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Pavement Quality Evaluation Using Connected Vehicle Data
Modern vehicles have extensive instrumentation that can be used to actively assess the condition of infrastructure such as pavement markings, signs, and pavement smoothness. Currently, pavement condition evaluations are performed by state and federal officials typically using the industry standard o...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737007/ https://www.ncbi.nlm.nih.gov/pubmed/36501810 http://dx.doi.org/10.3390/s22239109 |
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author | Mahlberg, Justin A. Li, Howell Zachrisson, Björn Leslie, Dustin K. Bullock, Darcy M. |
author_facet | Mahlberg, Justin A. Li, Howell Zachrisson, Björn Leslie, Dustin K. Bullock, Darcy M. |
author_sort | Mahlberg, Justin A. |
collection | PubMed |
description | Modern vehicles have extensive instrumentation that can be used to actively assess the condition of infrastructure such as pavement markings, signs, and pavement smoothness. Currently, pavement condition evaluations are performed by state and federal officials typically using the industry standard of the International Roughness Index (IRI) or visual inspections. This paper looks at the use of on-board sensors integrated in Original Equipment Manufacturer (OEM) connected vehicles to obtain crowdsource estimates of ride quality using the International Rough Index (IRI). This paper presents a case study where over 112 km (70 mi) of Interstate-65 in Indiana were assessed, utilizing both an inertial profiler and connected production vehicle data. By comparing the inertial profiler to crowdsourced connected vehicle data, there was a linear correlation with an R(2) of 0.79 and a p-value of <0.001. Although there are no published standards for using connected vehicle roughness data to evaluate pavement quality, these results suggest that connected vehicle roughness data is a viable tool for network level monitoring of pavement quality. |
format | Online Article Text |
id | pubmed-9737007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97370072022-12-11 Pavement Quality Evaluation Using Connected Vehicle Data Mahlberg, Justin A. Li, Howell Zachrisson, Björn Leslie, Dustin K. Bullock, Darcy M. Sensors (Basel) Article Modern vehicles have extensive instrumentation that can be used to actively assess the condition of infrastructure such as pavement markings, signs, and pavement smoothness. Currently, pavement condition evaluations are performed by state and federal officials typically using the industry standard of the International Roughness Index (IRI) or visual inspections. This paper looks at the use of on-board sensors integrated in Original Equipment Manufacturer (OEM) connected vehicles to obtain crowdsource estimates of ride quality using the International Rough Index (IRI). This paper presents a case study where over 112 km (70 mi) of Interstate-65 in Indiana were assessed, utilizing both an inertial profiler and connected production vehicle data. By comparing the inertial profiler to crowdsourced connected vehicle data, there was a linear correlation with an R(2) of 0.79 and a p-value of <0.001. Although there are no published standards for using connected vehicle roughness data to evaluate pavement quality, these results suggest that connected vehicle roughness data is a viable tool for network level monitoring of pavement quality. MDPI 2022-11-24 /pmc/articles/PMC9737007/ /pubmed/36501810 http://dx.doi.org/10.3390/s22239109 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mahlberg, Justin A. Li, Howell Zachrisson, Björn Leslie, Dustin K. Bullock, Darcy M. Pavement Quality Evaluation Using Connected Vehicle Data |
title | Pavement Quality Evaluation Using Connected Vehicle Data |
title_full | Pavement Quality Evaluation Using Connected Vehicle Data |
title_fullStr | Pavement Quality Evaluation Using Connected Vehicle Data |
title_full_unstemmed | Pavement Quality Evaluation Using Connected Vehicle Data |
title_short | Pavement Quality Evaluation Using Connected Vehicle Data |
title_sort | pavement quality evaluation using connected vehicle data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737007/ https://www.ncbi.nlm.nih.gov/pubmed/36501810 http://dx.doi.org/10.3390/s22239109 |
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