An explainable multi-task deep learning framework for crash severity prediction using multi-source data
Abstract Traffic accidents pose significant global challenges, causing substantial injuries, fatalities, and economic losses. Current research predominantly focuses on single-prediction objectives (e.g., fatality prediction) while neglecting property damage assessments and critical interactions betw...
Saved in:
| Main Authors: | Yuanyuan Xiao, Zongtao Duan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-09226-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX)
by: Jong-Hwan Jang, et al.
Published: (2025-07-01) -
Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model
by: Yalan Jiang, et al.
Published: (2025-01-01) -
Impacts of traffic barrier types on crash severity
by: Ye Dong, et al.
Published: (2025-07-01) -
The effectiveness of explainable AI on human factors in trust models
by: Justin C. Cheung, et al.
Published: (2025-07-01) -
Strategies for applying interpretable and explainable AI in real world IoT applications
by: Anber Abraheem Shlash Mohammad, et al.
Published: (2025-06-01)