Use of Pavement–Vehicle Interaction-Related Models to Estimate Excess Fuel Consumption of Pavement Alternatives During the Design Stage

The need to quantify the effect of various pavement characteristics on pavement–vehicle interactions (PVI) and the associated excess fuel consumption (EFC) has been identified by practioners of pavement life cycle assessment (LCA), particularly for the use phase. With the current need to reduce carb...

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Veröffentlicht in:Transportation research record 2023-03, Vol.2677 (3), p.104-112
Hauptverfasser: Mohanraj, Kiran, Merritt, David K.
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description The need to quantify the effect of various pavement characteristics on pavement–vehicle interactions (PVI) and the associated excess fuel consumption (EFC) has been identified by practioners of pavement life cycle assessment (LCA), particularly for the use phase. With the current need to reduce carbon emissions, if found to be significant, these effects might also need to be considered by agencies when making investment decisions or when evaluating pavement design and rehabilitation strategies. Several studies have evaluated rolling resistance factors such as pavement roughness, macrotexture, and structural response (SR) to loading to generate models to predict EFC. Available PVI models consider either the effects of pavement surface characteristics (PSC) or SR only, and it is not possible to estimate the total PVI-related EFC using just one model. This paper summarizes a study demonstrating the use of select PVI models for estimating EFC during the pavement design stage for a new construction or reconstruction project and attempts to combine the results of PSC models with SR models to calculate the total contribution of PVI to EFC. The results from the analysis may be helpful for evaluating alternative pavement design strategies, materials, or surface textures for inclusion in the final design and project specifications, provided the gaps and limitations of the models are well understood and considered. The study also identified challenges that might be encountered by agencies in performing such analyses, such as availability of high-quality data for longer analysis periods and lack of measurement methods for ground truth.
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