Modeling safety of highway work zones with random parameters and random effects models

Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations

“…Random parameters models and finite-mixture (latent-class) models are two major types of models that are widely used in traffic safety analysis to address unobserved heterogeneity in crash data resulting from roadway features, driver demographic and behavior information, spatial and temporal variations, etc. (Anastasopoulos et al, 2012a,b;Chen and Tarko, 2014;Flask et al, 2014;Haleem and Gan, 2013;Islam and Mannering, 2006;Kim et al, 2013;Malyshkina and Mannering, 2010;Xiong and Mannering, 2013). In this study, the cross-level interaction effects between crash-level and vehicle level variables are generally not observed in crash data, but may have significant influence on driver injury severity outcomes in traffic crashes.…”

“…Random parameters models are a group of models that simulate individual unobserved heterogeneity by assuming a distribution for parameters of interest to allow them vary across observations or (group of observations) and/or determine observation groups, and include popular models such as random parameter logit (mixed logit) model (Anastasopoulos and Mannering, 2011;Gkritza and Mannering, 2008;Gan, 2015, 2013;Kim et al, 2010Kim et al, , 2008Malyshkina and Mannering, 2010;Milton et al, 2008;Moore et al, 2011;Pai et al, 2009;Shaheed et al, 2013;Wu et al, 2014), random parameter probit model (Christoforou et al, 2010;Russo et al, 2014;Tay, 2015), random parameter negative binomial models (Chen and Tarko, 2014;Dong et al, 2014;Flask et al, 2014;Venkataraman et al, 2014Venkataraman et al, , 2013Wu et al, 2013), random parameter Tobit model (Anastasopoulos et al, 2012a,b;Yu et al, 2015) and Markov switching models Xiong et al, 2014). Milton et al (2008) were the first to apply random parameter model in traffic crash analysis, and verified its effectiveness in traffic crash data modeling.…”

Detecting unforgiving roadside contributors through the severity analysis of ran-off-road crashes

“…Estimation can be done by solving the integral with Monte Carlo simulation. Efficiency has been increased using simulation with Halton draws (Halton, 1960), a popular and efficient estimation technique for random parameters models (Bhat, 2003;Train, 2009;Chen and Tarko, 2014).…”

Common hazards and their mitigating measures in work zones: A qualitative study of worker perceptions

“…However, the crash data-based approach is often hampered by the lack of detail in official datasets (Chen and Tarko, 2014;Cheng et al, 2012) and the likelihood of under-reporting of work zone crashes (Debnath et al, 2013;Schrock et al, 2004).…”