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• W2025096743 endingPage "144" @default.
• W2025096743 startingPage "133" @default.
• W2025096743 abstract "The Highway Safety Manual (HSM) recommends using the empirical Bayes (EB) method with locally derived calibration factors to predict an agency’s safety performance. However, the data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of the data variables identified in the HSM are currently unavailable in the states’ databases. Moreover, the process of collecting and maintaining all the HSM data variables is cost-prohibitive. Prioritization of the variables based on their impact on crash predictions would, therefore, help to identify influential variables for which data could be collected and maintained for continued updates. This study aims to determine the impact of each independent variable identified in the HSM on crash predictions. A relatively recent data mining approach called boosted regression trees (BRT) is used to investigate the association between the variables and crash predictions. The BRT method can effectively handle different types of predictor variables, identify very complex and non-linear association among variables, and compute variable importance. Five years of crash data from 2008 to 2012 on two urban and suburban facility types, two-lane undivided arterials and four-lane divided arterials, were analyzed for estimating the influence of variables on crash predictions. Variables were found to exhibit non-linear and sometimes complex relationship to predicted crash counts. In addition, only a few variables were found to explain most of the variation in the crash data." @default.
• W2025096743 created "2016-06-24" @default.
• W2025096743 creator A5001769956 @default.
• W2025096743 creator A5036836776 @default.
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• W2025096743 date "2015-06-01" @default.
• W2025096743 modified "2023-10-06" @default.
• W2025096743 title "Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees" @default.
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• W2025096743 doi "https://doi.org/10.1016/j.aap.2015.03.011" @default.
• W2025096743 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25823903" @default.
• W2025096743 hasPublicationYear "2015" @default.
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