A fully Bayesian before–after analysis of permeable friction course (PFC) pavement wet weather safety. (July 2015)
- Record Type:
- Journal Article
- Title:
- A fully Bayesian before–after analysis of permeable friction course (PFC) pavement wet weather safety. (July 2015)
- Main Title:
- A fully Bayesian before–after analysis of permeable friction course (PFC) pavement wet weather safety
- Authors:
- Buddhavarapu, Prasad
Smit, Andre F.
Prozzi, Jorge A. - Abstract:
- Abstract: Permeable friction course (PFC), a porous hot-mix asphalt, is typically applied to improve wet weather safety on high-speed roadways in Texas. In order to warrant expensive PFC construction, a statistical evaluation of its safety benefits is essential. Generally, the literature on the effectiveness of porous mixes in reducing wet-weather crashes is limited and often inconclusive. In this study, the safety effectiveness of PFC was evaluated using a fully Bayesian before–after safety analysis. First, two groups of road segments overlaid with PFC and non-PFC material were identified across Texas; the non-PFC or reference road segments selected were similar to their PFC counterparts in terms of site specific features. Second, a negative binomial data generating process was assumed to model the underlying distribution of crash counts of PFC and reference road segments to perform Bayesian inference on the safety effectiveness. A data-augmentation based computationally efficient algorithm was employed for a fully Bayesian estimation. The statistical analysis shows that PFC is not effective in reducing wet weather crashes. It should be noted that the findings of this study are in agreement with the existing literature, although these studies were not based on a fully Bayesian statistical analysis. Our study suggests that the safety effectiveness of PFC road surfaces, or any other safety infrastructure, largely relies on its interrelationship with the road user. The resultsAbstract: Permeable friction course (PFC), a porous hot-mix asphalt, is typically applied to improve wet weather safety on high-speed roadways in Texas. In order to warrant expensive PFC construction, a statistical evaluation of its safety benefits is essential. Generally, the literature on the effectiveness of porous mixes in reducing wet-weather crashes is limited and often inconclusive. In this study, the safety effectiveness of PFC was evaluated using a fully Bayesian before–after safety analysis. First, two groups of road segments overlaid with PFC and non-PFC material were identified across Texas; the non-PFC or reference road segments selected were similar to their PFC counterparts in terms of site specific features. Second, a negative binomial data generating process was assumed to model the underlying distribution of crash counts of PFC and reference road segments to perform Bayesian inference on the safety effectiveness. A data-augmentation based computationally efficient algorithm was employed for a fully Bayesian estimation. The statistical analysis shows that PFC is not effective in reducing wet weather crashes. It should be noted that the findings of this study are in agreement with the existing literature, although these studies were not based on a fully Bayesian statistical analysis. Our study suggests that the safety effectiveness of PFC road surfaces, or any other safety infrastructure, largely relies on its interrelationship with the road user. The results suggest that the safety infrastructure must be properly used to reap the benefits of the substantial investments. … (more)
- Is Part Of:
- Accident analysis and prevention. Volume 80(2015)
- Journal:
- Accident analysis and prevention
- Issue:
- Volume 80(2015)
- Issue Display:
- Volume 80, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 80
- Issue:
- 2015
- Issue Sort Value:
- 2015-0080-2015-0000
- Page Start:
- 89
- Page End:
- 96
- Publication Date:
- 2015-07
- Subjects:
- Porous/permeable friction course (PFC) -- Wet weather safety -- Before–after safety analysis -- Fully Bayesian analysis -- Data augmentation
Accidents -- Prevention -- Periodicals
Accident Prevention -- Periodicals
Accidents -- Prévention -- Périodiques
363.106 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00014575 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.aap.2015.04.003 ↗
- Languages:
- English
- ISSNs:
- 0001-4575
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0573.130000
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