Cargando…
Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models
The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer perceptr...
Autor principal: | Assi, Khaled |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589286/ https://www.ncbi.nlm.nih.gov/pubmed/33086567 http://dx.doi.org/10.3390/ijerph17207598 |
Ejemplares similares
-
Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol
por: Assi, Khaled, et al.
Publicado: (2020) -
Candidates for Synergies: Linear Discriminants versus Principal Components
por: Vinjamuri, Ramana, et al.
Publicado: (2014) -
Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio
por: Alam, Md Saiful, et al.
Publicado: (2023) -
Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach
por: Zhu, Shengxue, et al.
Publicado: (2021) -
Crash severity analysis of vulnerable road users using machine learning
por: Komol, Md Mostafizur Rahman, et al.
Publicado: (2021)