Beyond 1D and oversimplified kinematics: A generic analytical framework for surrogate safety measures

Area of Science:

• Road safety engineering
• Traffic simulation and modeling
• Vehicle dynamics

Background:

• Surrogate safety measures (SSMs) are crucial for assessing road safety without historical crash data.
• Existing SSMs often lack versatility across different road geometries and vehicle dynamics.
• Accurate prediction of future vehicle trajectories is essential for reliable collision risk assessment.

Purpose of the Study:

• To present a generic analytical framework for SSMs applicable to diverse highway geometries and vehicle dynamics.
• To develop a mathematical criterion for detecting potential collisions based on spatial overlap and future trajectory.
• To evaluate the framework's accuracy and compare the performance of 1D versus multi-dimensional SSMs.

Main Methods:

• Developed a generic vehicle movement model accommodating varying complexity and dimensionality.
• Established a mathematical criterion for collision risk based on spatial overlap and future time points.
• Validated the framework using simulation experiments, including linearization of the vehicle movement model.
• Compared conventional 1D SSMs with extended 3D SSMs using Time-to-Collision (TTC) metrics.

Main Results:

• The framework accurately estimates vehicle trajectory evolution and time to potential collisions.
• Simulation results show high precision compared to numerical integration solutions.
• 1D SSMs demonstrated significant inaccuracies (up to 300% for TTC > 1.5s) compared to 3D SSMs.
• The study confirmed the necessity of higher-dimensional and higher-fidelity SSMs for reliable safety analysis.

Conclusions:

• The proposed generic framework offers a versatile and accurate approach to surrogate safety assessment.
• Advanced multi-dimensional SSMs are essential for reliable collision risk evaluation in dynamic traffic environments.
• The framework's practical application in real-world scenarios underscores its effectiveness in identifying potential conflicts.