland, Australia

    Ravinesh Deo

  4. Polytechnic University of Milan, Milan, Italy

    Stavros Ntalampiras

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Adetiloye, T., Awasthi, A. (2018). Traffic Condition Monitoring Using Social Media Analytics. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_13

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  • DOI: https://doi.org/10.1007/978-981-10-8476-8_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8475-1

  • Online ISBN: 978-981-10-8476-8

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