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NEAR REAL TIME ROAD TRAFFIC EVENT DETECTION USING TWITTER AND SPARK

TEAM MEMBERS:

  1. Akshaya Ramaswamy (AXR170131)
  2. Aswin Krishna Gunasekaran (AXK175831)
  3. Sai Spandan Gogineni (SXG175130)
  4. Sankalp Rath (SXR173830)
  5. Sivagurunanthan Velayutham (SXV176330)

OVERVIEW:

  • Gather tweets using twitter search API, pre-process tweets and extract important features to build a model using spark MLlib.
  • Stream tweets using twitter streaming API and push data into kafka topic using a kafka producer after applying partial filters.
  • Read from kafka topic using kafka consumer.
  • Perform tokenization, stopword removal etc. to pre-process the data.
  • Extract machine readable features using bag of words approach and predict instances with the model.
  • Tweets are indexed to elasticsearch after classification Constructed a traffic heat map by reading the coordinates data from elasticsearch.

TECHNOLOGIES USED:

  • TWEEPY
  • KAFKA
  • ELASTICSEARCH
  • GOOGLE MAP’S API
  • SPARK STREAMING
  • SPARK MLIB
  • NLTK

ARCHITECTURE:

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