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Surrogate Safety Measures (SSMs) are used to express road safety in terms ofthe safety risk in traffic conflicts. Typically, SSMs rely on assumptionsregarding the future evolution of traffic participant trajectories to generatea measure of risk, restricting their applicability to scenarios where theseassumptions are valid. In response to this limitation, we present the novelProbabilistic RISk Measure derivAtion (PRISMA) method. The objective of thePRISMA method is to derive SSMs that can be used to calculate in real time theprobability of a specific event (e.g., a crash). The PRISMA method adopts adata-driven approach to predict the possible future traffic participanttrajectories, thereby reducing the reliance on specific assumptions regardingthese trajectories. Since the PRISMA is not bound to specific assumptions, thePRISMA method offers the ability to derive multiple SSMs for various scenarios.The occurrence probability of the specified event is based on simulations andcombined with a regression model, this enables our derived SSMs to makereal-time risk estimations. To illustrate the PRISMA method, an SSM is derivedfor risk evaluation during longitudinal traffic interactions. Since there is noknown method to objectively estimate risk from first principles, i.e., there isno known risk ground truth, it is very difficult, if not impossible, toobjectively compare the relative merits of two SSMs. Instead, we provide amethod for benchmarking our derived SSM with respect to expected risk trends.The application of the benchmarking illustrates that the SSM matches theexpected risk trends. Whereas the derived SSM shows the potential of the PRISMAmethod, future work involves applying the approach for other types of trafficconflicts, such as lateral traffic conflicts or interactions with vulnerableroad users.

PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation