Macroscopic State-Level Analysis of Pavement Roughness Using Time–Space Econometric Modeling Methods

Mehmet Fettahoglu, Sheikh Shahriar Ahmed, Irina Benedyk and Panagiotis Ch. Anastasopoulos ()
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Mehmet Fettahoglu: Department of Civil, Structural and Environmental Engineering, University at Buffalo—The State University of New York, Buffalo, NY 14260, USA
Sheikh Shahriar Ahmed: Steer Group, Brooklyn, NY 11201, USA
Irina Benedyk: Department of Civil, Structural and Environmental Engineering, University at Buffalo—The State University of New York, Buffalo, NY 14260, USA
Panagiotis Ch. Anastasopoulos: Department of Civil, Structural and Environmental Engineering, University at Buffalo—The State University of New York, Buffalo, NY 14260, USA

Sustainability, 2024, vol. 16, issue 20, 1-21

Abstract: This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level.

Keywords: pavement roughness; pavement conditions; spatial econometric modeling; time–space modeling; sustainable pavement rehabilitation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:20:p:9071-:d:1502423

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