Use Of Empirical And Full Bayes Before–After Approaches To Estimate The Safety Effects Of Roadside Barriers With Different Crash Conditions

Keywords

Crash modification factors; Empirical and Full Bayes; Highway safety manual; Roadside barriers; Safety effectiveness

Abstract

Introduction Although many researchers have estimated the crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of prior studies that have explored the variation of CMFs. Thus, the main objectives of this study are: (a) to estimate CMFs for the installation of different types of roadside barriers, and (b) to determine the changes of safety effects for different crash types, severities, and conditions. Method Two observational before–after analyses (i.e. empirical Bayes (EB) and full Bayes (FB) approaches) were utilized in this study to estimate CMFs. To consider the variation of safety effects based on different vehicle, driver, weather, and time of day information, the crashes were categorized based on vehicle size (passenger and heavy), driver age (young, middle, and old), weather condition (normal and rain), and time difference (day time and night time). Results The results show that the addition of roadside barriers is safety effective in reducing severe crashes for all types and run-off roadway (ROR) crashes. On the other hand, it was found that roadside barriers tend to increase all types of crashes for all severities. The results indicate that the treatment might increase the total number of crashes but it might be helpful in reducing injury and severe crashes. In this study, the variation of CMFs was determined for ROR crashes based on the different vehicle, driver, weather, and time information. Practical applications Based on the findings from this study, the variation of CMFs can enhance the reliability of CMFs for different roadway conditions in decision making process. Also, it can be recommended to identify the safety effects of specific treatments for different crash types and severity levels with consideration of the different vehicle, driver, weather, and time of day information.

Publication Date

9-1-2016

Publication Title

Journal of Safety Research

Volume

58

Number of Pages

31-40

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jsr.2016.06.002

Socpus ID

84977107094 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84977107094

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