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Insights into vehicle conflicts based on traffic flow dynamics.

Shengxuan Ding1, Mohamed Abdel-Aty2, Zijin Wang2

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This study introduces a new method for traffic safety analysis using unsupervised learning to identify traffic conflicts. It found that traffic conflict risk varies by traffic state, with wide moving jams posing the highest risk.

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Area of Science:

  • Traffic Engineering
  • Transportation Safety
  • Data Science

Background:

  • Assessing traffic safety is vital, particularly when crash data is scarce.
  • Traffic conflict indicators offer a valuable alternative for safety evaluation.
  • Understanding traffic flow dynamics is key to proactive safety measures.

Purpose of the Study:

  • To develop a framework for identifying traffic conflicts using unsupervised learning and traffic flow characteristics.
  • To automatically establish dynamic surrogate safety measures (SSM) thresholds.
  • To analyze traffic conflict distribution across different traffic states.

Main Methods:

  • Utilized high-resolution trajectory data and the three-phase traffic theory to identify traffic states and transitions.
  • Employed unsupervised learning models (k-means, GMM, Mclust) to establish SSM thresholds.
  • Mapped SSMs to traffic states considering time, space, and deceleration.

Main Results:

  • Mclust demonstrated superior performance in identifying traffic conflicts compared to k-means and GMM.
  • Traffic conflict risk varied significantly across traffic states: wide moving jam (J) > synchronous flow (S) > free flow (F).
  • Identified that traffic conflict thresholds are not uniform across different traffic states.

Conclusions:

  • The study highlights the necessity of dynamic thresholds for accurate traffic conflict analysis.
  • Traffic state heterogeneity necessitates state-specific safety measure thresholds.
  • The proposed framework effectively identifies traffic conflicts and their varying risks across traffic conditions.