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Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads (2019)

Chapter: Chapter 1 - Introduction and Research Approach

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Suggested Citation:"Chapter 1 - Introduction and Research Approach." National Academies of Sciences, Engineering, and Medicine. 2019. Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads. Washington, DC: The National Academies Press. doi: 10.17226/25563.
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Suggested Citation:"Chapter 1 - Introduction and Research Approach." National Academies of Sciences, Engineering, and Medicine. 2019. Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads. Washington, DC: The National Academies Press. doi: 10.17226/25563.
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Suggested Citation:"Chapter 1 - Introduction and Research Approach." National Academies of Sciences, Engineering, and Medicine. 2019. Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads. Washington, DC: The National Academies Press. doi: 10.17226/25563.
×
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Suggested Citation:"Chapter 1 - Introduction and Research Approach." National Academies of Sciences, Engineering, and Medicine. 2019. Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads. Washington, DC: The National Academies Press. doi: 10.17226/25563.
×
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Suggested Citation:"Chapter 1 - Introduction and Research Approach." National Academies of Sciences, Engineering, and Medicine. 2019. Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads. Washington, DC: The National Academies Press. doi: 10.17226/25563.
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4 1.1 Problem Statement State highway agencies monitor pavement roughness to assess road network condition and for other purposes, such as assessing construction quality and optimizing investments in preservation, rehabilitation, and reconstruction. States are also required to report the International Roughness Index (IRI) on an expanded National Highway System (NHS) net- work under the National Highway Performance Program (NHPP) (23 CFR 490 Subpart C). The IRI is not measured directly. Instead, it is computed from longitudinal elevation profiles using a quarter-car simulation. The IRI calculation procedure uses standard coefficients, including a standard simulated travel speed of 49.7 mi/hr (80 km/hr). This research was conducted in response to concerns about using current practices for monitoring the roughness of urban and low-speed roadways. Urban roadways contain unique fea- tures, such as drainage provisions, sudden grade changes, and crowned intersecting streets. These features are included in the longitudinal elevation profile and interpreted as rough- ness. Also, because the IRI calculation algorithm is based on a standard simulation speed, the relationship between the IRI and the response of the prevailing traffic fleet to rough features in the elevation profile will change at slower traffic speeds. In addition, changes in travel speed and stops or near stops by vehicles that carry instrumentation for measuring longitudinal elevation profile can distort, or even invalidate, the measured elevation profile. Because of the unique features of urban and low-speed roadways, use of the current practices for estimating pave- ment roughness may yield inappropriate and misleading data. Research was needed to identify, or, if necessary, to develop, means for appropriately measuring, characterizing, and reporting pavement roughness of these roads. This report presents findings from National Cooperative Highway Research Program (NCHRP) Project 10-93, which investi- gated these issues. The findings will help highway agencies obtain reliable information for use in monitoring pavement performance, evaluating construction quality, planning and making investment decisions, and interpreting data within the NHPP. 1.2 Research Objective The objective of this research was to identify, or, if necessary, to develop a means for measuring, characterizing, and report- ing pavement roughness on urban and low-speed roadways. 1.3 Scope Valid measurement of a longitudinal profile as a prerequi- site for valid and relevant measurement of roughness is at the core of the research approach. Collecting and storing valid profile measurements on urban and low-speed roadways provides highway agencies with flexibility to use the data for multiple purposes and maximizes the public’s return on the cost of obtaining the measurements in three important ways. First, if there are measurements of profile available that accurately measure features affecting major vehicle responses of interest (e.g., passenger vibrations, dynamic tire loads, etc.), they may be used for multiple applications. For example, an agency may use a roughness index for estimating the public’s satisfaction with ride quality over the entire network, but apply a specialized index in addition to it over a subset of the network where conditions (such as the prevailing traffic speed) are different or where other performance measures (like reducing dynamic loading) have higher priority. In addition, measurement and storage of valid profiles offer an agency the flexibility to adopt a new roughness index or new analysis methods as the state of knowledge improves without sacrificing the value of their historical database. C H A P T E R 1 Introduction and Research Approach

5 Second, with the application of proper measurement requirements, profiles contain the information needed to diagnose the sources of roughness that degrade ride quality and other serviceability factors. In addition to the calculation of a summary roughness index, an important application of profiles is to identify localized roughness and quantify its severity. The research approach included an experiment that examined the relative role of localized roughness and dis- tributed roughness in determining the ride quality of urban and low-speed roadways. Profiles also contain the informa- tion needed and distinguish between rough features associ- ated with distress, poor workmanship, or built-in roughness that exists because of other engineering requirements (e.g., crowned intersections, utility covers, etc.). Third, measurement and storage of profile data provide an additional opportunity for data quality control. If a pave- ment section exhibits an unexpected change in roughness, inspection of the profile and comparison to the profile from the previous visit often helps determine whether the change is genuine or whether a problem may exist with the accuracy of the measurement. The objective of this research was to identify or develop a means for measuring, characterizing, and reporting pave- ment roughness on urban and low-speed roadways. The state of practice for measuring, characterizing, and report- ing road roughness has matured tremendously over the past three decades. However, most of the work has emphasized the monitoring of high-speed limited-access facilities. Apply- ing road-profiling technology to urban and low-speed road networks poses several technical challenges, which are sum- marized here. 1.3.1 Profiler Operating Speed Profilers that operate on urban and low-speed roadways are sometimes required to operate at speeds lower than the valid operating speed of an inertial profiler, which is typically about 15 mi/hr (24 km/hr). The profiler also must accelerate and decelerate often to operate with traffic conditions on the roadway, and it often needs to stop due to traffic signals, con- gestion, or for safety reasons. Operation of inertial profil- ers under these conditions often introduces errors beyond what can be tolerated for the engineering purposes of the measurement. 1.3.2 Profiler Operating Environment The type of roughness that appears on urban and low- speed roadways, in terms of construction imperfections and constraints, sources of localized roughness, and prevailing surface distress, is different than on high-speed, limited- access roads. This affects profile measurement because a profiler that operates well on high-speed roadways may expe- rience problems with vertical motion (exceeding the range of height sensors) and excessive wheel vibration (causing errors in longitudinal distance measurement) on very rough urban roads. This also affects profile characterization because a dif- ferent mix of ride vibrations may be experienced on urban and low-speed roadways, including higher potential for excit- ing roll and pitch motion. 1.3.3 Roughness Index The IRI is the dominant index for road roughness used in the United States, and the Federal Highway Administration (FHWA) requires state highway agencies to submit rough- ness of Highway Performance Monitoring System (HPMS) sections in terms of IRI. The 49.7 mi/hr (80 km/hr) simula- tion speed used in the IRI may not be the optimal choice on lower-speed roadways or urban non-freeways where prevail- ing speeds are significantly lower. This is because travel speed determines what aspects of the road surface are important. Lowering travel speed also alters the way roughness is expe- rienced by the public because the same amount of rough- ness spread out over a given length of roadway registers less intensely over a longer period of time. The standard quarter-car model used for calculating the IRI (the “Golden Car”) was validated as a general pavement- condition indicator at speeds as low as 15 mi/hr (24 km/hr) on rural roads with roughness beyond 500 in/mi (7.9 m/km) when it was developed. However, it was not developed with urban roadways in mind, where the relative importance of ride quality, dynamic tire loads, and user cost may be differ- ent and localized roughness may affect user opinion of the roadway differently due to a difference in expectations. The vehicle properties and the simulated travel speed in the IRI algorithm determine which features register as road rough- ness and their relative importance. This is often expressed in terms of waveband and summarized using an upper and lower wavelength of interest. The wavelength range of interest for a given vehicle shifts in proportion to travel speed. As travel speeds decrease, aspects of the road profile that correspond to long wavelengths, such as swells and gradual grade changes, diminish in importance. Aspects of the road profile that cor- respond to short wavelengths grow in importance, including details of the profile at rough features that appear over short lengths. However, as speed decreases, the enveloping action of vehicle tires blunts more and more of the short-wavelength features that affect suspension response. Interactions of pro- file features with vehicle response that depend on the time delay between passing of the front and rear axle also change

6 as vehicle speed decreases. A simple change in the simula- tion speed used in the IRI calculation to correspond to travel speeds may fail to capture these effects. Often, the posted speed for a given roadway segment is not consistent (e.g., lower speed limit in a downtown area), and the prevailing traffic flow may include variable speed and stop-and-go conditions. Further, any roughness other than that caused by severe distress may be of little concern in areas where prevailing traffic is in a stop-and-go condition over most of the day because of congestion. 1.3.4 Built-In Roughness Many of the rough features experienced on urban and low- speed roadways are due to construction constraints because of existing road geometry (e.g., matching curb line and other features during rehabilitation) or because of features present on roadways (e.g., utility covers, utility cuts). Other engineering requirements, such as drainage, sometimes take higher priority than avoiding roughness in urban settings. In addition, the steps taken to avoid roughness on high-speed limited-access roadways are often not practical on urban roadways. Interpretation of roughness detected on urban and low-speed roadways must account for this. Management of urban pavements is often hampered when the locations of built-in road features are not known. Local- ized roughness appears at many built-in features with mag- nitude that is comparable to the localized roughness caused by surface distress. In some cases, the localized roughness raises the IRI of the segment that contains the built-in fea- ture enough to change its roughness classification (e.g., from good to fair, fair to poor, etc.). Prioritization of maintenance resources cannot be optimized unless the source of roughness is known. 1.4 Research Approach This research approach included four threads, which were designed to address engineering challenges described previously. 1.4.1 Study Urban and Low-Speed Roadway Features This aspect of the research examined characteristics of urban and low-speed roadways that register as roughness in measured longitudinal profiles. Four activities supported this effort: (1) a review of the literature, (2) inspection of photo logs from a network pavement survey of Philadelphia County, Pennsylvania, (3) analysis of photo logs, profiles, and profiler speed records from 60 pavement segments on 26 routes in Philadelphia County, Pennsylvania, and (4) analysis of photo logs, profiles, and profiler speed records from ten routes in New Jersey. The research produced several examples of urban and low-speed roadway features, with an emphasis on rough- ness at built-in structures. These examples helped quantify the potential roughness caused by common built-in features and determine the relative role of localized and distributed roughness in determining the overall IRI and the type of roughness in a given road segment. The research also dem- onstrated some challenges to the measurement process that are more common on urban and low-speed roadways. Exam- ples include the “hit or miss” quality of many rough features with severity that varies transversely and the interaction of special road features with profiler height sensors (e.g., deep narrow dips, extreme localized roughness, reflective surface materials, etc.). Review of the photo logs also revealed several examples of the challenges that profiler operators experience in urban areas, such as traffic stops, traffic congestion, inconsistent lane access, and other obstructions that prevent them from traveling at a consistent speed. 1.4.2 Validate Technology for Urban and Low-Speed Profile Measurement The research experimentally evaluated the accuracy and repeatability of existing profiling technology under opera- tional conditions that are not common on high-speed, limited-access roadways, but occur often in urban and low- speed environments. The test program was designed to do the following: • Determine the lowest speed at which each profiler could produce valid measurements; • Demonstrate the effects on profile and quantify the effect on IRI of braking, aggressive application of the accelerator, and stop-and-go operation; • Examine the potential errors in profile and roughness caused by lateral acceleration while traversing a curve; • Observe the differences in behavior between profilers with different host vehicles, sensor mounting locations, and data processing procedures (i.e., high-pass filters); and • Establish recommended guidelines for marking profile data near a stop or heavy application of the brakes as invalid. To support these goals, the test program included several passes by each of the participating profilers over a tangent section and a curved section on the low-volume loop at the MnROAD research facility near Albertville, Minnesota. Each profiler measured both test sections at a broad range of speeds and the tangent section with staged reproductions of

7 speed variations that occur during profile measurements in urban environments. Passes with speed variations included several events with braking, application of the accelerator, and stops within the test section. The runs included several iterations of each type of event with variations in the severity of braking, variations in the initial and final speed, and (for events with stops) variations in the length of time spent at the stop. Review of the speed records from the network pavement survey of Philadelphia County, Pennsylvania, influenced the test conditions selected for the experiment. Six high-speed profilers built by six different commercial manufacturers participated in the experiment. Collectively, these manufacturers supplied most of the network-level pro- filers in service in the United States. Some of the profilers included special provisions for reducing or avoiding artifi- cial roughness measured during a stop. However, all six were inertial profilers. As such, they all suffered from the same underlying cause of measurement error at very low travel speed, during braking, on curves, and at a stop. Although one commercially available non-inertial profiler type from over- seas was identified in the literature search, the only known user of a profiler of this design was not able to attend the experiment (Still and Jordan 1980). 1.4.3 Recommend a Method for Quantifying Roughness on Urban and Low-Speed Roadways The research examined the correlation between the IRI and objective measurements of ride quality on urban and low-speed road sections. The study also examined variations on the IRI calculation algorithm, such as changes to the stan- dard simulation speed. The evaluation emphasized measured ride quality over a range of travel speeds, on roads with and without localized roughness, on low-speed roads of various jurisdictions (e.g., urban and rural), and on roads of vari- ous roughness types (e.g., heavy roll content, heavy long- wavelength content, etc.). Three instrumented vehicles were tested on 29 urban and low-speed test sections. The instrumentation provided simultaneous measurements of road profile, vehicle speed and position, vehicle chassis acceleration, and acceleration at three interfaces between the vehicle and the driver: (1) seat/ buttock, (2) seat/back, and (3) floor/foot. The testing included multiple passes over each test section at each of two speeds. The study quantified user discomfort from measured accelerations using standard methods for evaluating human response to vibration. Objective measures of discomfort were correlated to the IRI, Ride Number (RN), and indices pro- duced by several modifications to the IRI algorithm. Variations on the IRI algorithm included changes to the simulated travel speed, normalization of the index response by time instead of distance, and prediction of vehicle body acceleration in place of suspension response. The study also applied standard methods for identifying transient response from measured accelerations and compared them to standard methods for identifying localized roughness from measured profile. 1.4.4 Develop a Framework for Reporting Roughness in Urban Areas Study of urban and low-speed roadway features showed that reporting of roughness by road segment without addi- tional information was not sufficient to support pavement network management. In particular, localized roughness appeared frequently at built-in structures and often appeared where expected travel speeds were lowest. This research demonstrated spatial location of built-in road features that adversely affect ride quality and measured roughness. The demonstration proposes metadata require- ments that will be needed by urban pavement network managers to properly interpret the roughness of urban road segments and report their status. The demonstration also identified existing databases (and their elements) that will be needed to pinpoint built-in features and methods for coping with instances where built-in structures cannot be located using public databases. 1.5 Organization of the Report Chapter 1 provides an introduction to the project and its objectives, presents background relevant to the scope of the work, and concisely describes the research approach. Chapter 2 describes features of road profiles that are unique to urban and low-speed roadways. The chapter discusses sev- eral built-in features of urban and low-speed roadways that affect their roughness. Chapter 2 also describes aspects of profile measurement practice that may confound diagnostic interpretation of urban and low-speed road profiles. Chapter 3 presents findings from an experiment that exam- ined the effects of adverse operational conditions on the mea- surement of longitudinal road profile by high-speed inertial profilers. The chapter briefly describes the field experiment and data analysis methods and presents results that char- acterize the valid operating range of existing commercial profilers in terms of measurement speed, operation during braking and accelerating, and operation during stop-and-go. Chapter 4 presents the results of an experiment that cor- related road roughness derived from measured profiles with standard measurements of vibration response at driver-to- vehicle interfaces. Results are provided for correlation of overall segment roughness to overall driver vibration level and for localized roughness criteria to transient vibration. Chapter 5 summarizes the research that was performed and the primary technical findings, provides recommendations,

8 and offers suggestions for future research. Recommenda- tions are given to help improve the state of practice per- taining to measurement, characterization, and reporting of road roughness. The chapter describes the rationale behind recommended additions for American Association of State Highway and Transportation Officials (AASHTO) specifica- tions. Chapter 5 also briefly presents conclusions and offers suggestions for further research. Attachment 1 lists recommended changes to AASHTO specifications. Appendix A provides selected examples of profiles of sev- eral built-in features to augment the examples provided in Chapter 2. Appendix B describes the profile measurement experiment in detail. Appendix C provides details about the experimental measurement of road profile and vibration response discussed in Chapter 4.

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Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads Get This Book
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Pavement smoothness (or roughness) is used by state highway agencies for monitoring network condition and other purposes such as assessing construction quality and optimizing investments in preservation, rehabilitation, and reconstruction.

States are also required to report the International Roughness Index (IRI) as an element of the federal Highway Performance Monitoring System (HPMS). Because IRI is not measured directly but is calculated as the mechanical response of a generic quarter-car, traveling at 50 mph, to the elevation profile of the roadway, there are concerns about using current practices for estimating roughness of low-speed and urban roads

Because of the unique features of low-speed and urban roads, research was needed to identify or, if necessary, develop means for appropriately measuring, characterizing and reporting pavement roughness of these roads.

National Cooperative Highway Research Program (NCHRP) Research Report 914: Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads reviews the practices for roughness measurement and the unique features of urban and low-speed roadways, and it evaluates the use of existing inertial profilers for such measurements.

The report also proposes revisions to American Association of State Highway and Transportation Officials standard specifications and practices addressing inertial profiler certification and operations.

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