Driver injury severity: an application of ordered probit models.

By Kara M. Kockelman, Young-jun Kweon. Accident Analysis and Prevention, 2002

639 citations recorded by the source corpus.

Subfields: Automotive Engineering, Pulmonary and Respiratory Medicine, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/99d6766162b33161ac04ae14a9981af034f4dfbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Hong-guo Xu
2. Noah Goodall
3. Fang Zong
4. Hui-yong Zhang
5. S. Lo Piano

In Pulmonary and Respiratory Medicine

1. Hong-guo Xu
2. Noah Goodall
3. Fang Zong
4. Hui-yong Zhang

In Safety, Risk, Reliability and Quality

1. Hong-guo Xu
2. Noah Goodall
3. Fang Zong
4. Hui-yong Zhang

Citations in the corpus

• Away from Trolley Problems and Toward Risk Management (2016, background): “Trolley problems also assume known outcomes, which critics are quick to dismiss as unrealistic—experts can confidently predict the outcomes of only the most catastrophic crashes (Kockelman and Kweon 2002; O’Donnell and Connor 1996), yet almost everything else in road safety is probabilistic.”
• Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models (2013, methodology): “The most commonly used Regression models in traffic injury analysis are the Logistic Regression model and the Ordered Probit model [1–6].”