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Characteristics identification and evolution patterns analyses of road chain conflicts.

Hao Zhong1, Ling Wang1, Zicheng Su1

  • 1The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University, Shanghai 201804, China.

Accident; Analysis and Prevention
|December 12, 2023
PubMed
Summary

Chain conflicts, where evasive actions cause chain-reaction crashes, are analyzed. Understanding their evolution patterns is key to developing effective crash prevention strategies.

Keywords:
Conflict risk quantitationMulti segment typesRoad chain conflict patternsTraffic risk control strategiesTraffic states evolutionVehicle trajectory data

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Area of Science:

  • Traffic safety
  • Transportation engineering
  • Accident analysis

Background:

  • Chain conflicts, involving evasive maneuvers that trigger subsequent crashes, pose a significant risk.
  • These events are frequent but underreported, hindering effective safety management.

Purpose of the Study:

  • To comprehend chain conflict evolution patterns under varied traffic and road conditions.
  • To develop strategies for managing and reducing chain-reaction crash risks.

Main Methods:

  • Individual vehicle conflicts (rear-end, sideswipe) identified using UAV trajectory data.
  • A novel algorithm developed to link individual conflicts into chain conflicts, accounting for temporal randomness and duration fluctuations.
  • Risk and propagation indicators extracted to analyze chain conflict characteristics.

Main Results:

  • Chain conflict rates vary significantly across different road segments and traffic conditions.
  • Three distinct evolution patterns identified: Longitudinal Risk Decrease, Longitudinal Risk Increase, and Comprehensive High-risk Persistent.
  • Spatial-temporal high-risk areas and pattern transition probabilities determined, showing a tendency towards stability and shifts from low-risk to high-risk patterns.

Conclusions:

  • Understanding chain conflict patterns is crucial for preventing chain-reaction crashes.
  • Identified patterns and their transitions provide a basis for targeted risk reduction strategies.
  • The study offers significant insights into chain conflict dynamics and prevention.