@article{ref1,
title="Multilevel data in traffic safety: a 5 x ST-level hierarchy",
journal="Proceedings of the Road Safety on Four Continents Conference",
year="2010",
author="Huang, Hongwei and Abdel-Aty, Mohamed Ahmed",
volume="15",
number="",
pages="305-318",
abstract="Traditional crash prediction models, such as generalized linear regression model, suffer from a common underlying limitation that observations (e.g., a crash or a vehicle involvement) are treated as &quot;independent&quot; to each other. However, this &quot;independence&quot; assumption may often not hold true since multilevel  data structures exist extensively because of the traffic data collection and clustering process. Disregarding the possible within-group correlations may lead  to production of models with unreliable parameter estimates and statistical inferences. In this paper, a 5×ST-level hierarchy is proposed to represent the general framework of multilevel data structures in traffic safety, i.e. [Geographic region level - Traffic site level - Traffic crash level- Driver-vehicle unit level - Occupant level] × Spatiotemporal level. To properly accommodate the potential cross-level heterogeneity and spatiotemporal correlation due to the multilevel data structure, a Bayesian hierarchical approach that explicitly specifies multilevel structure and reliably yields parameter estimates is introduced and recommended. Using Bayesian hierarchical models, the results from several case studies are highlighted to show the improvements on model fitting and predictive performance over traditional models  by appropriately accounting for the multilevel data structure.<p />",
language="",
issn="",
doi="",
url="http://dx.doi.org/"
}