  Pedestrian safety has become an important research topic among various
studies due to the increased number of pedestrian-involved crashes. To evaluate
pedestrian safety proactively, surrogate safety measures (SSMs) have been
widely used in traffic conflict-based studies as they do not require historical
crashes as inputs. However, most existing SSMs were developed based on the
assumption that road users would maintain constant velocity and direction. Risk
estimations based on this assumption are less unstable, more likely to be
exaggerated, and unable to capture the evasive maneuvers of drivers.
Considering the limitations among existing SSMs, this study proposes a
probabilistic framework for estimating the risk of pedestrian-vehicle conflicts
at intersections. The proposed framework loosen restrictions of constant speed
by predicting trajectories using a Gaussian Process Regression and accounts for
the different possible driver maneuvers with a Random Forest model. Real-world
LiDAR data collected at an intersection was used to evaluate the performance of
the proposed framework. The newly developed framework is able to identify all
pedestrian-vehicle conflicts. Compared to the Time-to-Collision, the proposed
framework provides a more stable risk estimation and captures the evasive
maneuvers of vehicles. Moreover, the proposed framework does not require
expensive computation resources, which makes it an ideal choice for real-time
proactive pedestrian safety solutions at intersections.

Machine Learning