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

Abstract

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.

Keywords

infrastructure pavements sustainable and resilient pavements pavement and urban climate sustainable pavements life cycle assessment (LCA)

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References

Mohanraj

Merritt

Sivaneswaran

Dylla

PVI Related Decision-Making Tools in Use Phase of LCA: A Literature Review. Proc., International Symposium on Pavement, Roadway, and Bridge Life Cycle Assessment, (LCA 2020), Virtual, 2020.

Chatti

Zaabar

Estimating the Effects of Pavement Condition on Vehicle Operating Costs. NCHRP Report 720. National Academies Press, Washington, D.C., 2012.

Karlsson

Carlson

Dolk

Energy Use Generated by Traffic and Pavement Maintenance: Decision Support for Optimization of Low Rolling Resistance Maintenance Treatments. Vti Notat. Statens väg-och transportforskningsinstitut, Linköping, 2012, p. 43.

Ziyadi

Ozer

Kang

Al-Qadi

I. L.

Vehicle Energy Consumption and an Environmental Impact Calculation Model for the Transportation Infrastructure Systems. Journal of Cleaner Production, Vol. 174, 2018, pp. 424–436.

Hajj

E. Y.

Bailey

Sime

Chkaiban

Kazemi

S. F.

Sebaaly

P. E.

Enhanced Prediction of Vehicle Fuel Economy and Other Operating Costs, Phase I: Modeling the Relationship Between Vehicle Speed and Fuel. Technical Report. Federal Highway Administration, Washington, D.C., 2017.

Hajj

E. Y.

Sime

Chkaiban

Bailey

Sebaaly

P. E.

Enhanced Prediction of Vehicle Fuel Economy and Other Operating Costs, Phase II: Modeling the Relationship Between Pavement Roughness, Speed, Roadway Characteristics and Vehicle Operating Costs. Technical Report. Federal Highway Administration, Washington, D.C., 2018.

Louhghalam

Tootkaboni

Igusa

Ulm

F. J.

Closed-Form Solution of Road Roughness-Induced Vehicle Energy Dissipation. Journal of Applied Mechanics, Vol. 86, No. 1, 2018, p. 011003.

Louhghalam

Akbarian

Ulm

F. J.

Scaling Relationships of Dissipation-Induced Pavement-Vehicle Interactions. Transportation Research Record: Journal of the Transportation Research Board, 2014. 2457: 95–104.

Estaji

Coleri

Harvey

J. T.

Butt

A. A.

Predicting Excess Vehicle Fuel Use due to Pavement Structural Response Using Field Test Results and Finite Element Modelling. International Journal of Pavement Engineering, Vol. 42, No. 8, 2021, pp. 973–983.

10.

Balzarini

Zaabar

Chatti

Impact of PCC Pavement Structural Rolling Resistance on Vehicle Fuel Economy. Proc., The International Pavement Life-Cycle Assessment Symposium Convened, Champaign, IL, 2017, p. 24. https://doi.org/doi:10.1201/9781315159324-8.

11.

Balzarini

Zaabar

Chatti

Effect of Pavement Structural Response on Rolling Resistance and Fuel Economy Using a Mechanistic Approach. In Advances in Materials and Pavement Performance Prediction ( Masad

Bhasin

Scarpas

Menapace

Kumar

, eds.), CRC Press, London, UK, 2018, pp. 49–51.

12.

Mohanraj

Merritt

Sivaneswaran

Dylla

Parametric and Sensitivity Analysis of PVI-Related Surface Characteristics Models for Fuel Consumption. In Advances in Materials and Pavement Performance Prediction (AM3P 2020) ( Kumar

Papagiannakis

A. T.

Bhasin

Little

, eds.), CRC Press, London, UK, 2020.

13.

Wang

Lee

Harvey

Kendall

Lee

Kim

UCPRC Life Cycle Assessment Methodology and Initial Case Studies for Energy Consumption and GHG Emissions for Pavement Preservation Treatments With Different Rolling Resistance. UCPRC-RR-2012-02. University of California, Davis, 2012.

14.

AASHTO. Mechanistic-Empirical Pavement Design Guide - A Manual of Practice, 2nd ed. American Association of State Highway and Transportation Officials, Washington, D.C., 2015.

15.

Khazanovich

Tayabji

S. D.

Darter

M. I.

Backcalculation of Layer Parameters for LTPP Test Sections, Volume I: Slab on Elastic Solid and Slab on Dense-Liquid Foundation Analysis of Rigid Pavements. FHWA-RD-00-086. FHWA, U.S. Department of Transportation, McLean, VA, 2001.

16.

Diefenderfer

B. K.

Al-Qadi

I. L.

Diefenderfer

S. D.

Model to Predict Pavement Temperature Profile: Development and Validation. Journal of Transportation Engineering, Vol. 132, No. 2, 2006, pp. 162–167. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:2(162).

17.

NREL. Solar Resource Data, Tools, and Maps. https://www.nrel.gov/gis/solar.html. Accessed October 9, 2020.