The objective of this study was to develop valid statistical collision models for multilane highway segments to examine the safety of curbs. For this, road geometric traffic and collision data for 2001 to 2003 were collected. The data included 2,274 collisions and 885 injury collisions that occurred on 191.85 mi of 199 directional segments in North Carolina. The authors applied a new modeling method of introducing variables into the model one by one in a multiplicative form. A nonlinear optimizing algorithm for estimating parameters using a negative binomial log likelihood function was adopted for the modeling. The functional form of the variable to be introduced was determined on the basis of the relationship between the recorded number of collisions and the number of collisions predicted by the current model without the variable. The integrate-differentiate method was applied to find candidate functional forms for each variable. Model selections were based on the −2 log likelihood and Bayesian information criterion statistics, and the cumulative residuals plot method to check the goodness of fit of the models was adopted. As a result of the modeling efforts, the annual average daily traffic, access point density, shoulder width, and shoulder type (including curb presence) variables were introduced to the final model for total collisions. The same variables except the shoulder type variable were introduced to the injury collision model. Overall, then, it appears that curbs mean fewer total collisions and no change in injury collisions as compared to no curbs on the sampled road segments.


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    Titel :

    Collision Models for Multilane Highway Segments to Examine Safety of Curbs


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Baek, Jongdae (Autor:in) / Hummer, Joseph E. (Autor:in)


    Erscheinungsdatum :

    01.01.2008




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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