01518716

Component

This study presents an innovative hierarchical Bayesian model for spatial modeling of county level crashes in New Jersey. First, the model is estimated using raw crash counts. Then, weights are applied to crashes with different severities to obtain a weighted crash count. The goal in incorporating severities in the spatial model is to demonstrate the importance of representing spatial variation of crashes as well as their severity. As a contribution to existing literature, crash rates are also analyzed by road type. Finally, crash rate maps are developed based on modeling results to visualize the effects of spatial covariates. The results of the study indicate that the most influential covariate for the crashes is the road curvature, followed by roadway mileage and roadway defects. It is also found that it is possible to represent the crash risk better by applying severity weights to the individual crashes. The developed crash rate maps can help transportation professionals on identifying and ranking the locations at an aggregate level, which requires closer attention.

This paper was sponsored by TRB committee ANB20 Safety Data, Analysis and Evaluation.

01503729

14-5127

English

19p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures; Maps; References; Tables

Counties; Crash rates; Crash severity; Highway factors in crashes; Highway safety; Spatial analysis

New Jersey

Highways; Safety and Human Factors; I82: Accidents and Transport Infrastructure

Transportation Research Board Annual Meeting 2014 Paper #14-5127

TRIS, TRB, ATRI

Jan 27 2014 3:47PM