33 research outputs found
PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation
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. As a result, they are only applicable in scenarios where
those assumptions hold. To address this issue, we present a novel data-driven
Probabilistic RISk Measure derivAtion (PRISMA) method. The PRISMA method is
used to derive SSMs that can be used to calculate in real time the probability
of a specific event (e.g., a crash). Because we adopt a data-driven approach to
predict the possible future evolutions of traffic participant trajectories,
less assumptions on these trajectories are needed. Since the PRISMA is not
bound to specific assumptions, multiple SSMs for different types of scenarios
can be derived. To calculate the probability of the specific event, the PRISMA
method uses Monte Carlo simulations to estimate the occurrence probability of
the specified event. We further introduce a statistical method that requires
fewer simulations to estimate this probability. 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. 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.Comment: 26 pages, 4 figures, 1 tabl