.png){kind=icon}
.png){kind=icon}
Return
Machine learning-based real-time crash risk forecasting for pedestrians
.png){kind=icon}
OA
AI
F
Y
M
DOI：10.1016/j.commtr.2025.100224.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• A bi-level framework of extreme value theory and machine learning forecasts pedestrian crash risk. • Safety-critical interactions for pedestrians were measured by post-encroachment time. • A Bayesian peak over threshold model estimates the crash risk at the signal cycle level. • The Recurrent Neural Network (RNN) model forecasts crash risk up to 12 signal cycles.
Keywords:
Artificial intelligence (AI)
Machine learning
Extreme value theory
Pedestrian safety
Forecasting
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
14.5
Papers:
216
Citations:
915
Organization
Cited Papers