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Spatiotemporal filtering method for detecting kinematic waves in a connected environment.

Eui-Jin Kim1, Dong-Kyu Kim2, Seung-Young Kho2

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Summary

This study introduces a new method to detect backward-moving kinematic waves (KWs) in traffic using connected vehicle data. This advance helps create strategies to improve traffic flow and safety.

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Area of Science:

  • Traffic Engineering
  • Transportation Science
  • Data Science

Background:

  • Backward-moving kinematic waves (KWs), such as stop-and-go traffic, degrade freeway capacity and increase travel times.
  • Existing detection methods may not fully leverage data from connected vehicle environments.

Purpose of the Study:

  • To develop a sequential filtering method for detecting KWs using connected vehicle data.
  • To enable the development of traffic control strategies for connected vehicles to mitigate KW propagation.

Main Methods:

  • Ensemble empirical mode decomposition to filter data and consider spectral features of KWs.
  • Cross-correlation to analyze spatial movements and identify candidate KWs.
  • Logistic regression to evaluate candidate KWs based on asynchronous speed and flow changes.

Main Results:

  • The proposed method effectively detects KWs in connected environments, even with 30% market penetration.
  • The study investigated the impact of data resolution on KW detection performance.
  • The method successfully distinguishes true KWs from localized speed reductions not propagating upstream.

Conclusions:

  • The sequential filtering method shows significant promise for real-time KW detection in connected traffic systems.
  • This approach can support proactive traffic management and enhance road safety.
  • Understanding data resolution's impact is crucial for optimizing detection in connected environments.