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Related Experiment Video

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Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using

Ebtesam Alomari1, Iyad Katib1, Aiiad Albeshri1

  • 1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for automatically detecting traffic events using social media data and distributed machine learning. The Iktishaf+ tool effectively identifies real-world incidents from Arabic tweets, enhancing transportation safety analysis.

Keywords:
Arabic tweetsautomatic labelingbig datadata analyticsdistributed machine learningevent detectionroad trafficsmart citiessocial mediasocial media analytics

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Area of Science:

  • Computer Science
  • Transportation Engineering
  • Data Science

Background:

  • Social media platforms serve as increasingly valuable data sources for real-world event detection.
  • Traditional transportation monitoring methods face limitations in scope and cost-effectiveness.
  • Big data analytics and machine learning applications in transportation are still evolving.

Purpose of the Study:

  • To develop and implement an automatic labelling method for detecting traffic-related events from social media data.
  • To create a software tool, Iktishaf+, for real-time traffic event detection using Arabic Twitter data.
  • To leverage big data and distributed machine learning for enhanced transportation safety analysis.

Main Methods:

  • Utilized distributed machine learning over Apache Spark for processing large volumes of social media data.
  • Developed an automatic labelling method and a location extractor for Arabic tweets.
  • Employed machine learning classifiers including support vector machines, Naïve Bayes, and logistic regression.
  • Collected and analyzed 33.5 million Arabic tweets from Saudi Arabia via the Twitter API.

Main Results:

  • Successfully detected and validated real-world traffic events such as fires, heavy rains, and accidents in Saudi Arabia.
  • Demonstrated the effectiveness of the Iktishaf+ tool in automatically identifying events without prior knowledge.
  • Extracted and visualized spatio-temporal information of detected traffic events.

Conclusions:

  • Social media analytics, particularly Twitter data, is a powerful and cost-effective tool for real-time traffic event detection.
  • The developed Iktishaf+ tool and automatic labelling method show significant promise for improving transportation safety and management.
  • This research highlights the potential of big data and distributed machine learning in addressing critical transportation challenges.