01030705

Component

https://doi.org/10.3141/1950-03

http://scholar.google.com...avis&publication_year=2006

In recent years increased attention has been given to predicting the effects of roadway improvements on traffic safety. Tools have been developed in cooperation with FHWA and TRB that attempt to predict crash experience, and they require estimates of crash modification factors (CMFs) to produce predicted reduction benefits. The tools make use of empirical Bayes statistics, which currently require that crash data be overdispersed. The main purpose of this paper is to illustrate two alternative methods of estimating CMFs that can be applied whether or not the crash data are overdispersed. The first combines the hierarchical Bayes model with a model that allows for temporal changes in the covariates. The second uses an estimated propensity score to match a group of locations that had a countermeasure installed with a comparison group that was not treated. Both methods were used to compute estimates for the CMFs associated with signalizing a set of nonrural intersections. Neither method found evidence of an overall reduction or an increase in crashes following signalization. However, use of the first method indicated that right-angle crashes decreased while rear-ending crashes increased, as has been observed in other studies.

01030706

English

pp 17-23

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099595

Print

Figures (1) ; References (16) ; Tables (4)

Crash modification factors; Estimating; Highway safety; Improvements; Intersections; Rear end crashes; Right angle crashes; Signalization; Traffic safety

Bayesian statistics

Highways; Safety and Human Factors; I81: Accident Statistics

TRIS, TRB, ATRI

Mar 3 2006 10:55AM

• Application of "Highway Safety Manual" Draft Chapter: Louisiana Experience
• Bayesian Multivariate Poisson Regression for Models of Injury Count, by Severity
• Calibration of Safety Prediction Models for Planning Transportation Networks
• Comparison of Two Negative Binomial Regression Techniques in Developing Accident Prediction Models
• Describing the Evolution in the Number of Highway Deaths by Decomposition in Exposure, Accident Risk, and Fatality Risk
• Effects of Sample Size on Goodness-of-Fit Statistic and Confidence Intervals of Crash Prediction Models Subjected to Low Sample Mean Values
• Refinement of Accident Prediction Models for Spanish National Network
• Using Macrolevel Collision Prediction Models in Road Safety Planning Applications
• Using the Rural Two-Lane Highway Draft Prototype Chapter