| Autor: |
ZIHANG WEI, ZIHAO LI, KULKARNI, MIHIR MANDAR, XIMIN YUE |
| Zdroj: |
ITE Journal; Sep2023, Vol. 93 Issue 9, p37-45, 9p |
| Abstrakt: |
The article focuses on developing an equitable traffic crash prediction framework to guide the fair allocation of safety improvement grants. It is reported that the proposed framework uses a TabNet machine learning model that integrates deep learning and interpretability. It aims to predict crashes accurately and equally for various demographic groups (e.g., high-income/low-income, urban/rural). |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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