  Addressing pedestrian safety at intersections is one of the paramount
concerns in the field of transportation research, driven by the urgency of
reducing traffic-related injuries and fatalities. With advances in computer
vision technologies and predictive models, the pursuit of developing real-time
proactive protection systems is increasingly recognized as vital to improving
pedestrian safety at intersections. The core of these protection systems lies
in the prediction-based evaluation of pedestrian's potential risks, which plays
a significant role in preventing the occurrence of accidents. The major
challenges in the current prediction-based potential risk evaluation research
can be summarized into three aspects: the inadequate progress in creating a
real-time framework for the evaluation of pedestrian's potential risks, the
absence of accurate and explainable safety indicators that can represent the
potential risk, and the lack of tailor-made evaluation criteria specifically
for each category of pedestrians. To address these research challenges, in this
study, a framework with computer vision technologies and predictive models is
developed to evaluate the potential risk of pedestrians in real time. Integral
to this framework is a novel surrogate safety measure, the Predicted
Post-Encroachment Time (P-PET), derived from deep learning models capable to
predict the arrival time of pedestrians and vehicles at intersections. To
further improve the effectiveness and reliability of pedestrian risk
evaluation, we classify pedestrians into distinct categories and apply specific
evaluation criteria for each group. The results demonstrate the framework's
ability to effectively identify potential risks through the use of P-PET,
indicating its feasibility for real-time applications and its improved
performance in risk evaluation across different categories of pedestrians.
