Compared with roadway segments and intersections, the safety of interchange ramp segments has not been studied extensively, especially in the context of commercial motor vehicles (CMVs). The main objective of this study was to develop a safety performance function (SPF) tool for predicting CMV crashes occurring on interchange ramp segments. Four count models, including the negative binomial (NB), heterogeneous NB (HTNB), standard Conway–Maxwell–Poisson (CMP), and heterogeneous Conway–Maxwell–Poisson (HTCMP), were used and compared while fitting CMV crash-specific SPFs along interchange ramp segments in Kentucky. The HTCMP model, which is an extension of the standard CMP model, is a more flexible approach that handles both over-dispersed and under-dispersed crash data while exhibiting varying dispersion parameters. Five-year (2015 to 2019) CMV-related crashes along Kentucky’s ramp segments were used. The model comparison results showed that the HTCMP significantly outperformed the other three models in crash prediction accuracy and goodness-of-fit statistics (e.g., the Akaike information criterion, Bayesian information criterion, and McFadden’s Pseudo R-squared). The SPF model results using the HTCMP approach indicated that on-ramps (relative to off-ramps), ramp annual average daily traffic, ramp configuration, left shoulder width, ramp gore length, absence of left roadside barrier, and presence of other merging or diverging ramps within the ramp of interest were significantly associated with CMV crash frequency on ramp segments. Potential safety countermeasures were proposed, for example, increasing ramp gore length to be at least 730 ft (since this was associated with a reduction in CMV crashes on ramp segments).


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    Titel :

    Developing Safety Performance Functions for Commercial Motor Vehicle Crashes at Interchange Ramp Segments in Kentucky


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:


    Erscheinungsdatum :

    2023-03-03




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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