  Surrogate Safety Measures (SSMs) are used to express road safety in terms of
the safety risk in traffic conflicts. Typically, SSMs rely on assumptions
regarding the future evolution of traffic participant trajectories to generate
a measure of risk, restricting their applicability to scenarios where these
assumptions are valid. In response to this limitation, we present the novel
Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the
PRISMA method is to derive SSMs that can be used to calculate in real time the
probability of a specific event (e.g., a crash). The PRISMA method adopts a
data-driven approach to predict the possible future traffic participant
trajectories, thereby reducing the reliance on specific assumptions regarding
these trajectories. Since the PRISMA is not bound to specific assumptions, the
PRISMA method offers the ability to derive multiple SSMs for various scenarios.
The occurrence probability of the specified event is based on simulations and
combined with a regression model, this enables our derived SSMs to make
real-time risk estimations. To illustrate the PRISMA method, an SSM is derived
for risk evaluation during longitudinal traffic interactions. Since there is no
known method to objectively estimate risk from first principles, i.e., there is
no known risk ground truth, it is very difficult, if not impossible, to
objectively compare the relative merits of two SSMs. Instead, we provide a
method for benchmarking our derived SSM with respect to expected risk trends.
The application of the benchmarking illustrates that the SSM matches the
expected risk trends. Whereas the derived SSM shows the potential of the PRISMA
method, future work involves applying the approach for other types of traffic
conflicts, such as lateral traffic conflicts or interactions with vulnerable
road users.
