A Bayesian procedure for evaluating the frequency of calibration factor updates in highway safety manual (HSM) applications. (January 2017)

• Record Type: Journal Article
• Authors: Saha, Dibakar; Alluri, Priyanka; Gan, Albert
• Is Part Of: Accident analysis and prevention. Volume 98(2017)
• Page Start: 74
• Page End: 86
• Publication Date: 2017-01
• Subjects: Highway Safety Manual -- Calibration factor -- Bayesian estimation -- Marcov chain Monte Carlo -- Gibbs sampling -- Region of practical equivalence; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2016.09.027
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: The variations between triennial, biennial, and annual calibration factors were assessed. A Bayesian estimation technique was applied to obtain comprehensive information about model parameters. The posterior distribution of the mean parameter was used to decide whether the variation is credibly high. Calibration factors need to be updated every two years, if variations not more than 0.01 are desired. Updating calibration factors at a frequency of three years is suggested for variations between 0.01 and 0.05. Abstract: The Highway Safety Manual (HSM) presents statistical models to quantitatively estimate an agency's safety performance. The models were developed using data from only a few U.S. states. To account for the effects of the local attributes and temporal factors on crash occurrence, agencies are required to calibrate the HSM-default models for crash predictions. The manual suggests updating calibration factors every two to three years, or preferably on an annual basis. Given that the calibration process involves substantial time, effort, and resources, a comprehensive analysis of the required calibration factor update frequency is valuable to the agencies. Accordingly, the objective of this study is to evaluate the HSM's recommendation and determine the required frequency of calibration factor updates. A robust Bayesian estimation procedure is used to assess the variation between calibration factors computed annually, biennially, and triennially using data collected from over 2400 miles of segments and over 700 intersections on urban and suburban facilities in Florida. Bayesian model yields a posterior distribution of the model parameters that give credible information to infer whether the difference between calibration factors computed at specified intervals is credibly different from the null value which represents unaltered calibration factors between the comparison years or in other words, zero difference. The concept of the null value is extended to include the range of values that are practically equivalent to zero. Bayesian inference shows that calibration factors based on total crash frequency are required to be updated every two years in cases where the variations between calibration factors are not greater than 0.01. When the variations are between 0.01 and 0.05, calibration factors based on total crash frequency could be updated every three years.