Document Open Access Logo

Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper)

Authors Rahul Deb Das , Ross S. Purves

PDF

File

Thumbnail PDF
PDF
LIPIcs.GISCIENCE.2018.25.pdf
  • Filesize: 347 kB
  • 7 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Information systems → Information retrieval
  • Computing methodologies → Natural language processing
  • Computing methodologies → Artificial intelligence
  • Human-centered computing → Ubiquitous and mobile computing
Keywords
  • Urban mobility
  • traffic
  • UGC
  • tweet
  • event
  • GIR
  • geoparsing

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

This paper explores the usefulness of Twitter data to detect traffic events and their geographical locations in India through machine learning and NLP. We develop a classification module that can identify tweets relevant for traffic authorities with 0.80 recall accuracy using a Naive Bayes classifier. The proposed model also handles vernacular geographical aspects while retrieving place information from unstructured texts using a multi-layered georeferencing module. This work shows Mumbai has a wide spread use of Twitter for traffic information dissemination with substantial geographical information contributed by the users.

Cite As Get BibTex

Rahul Deb Das and Ross S. Purves. Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 25:1-25:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.25

Author Details

Rahul Deb Das
  • Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
Ross S. Purves
  • Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland

Funding

We are grateful to the Swiss National Science Foundation (SNSF) grant number 166788.

References

  1. E. D. Andrea, P. Ducange, B. Lazzerini, and F. Marcelloni. Real-time detection of traffic from twitter stream analysis. IEEE Transactions on Intelligent Transportation Systems, 16(4):2269-2283, 2015. Google Scholar
  2. Farzindar Atefeh and Wael Khreich. A survey of techniques for event detection in twitter. Computational Intelligence, 31(1):132-164, 2015. Google Scholar
  3. Andrew Crooks, Arie Croitoru, Anthony Stefanidis, and Jacek Radzikowski. #Earthquake: Twitter as a distributed sensor system. Transactions in GIS, 17(1):124-147, 2013. Google Scholar
  4. Judith Gelernter and Shilpa Balaji. An algorithm for local geoparsing of microtext. GeoInformatica, 17(4):635-667, 2013. Google Scholar
  5. Michael F. Goodchild. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4):211-221, 2007. Google Scholar
  6. Yiming Gu, Zhen Qian, and Feng Chen. From twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies, 67:321-342, 2016. Google Scholar
  7. Livia Hollenstein and Ross S Purves. Exploring place through user-generated content: Using flickr to describe city cores. Journal of Spatial Information Science, 1(1):21-48, 2010. Google Scholar
  8. Akira Kinoshita, Atsuhiro Takasu, and Jun Adachi. Real-time traffic incident detection using a probabilistic topic model. Information Systems, 54:169-188, 2015. Google Scholar
  9. D. A. Kurniawan, S. Wibirama, and N. A. Setiawan. Real-time traffic classification with twitter data mining. In 8th International Conference on Information Technology and Electrical Engineering (ICITEE), pages 1-5, Yogyakarta, Indonesia, 2016. Google Scholar
  10. Jochen L. Leidner, Gail Sinclair, and Bonnie Webber. Grounding spatial named entities for information extraction and question answering. In Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1, Stroudsburg, USA, 2003. Google Scholar
  11. Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J Bethard, and David McClosky. The stanford corenlp natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Baltimore, Maryland, USA, 2014. Google Scholar
  12. Francisco C. Pereira, Filipe Rodrigues, and Moshe Ben-Akiva. Text analysis in incident duration prediction. Transportation Research Part C: Emerging Technologies, 37:177-192, 2013. Google Scholar
  13. Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World Wide Web, pages 851-860, Raleigh, North Carolina, USA, 2010. ACM. Google Scholar
  14. A. Salas, P. Georgakis, and Y. Petalas. Incident detection using data from social media. In IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pages 751-755, Yokohama, Japan, 2017. Google Scholar
  15. Azeem Uddin. Traffic congestion in indian cities: Challenges of a rising power, draft. Report, General Motors India, 2009. Google Scholar
  16. Napong Wanichayapong, Wasawat Pruthipunyaskul, Wasan Pattara-Atikom, and Pimwadee Chaovalit. Social-based traffic information extraction and classification. In IEEE 11th International Conference on ITS Telecommunications, St. Petersburg, Russia, 2011. Google Scholar
  17. Yuchao Zhou, Suparna De, and Klaus Moessner. Real world city event extraction from twitter data streams. Procedia Computer Science, 98:443-448, 2016. Google Scholar
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact