  This paper presents a generic analytical framework tailored for surrogate
safety measures (SSMs) that is versatile across various highway geometries,
capable of encompassing vehicle dynamics of differing dimensionality and
fidelity, and suitable for dynamic, real-world environments. The framework
incorporates a generic vehicle movement model, accommodating a spectrum of
scenarios with varying degrees of complexity and dimensionality, facilitating
the prediction of future vehicle trajectories. It establishes a generic
mathematical criterion to denote potential collisions, characterized by the
spatial overlap between a vehicle and any other entity. A collision risk is
present if the collision criterion is met at any non-negative time point, with
the minimum threshold representing the remaining time to collision. The
framework's proficiency spans from conventional one-dimensional (1D) SSMs to
extended multi-dimensional, high-fidelity SSMs. Its validity is corroborated
through simulation experiments that assess the precision of the framework when
linearization is performed on the vehicle movement model. The outcomes showcase
remarkable accuracy in predicting vehicle trajectories and the time remaining
before potential collisions occur. The necessity of higher-dimensional and
higher-fidelity SSMs is highlighted through a comparison of conventional 1D
SSMs and extended three-dimensional (3D) SSMs. The results showed that using 1D
SSMs over 3D SSMs could be off by 300% for non-critical Time-to-Collision (TTC)
values and about 20% for critical TTC values (below 1.5 seconds). Furthermore,
the framework's practical application is demonstrated through a case study that
actively evaluates all potential conflicts, underscoring its effectiveness in
dynamic, real-world traffic situations.
