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Related Experiment Video

Updated: Dec 18, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Left turn crash risk analysis: Development of a microsimulation modeling approach.

Justice Appiah1, F Adam King2, Michael D Fontaine1

  • 1Virginia Transportation Research Council, Charlottesville, VA 22903, United States.

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|June 11, 2020
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Summary

Traffic microsimulation models can predict left-turn crash risk at signalized intersections. This study developed statistical models to assess risk based on traffic conditions and flashing yellow arrow (FYA) phasing.

Keywords:
Crash riskFlashing yellow arrowLeft-turn phasingTraffic conflicts

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Microsimulation Modeling

Background:

  • Traffic simulation is crucial for analyzing complex transportation issues.
  • Advancements in microsimulation and surrogate safety measures enable detailed traffic safety analysis.
  • Existing crash prediction models often lack disaggregated risk assessment for specific maneuvers like left turns.

Purpose of the Study:

  • To examine how left-turn crash risk varies with changing traffic conditions at signalized intersections.
  • To develop statistical models predicting left-turn crash risk based on intersection parameters and traffic conditions.
  • To support the implementation of adaptive traffic control strategies, such as time-variable flashing yellow arrow (FYA) phasing.

Main Methods:

  • Utilized a calibrated traffic microsimulation model.
  • Employed surrogate safety assessment model analysis.
  • Simulated 750 unique combinations of intersection geometry, traffic, and signal timing.
  • Developed statistical models linking crash risk to left-turn phasing and prevailing conditions.

Main Results:

  • Quantified left-turn conflicts per hour across diverse simulated scenarios.
  • Established statistical models predicting the risk of left-turn crashes.
  • Demonstrated the relationship between left-turn phasing mode and crash risk.
  • Validated the model's potential for time-variable safety-based phasing selection.

Conclusions:

  • Traffic microsimulation and surrogate safety measures provide a viable approach for detailed traffic safety analysis.
  • The developed statistical models offer a disaggregated method for predicting left-turn crash risk.
  • The findings support the use of FYA phasing for optimizing traffic operations and safety at signalized intersections.