{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/124338"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/124338","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Pavement Surface Characteristics Evaluation Using Vehicle-Based Data Collection","abstract":"Various methods are used to collect pavement surface conditions, varying from manual checks by employees to specialized equipment. However, traditional methods usually require expensive specialized equipment and are time-consuming and costly. This thesis examines the use of connected vehicles (CV) to collect pavement surface data estimated based on sensors mounted in the cars. This concept was first analyzed through a literature review, where CV technology was examined to measure roughness, friction, and pothole data. Data collection was performed by a specialized company, utilizing sensor data from standard manufactured cars. A sample of data from the Richmond district of Virginia, was used to compare the estimated values to the standard International Roughness Index (IRI) values used by VDOT. Data collected using both methods were matched using Matlab code to have a common linear referencing system. Subjective visual comparison showed that both data sets had similar trends, highlighting roads with rough sections. A quantitative analysis performed to compare the average results of the two methods on a sample of uniform sections, showed a high correlation. A technology assessment was also conducted to evaluate the maturity level of the CV, which was found to be at least a TRL 7. This suggests that CV technology can be a valuable addition to the traditional methods for collecting pavement surface data.","abstract_html":"Various methods are used to collect pavement surface conditions, varying from manual checks by employees to specialized equipment. However, traditional methods usually require expensive specialized equipment and are time-consuming and costly. This thesis examines the use of connected vehicles (CV) to collect pavement surface data estimated based on sensors mounted in the cars. This concept was first analyzed through a literature review, where CV technology was examined to measure roughness, friction, and pothole data. Data collection was performed by a specialized company, utilizing sensor data from standard manufactured cars. A sample of data from the Richmond district of Virginia, was used to compare the estimated values to the standard International Roughness Index (IRI) values used by VDOT. Data collected using both methods were matched using Matlab code to have a common linear referencing system. Subjective visual comparison showed that both data sets had similar trends, highlighting roads with rough sections. A quantitative analysis performed to compare the average results of the two methods on a sample of uniform sections, showed a high correlation. A technology assessment was also conducted to evaluate the maturity level of the CV, which was found to be at least a TRL 7. This suggests that CV technology can be a valuable addition to the traditional methods for collecting pavement surface data.","abstract_has_math":false,"creators":["Mardirossian, Grace"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Civil Engineering","degree_department":"Civil and Environmental Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Flintsch, Gerardo W."],"committee_members":["de Leon Izeppi, Edgar D.","Trani, Antonio A."],"year":2025,"date_issued":"2025-01-23","date_published":"2025-01-23","updated_at":"2026-07-22T22:19:15Z","subjects":["connected vehicle","data collection","pavement management system","roughness"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:41801"],"render_values":[{"text":"vt_gsexam:41801","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/124338","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Flintsch, Gerardo W."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["de Leon Izeppi, Edgar D.","Trani, Antonio A."]},{"key":"dc:contributor.department","label":"Department","values":["Civil and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Mardirossian, Grace"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-01-24T09:00:56Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-01-24T09:00:56Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-01-23"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["connected vehicle","data collection","pavement management system","roughness"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:41801"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/124338"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Various methods are used to collect pavement surface conditions, varying from manual checks by employees to specialized equipment. However, traditional methods usually require expensive specialized equipment and are time-consuming and costly. This thesis examines the use of connected vehicles (CV) to collect pavement surface data estimated based on sensors mounted in the cars. This concept was first analyzed through a literature review, where CV technology was examined to measure roughness, friction, and pothole data. Data collection was performed by a specialized company, utilizing sensor data from standard manufactured cars. A sample of data from the Richmond district of Virginia, was used to compare the estimated values to the standard International Roughness Index (IRI) values used by VDOT. Data collected using both methods were matched using Matlab code to have a common linear referencing system. Subjective visual comparison showed that both data sets had similar trends, highlighting roads with rough sections. A quantitative analysis performed to compare the average results of the two methods on a sample of uniform sections, showed a high correlation. A technology assessment was also conducted to evaluate the maturity level of the CV, which was found to be at least a TRL 7. This suggests that CV technology can be a valuable addition to the traditional methods for collecting pavement surface data."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Traditional methods of assessing road conditions usually require expensive specialized equipment and can be time-consuming and expensive. This study explores a new approach, using connected vehicles to gather road data through car sensors. A review of existing research showed that connected vehicle technology could measure road surface characteristics such as roughness, friction, and potholes. Data collected from cars in Virginia's Richmond district was compared to standard road condition data from the Virginia Department of Transportation. Both methods showed similar results, with a high correlation value, indicating a strong match. The technology behind connected vehicles was assessed and found to be a potentially useful, cost-effective alternative to traditional road condition assessment methods."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Pavement Surface Characteristics Evaluation Using Vehicle-Based Data Collection"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Flintsch, Gerardo W."],"dc:contributor.committeemember":["de Leon Izeppi, Edgar D.","Trani, Antonio A."],"dc:contributor.department":["Civil and Environmental Engineering"],"dc:creator":["Mardirossian, Grace"],"dc:date.accessioned":["2025-01-24T09:00:56Z"],"dc:date.available":["2025-01-24T09:00:56Z"],"dc:date.issued":["2025-01-23"],"dc:description.abstract":["Various methods are used to collect pavement surface conditions, varying from manual checks by employees to specialized equipment. 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A quantitative analysis performed to compare the average results of the two methods on a sample of uniform sections, showed a high correlation. A technology assessment was also conducted to evaluate the maturity level of the CV, which was found to be at least a TRL 7. This suggests that CV technology can be a valuable addition to the traditional methods for collecting pavement surface data."],"dc:description.abstractgeneral":["Traditional methods of assessing road conditions usually require expensive specialized equipment and can be time-consuming and expensive. This study explores a new approach, using connected vehicles to gather road data through car sensors. A review of existing research showed that connected vehicle technology could measure road surface characteristics such as roughness, friction, and potholes. Data collected from cars in Virginia's Richmond district was compared to standard road condition data from the Virginia Department of Transportation. 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