The occurrence rate of death and injury due to road traffic accidents is rising increasingly
globally day by day. For several decades, the focus of research has been on getting a …

Recent advanced high-throughput field phenoty** combined with sophisticated big data
analysis methods have provided plant breeders with unprecedented tools for a better …

In this systematic literature review (SLR), we use a series of quantitative bibliometric
analyses to (1) identify the main papers, journals, and authors of the publications that make …

Abstract Machine learning (ML) model interpretability has attracted much attention recently
given the promising performance of ML methods in crash frequency studies. Extracting …

Zonal crash prediction has been one of the most prevalent topics in recent traffic safety
research. Typically, zonal safety level is evaluated by relating aggregated crash statistics at …

This study develops a Bayesian spatial random parameters Tobit model to analyze crash
rates on road segments, in which both spatial correlation between adjacent sites and …

In this study, a multivariate random-parameters Tobit model is proposed for the analysis of
crash rates by injury severity. In the model, both correlation across injury severity and …

This study investigates the inclusion of spatio-temporal correlation and interaction in a
multivariate random-parameters Tobit model and their influence on fitting areal crash rates …

This study develops neural network models to explore the nonlinear relationship between
crash frequency by severity and risk factors. To eliminate the possibility of over-fitting and to …