# ASTM E1489-08 (Reapproved 2013)

Designation: E1489 − 08 (Reapproved 2013)Standard Practice forComputing Ride Number of Roads from Longitudinal ProfileMeasurements Made by an Inertial Profile MeasuringDevice1This standard is issued under the fixed designation E1489; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon (´) indicates an editorial change since the last revision or reapproval.1. Scope1.1 This practice covers the mathematical processing oflongitudinal profile measurements to produce an estimate ofsubjective ride quality, termed Ride Number (RN).1.2 The intent of this practice is to provide the highwaycommunity a standard practice for the computing and reportingof an estimate of subjective ride quality for highway pave-ments.1.3 This practice is based on an algorithm developed inNational Cooperative Highway Research Project (NCHRP)1–23 (1 and 2),2two Ohio Department of Transportation ridequality research projects (3 and 4), and work presented in Refs(5 and 6).1.4 The computed estimate of subjective ride quality pro-duced by this practice was named Ride Number (RN) inNCHRPResearch Project 1–23 (1 and 2) to differentiate it fromother measures of ride quality computed from longitudinalprofile. Eq 1 of Section 8.2 represents the mathematicaldefinition of Ride Number.1.5 This standard does not purport to address all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:3E177 Practice for Use of the Terms Precision and Bias inASTM Test MethodsE867 Terminology Relating to Vehicle-Pavement SystemsE950 Test Method for Measuring the Longitudinal Profile ofTraveled Surfaces with an Accelerometer EstablishedInertial Profiling ReferenceE1170 Practices for Simulating Vehicular Response to Lon-gitudinal Profiles of Traveled SurfacesE1364 Test Method for Measuring Road Roughness byStatic Level MethodE1500 Practice for Computing Mean Square Numerics fromRoad Surface Elevation Profile Records (Withdrawn1998)4E1656 Guide for Classification of Automated PavementCondition Survey EquipmentE1927 Guide for Conducting Subjective Pavement RideQuality Ratings3. Terminology3.1 Terminology used in this standard conforms to thedefinitions included in Terminology E867.3.2 Definitions:3.2.1 Rideability Index (RI)—an index derived from con-trolled measurements of longitudinal profile in the wheel tracksand correlated with panel ratings of rideability.3.2.2 Ride Number (RN)—rideability index of a pavementusing a scale of 0 to 5, with 5 being perfect and 0 beingimpassable.4. Summary of Practice4.1 The practice presented here was developed specificallyfor estimating subjective ride quality from longitudinal profilemeasurements.4.2 This practice uses longitudinal profile measurements fortwo wheel tracks as an input to a mathematical computation ofestimated subjective ride quality (RN). The profile must berepresented as a series of elevation values taken at constantintervals along the wheel tracks.1This practice is under the jurisdiction of ASTM Committee E17 on Vehicle -Pavement Systems and is the direct responsibility of Subcommittee E17.33 onMethodology for Analyzing Pavement Roughness.Current edition approved Dec. 1, 2013. Published February 2014. Originallyapproved in 1996. Last previous edition approved in 2008 as E1489 – 08. DOI:10.1520/E1489-08R13.2The boldface numbers in parentheses refer to the list of references at the end ofthis standard.3For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at service@astm.org. For Annual Book of ASTMStandards volume information, refer to the standard’s Document Summary page onthe ASTM website.4The last approved version of this historical standard is referenced onwww.astm.org.Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States14.3 The range of the computed subjective ride qualityestimate is 0 to 5.0 Ride Number (RN) units where an RN of5.0 is considered to be a perfect ride quality road. The 0 to 5.0Ride Number scale is defined in Refs (1, 2, 3, 4, and 5).4.3.1 In the 0 to 5.0 Ride Number rating scale, the endpoints and some of the intermediate points have the followingdescriptions:Ride Number Rating ScaleDescription Ride NumberPerfect 5.0Very good 4.54.0Good 3.53.0Fair 2.52.0Poor 1.51.0Very poor 0.5Impassable 0.04.3.2 The end points are further defined by the followingdescriptions (1):Perfect: A road which is so smooth that at the speed you aretraveling you would hardly know the road was there. Youdoubt that if someone made the surface smoother that theride would be noticeably nicer.Impassable: A road which is so bad that you doubt that you or the carwill make it to the end at the speed you are traveling—likedriving down railroad tracks along the ties.4.4 The quality of the computed subjective ride qualityestimate (RN) produced by this practice is based on theprocessing of the longitudinal profile as measured with a roadprofile measuring device that meets the Class 1 requirements ofASTM Standard E950.NOTE 1—Less accurate Ride Number values will result from RoadProfile Data obtained from Profile Measuring Devices that are lessaccurate than class I (E950).5. Significance and Use5.1 This practice provides a means for obtaining a quanti-tative estimate of a pavement property defined as ride qualityor rideability using longitudinal profile measuring equipment.5.1.1 The Ride Number (RN) is portable because it can beobtained from longitudinal profiles obtained with a variety ofinstruments.5.1.2 The RN is stable with time because true RN is basedon the concept of a true longitudinal profile, rather than thephysical properties of particular type of instrument.5.2 Ride quality information is a useful input to the pave-ment manage systems (PMS) maintained by transportationagencies.5.2.1 The subjective ride quality estimate produced by thispractice has been determined (6) to be highly correlated (r =0.92) with measured subjective ride quality and to produce alow standard estimate of error (0.29 RN units) for the ridequality estimate.5.2.2 The subjective ride quality estimates produced by thispractice were found to be not significantly different withrespect to pavement type, road class, vehicle size, vehiclespeed (within posted speed limits), and regionality over therange of variables included in the experiment (1, 2, 3, and 4).5.2.3 The subjective ride quality estimates produced by thispractice have been found to be good predictors of the need ofnon-routine road maintenance for the various road classifica-tions (3).5.3 The use of this practice to produce subjective ridequality estimates from measured longitudinal profile eliminatesthe need for expensive ride panel studies to obtain the sameride quality information.6. Longitudinal Profile Measurement6.1 The elevation profile data used in this practice musthave sufficient accuracy to measure the longitudinal profileattributes that are essential to the computation of estimatedsubjective ride quality.6.1.1 The quality of the Ride Number estimates cited in thispractice are based on the use of elevation profile measurementsmade with a Class 1 road profile measuring device as definedin Test Method E950.6.1.2 Wave Length Content—The measured longitudinalprofile used as input to this practice must have the wavelengthcontent required for the application.6.1.2.1 As a guide to the wavelength requirement, a repeat-ing sine wave of the following wavelengths and peak-to-peakamplitudes in the absence of any other roughness will producethe following RN values:Amplitude, Wavelength,mm m RN25.4 91.4 4.95Amplitude, Wavelength,inch feet RN1.00 300 4.956.1.2.2 The quality of Ride Number estimates cited in thispractice are based on measured longitudinal profile withwavelength content up to 91.4 m (300 feet).7. Precision and Bias7.1 Precision and Bias—The accuracy of the computedsubjective ride quality estimate produced by this practice willbe a function of the accuracy of the longitudinal profilemeasurements.7.1.1 Correlation—The ride quality estimates (RN) com-puted by this practice have been determined to have acorrelation coefficient of .92 (r) with actual measured subjec-tive ride quality (3, 4, 5, and 6).7.2 Standard Error of Estimate—The ride quality estimates(RN) computed by this practice have been determined to havea Standard Error of Estimate of .29 RN units when comparedto actual measured subjective ride quality (3, 4, 5, and 6).7.3 The Correlation Coefficient and Standard Error of Esti-mate values cited in Sections 7.1 and 7.2 are based onlongitudinal profile measurements made with a road profilemeasuring device that meets the requirements of a Class 1measuring device as defined by ASTM Standard E950 andwavelength content up to 100 m (300 feet).7.4 It is not known how road profile measuring equipmentwith lesser resolution and precision and greater bias wouldaffect the accuracy of computed ride numbers.E1489 − 08 (2013)28. Ride Number Program8.1 This practice consists of the computation of RideNumber (RN) from an algorithm developed in National Coop-erative Highway Research Project (NCHRP) 1–23 (1 and 2),two Ohio DOT ride quality research projects (3 and 4), and thework presented in Refs (5) and (6).8.2 Ride Number is defined in this practice by the equation:RN 5 5e2160~PI!(1)where:PI 5ŒPIL21PIR22(2)and where:PILand PIRare Profile Indexes for the left and right wheelpaths, respectively, and are the computed root mean square(RMS) of the filtered slopes of the measured elevation profilesof the individual right and left wheel paths (6). The wavelength components of the profile slopes are modified by thefilter shown in Fig. 7 (6).8.3 A FORTRAN computer version of this algorithm hasbeen implemented as described in Ref (6).8.3.1 This practice presents a sample computer program“RNSMP” for the computation of the Ride Number equationfrom the recorded longitudinal profile measurement.8.3.1.1 The computer program RNSMP is a general com-puter program that accepts the elevation profile data set asinput, and then calculates the Ride Number using the equationpresented in 8.2.8.3.1.2 A listing of the RNSMP computer program for thecomputation of the Ride Number transform equation is in-cluded in this practice as Appendix X1.8.3.1.3 A provision has been made in the computer programlisting (Appendix X1) for the computation of the Ride Numbertransform equation from recorded longitudinal profile measure-ments in both SI and inch-pound units.8.3.2 The input to the sample Ride Number computerprogram is an ASCII profile data set stored in a 1X, F8.3, 1X,F8.3 Fortran format. In this format, the profile data appears asa multi-row, two-column array with the left wheel path profiledata points in column 1 and the right wheel path points incolumn 2. The profile data point interval is discretionary.However, the quality of the Ride Number estimates cited in thispractice, are based on a data point interval of 150 mm (6 in.)(see Section 5).8.3.2.1 If the input to the Ride Number computer program isin SI units, the elevation profile data points are scaled inmillimetres with the least significant digit being equal to 0.001mm.8.3.2.2 If the input to the Ride Number computer program isin inch-pound units, the elevation profile data points are scaledin inches with the least significant digit being equal to 0.001inch.8.4 The distance interval over which the Ride Number iscomputed is discretionary, but shall be reported along with theRide Number results.8.5 Validation of the Ride Number program is requiredwhen it is installed. Provisions for the RN program installationvalidation have been provided in this practice.8.5.1 The sample profile data set TRIPULSE.ASC has beenprovided in SI units in Appendix X2 for validation of thecomputer program installation.8.5.2 Using the sample profile data set TRIPULSE.ASC(Appendix X2) as input to the Ride Number computer program(Appendix X1), a Ride Number of 3.66 was computed asshown in Appendix X3 for a profile data point interval of .15m (.5 feet) and a distance interval equal to 15 meters of theprofile data set.9. Report9.1 The report for this practice shall contain the followinginformation:9.1.1 Profile Measuring Device—The report data shall in-clude the ASTM Standard E950 classification of the deviceused to make the measurements, the date of the last successfuldevice calibration, and the highpass filter wavelength used inthe profile measurement.9.1.2 Longitudinal Profile Measurements—Report data fromthe profile measuring process shall include the date and time ofday of the measurement, the location of the measurement, thelane measured, the direction of the measurement, length ofmeasurement, and the descriptions of the beginning and endingpoints of the measurement.9.1.3 Profile Data Point Interval (Sample Interval) andProfile Reporting Interval—Ride Number report data shallinclude the profile data point interval and the profile reportinginterval.9.1.4 Ride Number Results—The Ride Number resultsshould be reported to two decimal places along with thedistance interval over which the RN was computed.10. Keywords10.1 longitudinal profile; mean panel ratings (MPR); panelrating; pavement ride quality; profile index; rideability; ridenumber (RN); subjective ride quality; subjective ride qualityestimateE1489 − 08 (2013)3APPENDIXES(Nonmandatory Information)X1. RIDE NUMBER COMPUTER PROGRAMX1.1 Included as Fig. X1.1 is a sample Fortran computerprogram using user-selected SI or inch-pound units that can beused to compute the Ride Number specified by this practice. Ifthe SI option is selected, the program assumes the input roadprofile amplitudes are stored in millimetre units, if inch-pound,inches. In the sample program, the profile data sample intervalis assumed to be either .15 m or .5 feet depending on the optionselected. For the sample program, the maximum road sectionlength that can be processed is 160.9 metres (528 feet).X1.2 The elevations in the sample data file shown inAppendix X2 are in SI units (mm) and contain one thousandprofile data points. If the inch-pound option is selected, the usermust convert the profile data set from SI units to inch-poundunits to get the inch-pound output shown in Appendix X3.FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index.E1489 − 08 (2013)4FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index (continued)E1489 − 08 (2013)5FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index (continued)E1489 − 08 (2013)6FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Numb