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A two-dimensional surrogate safety measure based on fuzzy logic model.

Yueru Xu1, Wei Ye2, Yuanchang Xie3

  • 1Intelligent Transportation System Research Center, Southeast University, Nanjing, China; School of Transportation, Southeast University, Nanjing, China.

Accident; Analysis and Prevention
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Summary

This study introduces a new two-dimensional Surrogate Safety Measure (SSM) called Fuzzy Logic and Inverse Time to Collision (FL-iTTC) to assess vehicle crash risks, particularly those involving lateral movements. FL-iTTC demonstrates superior accuracy in identifying dangerous driving scenarios compared to existing methods.

Keywords:
Collision avoidanceFuzzy logic modelSurrogate Safety MeasureTraffic safety

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Area of Science:

  • Road Safety Engineering
  • Artificial Intelligence in Transportation
  • Vehicle Dynamics

Background:

  • Traditional Surrogate Safety Measures (SSM) primarily analyze longitudinal vehicle movements.
  • Existing SSM often fail to adequately assess risks from lateral vehicle interactions like sideswipes and angle crashes.
  • There is a critical need for advanced SSM capable of evaluating multi-dimensional crash scenarios.

Purpose of the Study:

  • To propose a novel two-dimensional SSM, Fuzzy Logic and Inverse Time to Collision (FL-iTTC), for enhanced vehicle safety analysis.
  • To evaluate the performance of FL-iTTC in identifying critical driving events and quantifying crash risks.
  • To compare FL-iTTC against existing two-dimensional SSM for lateral movement risk assessment.

Main Methods:

  • Development of a two-dimensional SSM integrating Fuzzy Logic and Inverse Time to Collision (FL-iTTC).
  • Utilizing the NGSIM dataset to extract various driving scenarios, including harsh decelerations, lane changes, and cut-ins.
  • Comparative analysis using confusion matrices and Receiver Operating Characteristic (ROC) curves against Anticipated Collision Time (ACT) and Probabilistic Driving Risk Field (PDRF).

Main Results:

  • FL-iTTC accurately identifies risky scenarios such as harsh decelerations, sudden lane-changes, cut-ins, and pre-crashes.
  • FL-iTTC achieved a higher Area Under the ROC Curve (AUC) of 0.923 compared to ACT (0.891) and PDRF (0.907).
  • The proposed FL-iTTC demonstrates superior performance in risk assessment for lateral vehicle movements.

Conclusions:

  • The developed FL-iTTC is a more accurate and reliable tool for assessing crash risks associated with vehicle lateral movements.
  • FL-iTTC effectively complements existing SSM by addressing the limitations in analyzing multi-dimensional crash scenarios.
  • This new SSM provides a valuable method for evaluating risks in complex traffic interactions like cut-ins and sideswipes.