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Li, H. International Roughness Index. Encyclopedia. Available online: (accessed on 07 December 2023).
Li H. International Roughness Index. Encyclopedia. Available at: Accessed December 07, 2023.
Li, Handwiki. "International Roughness Index" Encyclopedia, (accessed December 07, 2023).
Li, H.(2022, October 31). International Roughness Index. In Encyclopedia.
Li, Handwiki. "International Roughness Index." Encyclopedia. Web. 31 October, 2022.
International Roughness Index

The International Roughness Index (IRI) is the roughness index most commonly obtained from measured longitudinal road profiles. It is calculated using a quarter-car vehicle math model, whose response is accumulated to yield a roughness index with units of slope (in/mi, m/km, etc.). This performance measure has less stochasticity and subjectivity in comparison to other pavement performance indicators, but it is not completely devoid of randomness. The sources of variability in IRI data include the difference among the readings of different runs of the test vehicle and the difference between the readings of the right and left wheel paths. Despite these facts, since its introduction in 1986, the IRI has become the road roughness index most commonly used worldwide for evaluating and managing road systems. The measurement of IRI is required for data provided to the United States Federal Highway Administration, and is covered in several standards from ASTM International: ASTM E1926 - 08, ASTM E1364 - 95(2005), and others. IRI is also used to evaluate new pavement construction, to determine penalties or bonus payments based on smoothness.

road roughness index roughness index model

1. History

In the early 1980s the highway engineering community identified road roughness as the primary indicator of the utility of a highway network to road users. However, existing methods used to characterize roughness were not reproducible by different agencies using different measuring equipment and methods. Even within a given agency, the methods were not necessarily repeatable. Nor were they stable with time.

The United States National Cooperative Highway Research Program (NCHRP) initiated a research project to help state agencies improve their use of roughness measuring equipment.[1] The work was continued by The World Bank[2] to determine how to compare or convert data obtained from different countries (mostly developing countries) involved in World Bank projects. Findings from the World Bank testing showed that most equipment in use could produce useful roughness measures on a single scale if methods were standardized. The roughness scale that was defined and tested was eventually named the International Roughness Index.The World Bank.[3] The IRI is used for managing pavements, sometimes used to evaluate new construction to determine bonus/penalty payments for contractors, and to identify specific locations where repairs or improvements (e.g., grinding) are recommended. The IRI is also a key determinant of vehicle operating costs which are used to determine the economic viability of road improvement projects.[4]

2. Definition

The IRI was defined as a mathematical property of a two-dimensional road profile (a longitudinal slice of the road showing elevation as it varies with longitudinal distance along a travelled track on the road). As such, it can be calculated from profiles obtained with any valid measurement method, ranging from static rod and level surveying equipment to high-speed inertial profiling systems.

The quarter-car math model replicates roughness measurements that were in use by highway agencies in the 1970s and 1980s. The IRI is statistically equivalent to the methods that were in use, in the sense that correlation of IRI with a typical instrumented vehicle (called a "response type road roughness measuring system", RTRRMS) was as good as the correlation between the measures from any two RTRRMS's. As a profile-based statistic, the IRI had the advantage of being repeatable, reproducible, and stable with time. The IRI is based on the concept of a 'golden car' whose suspension properties are known. The IRI is calculated by simulating the response of this 'golden car' to the road profile. In the simulation, the simulated vehicle speed is 80 km/h (49.7 mi/h). The properties of the 'golden car' were selected in earlier research[1] to provide high correlation with the ride response of a wide range of automobiles that might be instrumented to measure a slope statistic (m/km). The damping in the IRI is higher than most vehicles, to prevent the math model from "tuning in" to specific wavelengths and producing a sensitivity not shared by the vehicle population at large.

The slope statistic of the IRI was chosen for backward compatibility with roughness measures in use. It is the average absolute (rectified) relative velocity of the suspension, divided by vehicle speed to convert from rate (e.g. m/s) to slope (m/km). The frequency content of the suspension movement rate is similar to the frequency content of chassis vertical acceleration and also tire/road vertical loading. Thus, IRI is highly correlated to the overall ride vibration level and to the overall pavement loading vibration level. Although it is not optimized to match any particular vehicle with full fidelity, it is so strongly correlated with ride quality and road loading that most research projects that have tested alternate statistics have not found significant improvements in correlation.

3. Measurement

The IRI is calculated from the road profile. This profile can be measured in several different ways. The most common measurements are with Class 1 instruments, capable of directly measuring the road profile, and Class 3 instruments, which use correlation equations. Using World Bank terminology, these correspond to Information Quality Level (IQL) 1 and IQL-3 devices, representing the relative accuracy of the measurements.[5] A common misconception is that the 80 km/h used in the simulation must also be used when physically measuring roughness with an instrumented vehicle. IQL-1 systems measure the profile direction, independent of speed, and IQL-3 systems typically have correlation equations for different speeds to relate the actual measurements to IRI.

IQL-1 systems typically report the roughness at 10–20 m intervals; IQL-3 at 100m+ intervals.

Early measurements were done with a rod-and-level survey technique. The Transportation Research Laboratory developed a beam which had a vertical displacement transducer. From the late 1990s the use of the Dipstick Profiler,[6] with a reported accuracy of .01 mm ( 0.0004 inches), became quite common.[7] The ROMDAS Z-250 operates in a similar manner to the Dipstick. The ARRB TR walking profiler was a major innovation as it allowed for accurate profiles to be measured at walking speed.

Dynamic measurements of the road profile are done with vehicle mounted instruments. The approach consisted of a sensor (initially ultrasonic but later laser) which measures the height of the vehicle relative to the road. An accelerometer is double integrated to give the height of the sensor relative to datum. The difference between the two is the elevation profile of the road. This elevation profile is then processed to obtain the IRI. The most common approaches see the IRI measured in each wheelpath. The wheelpath IRIs need to be combined to obtain the overall IRI "roughness profile".[8] for the lane. There are two ways this can be done. A 'half-car' model simulates the vehicle travelling along both wheelpaths, while a 'quarter car' model simulates one wheel on each wheelpath and the average is the lane IRI. The quarter-car approach is considered more accurate in representing the motion felt by users and so is most common.

A major issue with the profilers has to do with their contact areas compared to the footprint of a tyre. The latter is much larger than any of the static/slow speed Class 1 priofilers or a typical laser profilometer. This has been addressed more recently through the use of scanning lasers which create a 3D model of the pavement surface. An example of this is the Pavemetrics system which has been adopted by many different OEM suppliers of profilometer equipment around the world. In addition to measuring roughness this system also measures other key pavement attributes such as cracking, rut depth and texture.

Less expensive alternatives to profilometers are RTRRMS which do not record the profile but rather are installed in vehicles and measure how the vehicle responds to the pavement profile. These need to be calibrated against IRI to obtain an estimate of the IRI. Since RTRRMS are generally affected by pavement texture and speed, it is common to have different calibration equations to correct the readings for these effects.

RTRRMS can be grouped into three broad categories and are generally IQL-3 except arguably most cell phone based systems which are IQL-4:

  • Bump Integrators: These have a physical connection between the sprung and unsprung mass and record the relative motion. Originally trailer mounted, such as the one developed in India by CRRI, CRRI Trailer Bump Integrator, they are now most commonly installed on the floor of a vehicle with a cable connecting to the suspension such as those produced by the TRL (U.K.), CSIR (South Africa) or ROMDAS (N.Z.) ROMDAS Bump Integrator.
  • Accelerometer Based Systems: These use an accelerometer to measure the relative motion of the sprung mass, corrected (sometimes) for the unsprung mass. Examples of these are the early ARAN systems (Canada) and the ARRB Roughometer (Australia).
  • Cell Phone Based Systems: These are a subset of accelerometer systems insofar as the accelerometer is embedded in the cell phone. Examples of these apps are TotalPave, RoadBounce Roadroid, RoadLab Pro, RoadBump and [1]. While these are becoming ubiquitous, the apps have major differences when it comes to setup and calibration features. They need to be used with great caution and are more appropriately considered IQL-4 than IQL-3


  1. Gillespie, T.D., Sayers, M.W., and Segel, L., “Calibration of Response-Type Road Roughness Measuring Systems.” NCHRP Report. No. 228, December 1980
  2. Sayers, M.W., Gillespie, T. D., and Paterson, W.D. Guidelines for the Conduct and Calibration of Road Roughness Measurements, World Bank Technical Paper No. 46, The World Bank, Washington DC, 1986.
  3. Sayers, M. W. (Michael W.) (1986). International road roughness experiment : establishing methods for correlation and a calibration standard for measurements. World Bank Technical Paper No. 45. Washington, D.C.: World Bank. ISBN 0-8213-0589-1. OCLC 1006487409.
  4. Modelling Road User and Environmental Costs in HDM-4
  5. Data Collection Technologies for Road Management
  6. Face® Dipstick® website home page
  7. Comparison of Roughness Calibration Equipment - with a View to Increased Confidence in Network Level Data; G. Morrow, A. Francis, S.B. Costello, R.C.M. Dunn, 2006
  8. Sayers, M.W., Profiles of Roughness. Transportation Research Record 1260, Transportation Research Board, National Research Council, Washington, D.C. 1990
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