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dc.contributor.authorBridgelall, Raj
dc.description.abstractThere are many important applications that require ride quality characterization. However, the only international standard that specifies a roughness index is not suitable for applications beyond assessing the ride quality of paved roads. Other potential applications include automated ride quality characterization of gravel roads, bike or wheelchair paths, railways, rivers, airways, hyperloops, and elevator channels. This work proposes a composite index that characterizes roughness from multidimensional movements along any path. Statistical tests demonstrate two important properties—that the index is consistent based on an ever-decreasing margin-of-error of the mean, and distinguishable among different paths. A low-cost sensor package of accelerometers, gyroscopes, and a speedometer produced the data for spatio-temporal transformation. The experiments conducted on buses revealed that both the consistency and distinguishability of the index improves with the number of measurements. The approach is best suited for applications that can use in-situ sensors or crowdsensing to automate ride quality characterization.en_US
dc.rightsIn copyright. Permission to make this version available has been granted by the author and publisher.
dc.titleCharacterizing Ride Quality With a Composite Roughness Indexen_US
dc.typeArticleen_US
dc.typePreprinten_US
dc.descriptionRaj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).en_US
dc.date.accessioned2022-06-03T20:49:55Z
dc.date.available2022-06-03T20:49:55Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/10365/32678
dc.subjectInternational roughness index.en_US
dc.subjectRide comfort.en_US
dc.subjectMaintenance decision support system.en_US
dc.subjectProbe vehicle.en_US
dc.subjectVehicle design.en_US
dc.identifier.orcid0000-0003-3743-6652
dc.identifier.citationBridgelall, Raj. "Characterizing Ride Quality With a Composite Roughness Index." IEEE Transactions on Intelligent Transportation Systems, DOI:10.1109/TITS.2021.3140177, January 2022.en_US
dc.description.urihttps://www.ugpti.org/about/staff/viewbio.php?id=79
dc.language.isoen_USen_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.contributor.organizationUpper Great Plains Transportation Institute
ndsu.collegeCollege of Business
ndsu.departmentTransportation and Logistics
dc.identifier.doi10.1109/TITS.2021.3140177


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