Using a reliability process to reduce uncertainty in predicting crashes at unsignalized intersections

    Authors

    K. Haleem; M. Abdel-Aty;K. Mackie

    Comments

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    Abbreviated Journal Title

    Accid. Anal. Prev.

    Keywords

    Negative binomial model; Reliability; Bayesian updating; 3-Legged; unsignalized intersection; 4-Legged unsignalized intersection; Crash; prediction; MOTOR-VEHICLE CRASHES; DISPERSION PARAMETER; SIGNALIZED INTERSECTIONS; ACCIDENT MODELS; POISSON-GAMMA; ROAD SECTIONS; SITES; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation

    Abstract

    The negative binomial (NB) model has been used extensively by traffic safety analysts as a crash prediction model, because it can accommodate the over-dispersion criterion usually exhibited in crash count data. However, the NB model is still a probabilistic model that may benefit from updating the parameters of the covariates to better predict crash frequencies at intersections. The objective of this paper is to examine the effect of updating the parameters of the covariates in the fitted NB model using a Bayesian updating reliability method to more accurately predict crash frequencies at 3-legged and 4-legged unsignalized intersections. For this purpose, data from 433 unsignalized intersections in Orange County, Florida were collected and used in the analysis. Four Bayesian-structure models were examined: (1) a non-informative prior with a log-gamma likelihood function, (2) a non-informative prior with an NB likelihood function, (3) an informative prior with an NB likelihood function, and (4) an informative prior with a log-gamma likelihood function. Standard measures of model effectiveness, such as the Akaike information criterion (AIC), mean absolute deviance (MAD), mean square prediction error (MSPE) and overall prediction accuracy, were used to compare the NB and Bayesian model predictions. Considering only the best estimates of the model parameters (ignoring uncertainty), both the NB and Bayesian models yielded favorable results. However, when considering the standard errors for the fitted parameters as a surrogate measure for measuring uncertainty, the Bayesian methods yielded more promising results. The full Bayesian updating framework using the log-gamma likelihood function for updating parameter estimates of the NB probabilistic models resulted in the least standard error values. (C) 2009 Elsevier Ltd. All rights reserved.

    Journal Title

    Accident Analysis and Prevention

    Volume

    42

    Issue/Number

    2

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    654

    Last Page

    666

    WOS Identifier

    WOS:000275510600039

    ISSN

    0001-4575

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