Highlights Nonlinear safety performance functions fit the signalized intersection crash data better. Used generalized additive models to identify appropriate safety performance functions. It is important to consider the joint safety impacts of major- and minor-approach AADTs. Generalized additive models with isotropic smooth perform the best.

    Abstract Annual Average Daily Traffic (AADT) is often considered as a main covariate for predicting crash frequencies at urban and suburban intersections. A linear functional form is typically assumed for the Safety Performance Function (SPF) to describe the relationship between the natural logarithm of expected crash frequency and covariates derived from AADTs. Such a linearity assumption has been questioned by many researchers. This study applies Generalized Additive Models (GAMs) and Piecewise Linear Negative Binomial (PLNB) regression models to fit intersection crash data. Various covariates derived from minor-and major-approach AADTs are considered. Three different dependent variables are modeled, which are total multiple-vehicle crashes, rear-end crashes, and angle crashes. The modeling results suggest that a nonlinear functional form may be more appropriate. Also, the results show that it is important to take into consideration the joint safety effects of multiple covariates. Additionally, it is found that the ratio of minor to major-approach AADT has a varying impact on intersection safety and deserves further investigations.


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    Title :

    Modeling the effects of AADT on predicting multiple-vehicle crashes at urban and suburban signalized intersections


    Contributors:
    Chen, Chen (author) / Xie, Yuanchang (author)

    Published in:

    Publication date :

    2016-02-19


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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