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

Authors Rahul Deb Das , Ross S. Purves

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LIPIcs.GISCIENCE.2018.25.pdf
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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

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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

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