A heterogeneity-in-means count model for evaluating the effects of interchange type on heterogeneous influences of interstate geometrics on crash frequencies

By Narayanaswamy Venkataraman, V. Shankar, Gudmundur F. Ulfarsson, Damian Deptuch. 2014

120 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Social Psychology, Transportation

View Paper: https://www.semanticscholar.org/paper/4ee2449020fde58336f51d84f6fecc225c889b61

How researchers see this paper

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

In Safety, Risk, Reliability and Quality

1. Anuj Sharma
2. Mo Zhao
3. Panagiotis Ch. Anastasopoulos
4. Wei Li
5. Chenhui Liu
6. Grigorios Fountas

In Transportation

1. Panagiotis Ch. Anastasopoulos

Citations in the corpus

• Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis (2016, background): “177 In the accident injury-severity frequency framework, where the frequency of property damage only, 178 injuries, and fatalities are modeled simultaneously, overdispersion is expected (Shankar et al., 1998; 179 Venkataraman et al., 2014; Zou et al., 2015).”
• Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes (2018, methodology): “…across observations in crash frequency analysis is to adopt random parameters count data models (Lord and Mannering, 2010; Chen and Tarko, 2014; Venkataraman et al., 2014; Barua et al., 2015, 2016; Coruh et al., 2015; Alarifi et al., 2017; Bhat et al., 2017; Chen et al., 2017; Rista et al.,…”