Abstract:
As urban traffic volume rises, traffic incidents emerge as a pressing concern for cities. This study delves into the utilization of explainable artificial intelligence (X...Show MoreMetadata
Abstract:
As urban traffic volume rises, traffic incidents emerge as a pressing concern for cities. This study delves into the utilization of explainable artificial intelligence (XAI) methodologies alongside visual aids to forecast injury severity in traffic incidents. It specifically hones in on pivotal variables such as collision type, surface condition, weather, traffic control, and vehicle body type, all of which may exert influence on injury severity. Leveraging machine learning (ML) models, such as Decision Tree and Gradient Boosting, known for their capacity to unveil intricate relationships while maintaining interpretability, to advance an in-depth comprehension of the purposefulness of applying Visual Explainable AI (vXAI) methods in the thoroughly evaluation of these critical factors, thereby furnishing invaluable insights into enhancing road safety measures.
Date of Conference: 10-12 October 2024
Date Added to IEEE Xplore: 11 November 2024
ISBN Information: