Pavement management, a subdivision of transportation asset management, seeks to use finite and limited resources to maintain or improve the condition of a pavement network for a defined time horizon. Pavement performance prediction is integral to pavement management, and among other uses, it contributes to budget projections and allows scenario analyses to be conducted. This paper documents the evaluation of the original pavement performance models developed for the U.S. National Park Service (NPS) pavement network using historical condition data. The Federal Lands Highway Program began to use a pavement management system for NPS pavements in 2005, and the results in this paper represent an evaluation of those models with an emphasis on developing recommendations for improvement. The main finding of the work presented in this paper is that the current NPS models result in significant overprediction of deterioration in practically all cases, although the effect is more pronounced for those measures associated with structural failure. This means that the NPS pavement network is in better condition than the models predict. The performance prediction models for those segments that received preservation treatments are also investigated in this paper, and it is found that the model bias is less for these than for the models predicting condition without a preservation treatment. However, this is attributed to the segments that received preservation treatments being in good condition. Overall, the findings point to a need to recalibrate or redevelop the models to improve their prediction capabilities.


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    Titel :

    Evaluation of Pavement Performance Models Using Historical Condition Data for the U.S. National Parks Pavement Network


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:


    Erscheinungsdatum :

    03.03.2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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