Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico |
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| Affiliation: | 1. Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA;2. Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA;1. Department of Astronomy, Faculty of Mathematics, University of Belgrade, Studentski trg 16, 11000 Belgrade, Serbia;2. Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA;3. INAF – Osservatorio Astrofisico di Torino, Via Osservatorio 20, I-10025 Pino Torinese, Italy;4. Fundación Galileo Galilei – INAF Rambla José Ana Fernández Pérez, 7 38712 Breña Baja, TF, Spain;1. Ondokuz Mayis University, Department of Civil Engineering, Samsun, Turkey;2. Istanbul University, Department of Civil Engineering, Istanbul, Turkey |
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| Abstract: | Roadway networks, as part of transportation infrastructure, play an indispensable role in regional economies and community development. The high-quality pavement serviceability of these networks is essential to ensure safe, cost-effective daily traffic operations. In-depth analyses of network-wide pavement surface condition data are necessary inputs for optimal pavement design and maintenance, traffic safety enhancement, and sustainable traffic infrastructure system development. This study aims to investigate various pavement distress condition performance measurements and their correlations to better understand temporal–spatial characteristics of roadway distress based on pavement distress condition data collected in New Mexico from 2006 to 2009. Eight major corridors across various urban and rural areas were selected for analyzing pavement surface-distress conditions and discovering their intrinsic characteristics and patterns across both temporal and spatial domains. The results show that there are not strong correlations among different distress measurements, implying the rationality of the current pavement performance measurement protocol used by the state transportation agencies. Regression models were established and GIS-based spatial analyses were performed to extract temporal and spatial patterns of Distress Rate (DR) data. The model results illustrate significant correlations of the DR data on the same route between two consecutive years, which can be partially characterized by a Markov process. GIS-based spatial investigations also show unique features of pavement condition deterioration attributed to diverse geometric characteristics and traffic conditions, such as vehicle compositions and volumes and urban and rural areas. The research findings are helpful to understand the characteristics of pavement distress conditions more clearly and to optimize traffic infrastructure design and maintenance. |
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| Keywords: | Pavement distress conditions GIS-based temporal and spatial analysis Regression data |
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