The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity

144 citations recorded by the source corpus.

How researchers see this paper

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

Temporal instability and the analysis of highway accident data (2018, background): “Malyshkina and Mannering (2010) and Xiong et al. (2014) found similar statistical support for Markov switching in injury-severity models using the detailed data available conditioned on an accident having occurred.”
Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances (2018, result): “In line with previous studies (Eluru et al., 2008; Xiong et al., 2014; Yasmin et al., 2016; Fountas and Anastasopoulos, 2017; Sarwar and Anastasopoulos, 2017; Fountas et al., 2018b), the annual average daily traffic, the traffic control indicator, the County indicator, and the truck involvement indi”
Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes (2018, background): “In addition, the dataset did not include information about pavement conditions and access points, which have been proved to be very important for segment crash frequencies in many studies (Usman et al., 2010; Lee et al., 2011; Xiong et al., 2014; Zeng and Huang, 2014).”