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Crowdsourced Traffic Event Detection and Source Reputation Assessment Using Smart Contracts.

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This study introduces a system for assessing the trustworthiness of crowdsourced traffic data, crucial for safe transportation. It effectively detects data manipulation and assesses source reputation using blockchain technology.

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Area of Science:

  • Computer Science
  • Transportation Engineering
  • Data Science

Background:

  • Real-time traffic event data is vital for transportation safety and efficiency.
  • Crowdsensing offers broader coverage than traditional infrastructure but requires data trustworthiness assessment.
  • Detecting data manipulation, deception, and spam is critical for reliable crowdsourced traffic information.

Purpose of the Study:

  • To design and demonstrate a system for road traffic event detection and source reputation assessment.
  • To address the challenge of unreliable data sources in crowdsensing applications.
  • To develop a decentralized system resilient to institutional data manipulation.

Main Methods:

  • A system integrating road traffic event detection and source reputation assessment was developed.
  • The system was designed for decentralized operation using smart contracts on a blockchain platform.
  • Evaluation involved synthetic datasets and real user data from a mobile application within a driving simulator.

Main Results:

  • The proposed system accurately detects various manipulative and misreporting behaviors in crowdsourced traffic data.
  • The system demonstrates rapid convergence to a final trust score.
  • Effectiveness was validated even within the resource constraints of a blockchain virtual machine.

Conclusions:

  • The developed system enhances the trustworthiness of crowdsourced traffic data for improved transportation management.
  • Decentralized implementation on blockchain increases resilience and eliminates single points of failure.
  • The approach is effective in identifying and mitigating unreliable data sources in real-world traffic scenarios.