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1 Measuring, Characterizing, and Reporting Pavement Roughness of Low-Speed and Urban Roads State highway agencies monitor pavement roughness to assess road network condition and construction quality assurance. States are also required to report the International Roughness Index (IRI) on the National Highway System under the National High- way Performance Program. The IRI is computed from longitudinal elevation profiles using a quarter-car simulation with standard coefficients, including a standard simu- lated 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 features that appear in the longitudinal profile, such as drainage provisions, sudden grade changes, and crowned intersecting streets. The IRI calculation algorithm is based on a standard simulation speed, and the rela- tionship 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 invalidate the measured elevation profile. The objective of this research was to identify or, if necessary, to develop a means for measuring, characterizing, and reporting pavement roughness on urban and low-speed roadways. Measurement An experiment was conducted to demonstrate the effects of potentially adverse opera- tional conditions on the measurement of longitudinal road profiles by six high-speed inertial profilers: operation at low speeds, acceleration, deceleration, stop-and-go operation, profiling from a dead stop, and operation on a curve. The results of the experiment confirmed the sensitivity of data collected by inertial profilers to misalignment of the accelerometers during host vehicle longitudinal acceleration, longitudinal deceleration, lateral acceleration, and stops. In particular, braking and collection of profile through a stop introduced artificial content into the profiles that affected the IRI and appeared as localized roughness. The severity of localized roughness caused by braking increased with the severity of decel- eration; and the severity of localized roughness caused by stop-and-go operation increased as the length of time at the stop increased. For both braking and stop-and-go operation, the error in profile measurement and the severity of artificial roughness in the profile was greater for profilers mounted to vehicles with a higher propensity for pitch and roll, such as vehicles with a high center of gravity or soft suspensions. The extent of the invalid area within the profile where braking or a stop occurred was not consistent among the profilers tested, and application of a high-pass filter to the measured profile spread out the influence of errors in the accelerometer signal differently depending on the filter type. S U M M A R Y
2 The validity of measured profiles decreased at very low speeds. However, the six profilers tested did not dem onstrate a common low-speed limit. A set of experimental procedures is proposed for certification of inertial profilers to be used in urban and low-speed network profiling applications. The testing is recommended to identify the lowest valid operating speed of a profiler and discern the range of distance within a measured profile that should be marked as invalid in the vicinity of braking or stop-and-go operation for computation of IRI. Characterization An experiment was conducted to correlate measured road roughness to objective mea- surements of vibration experienced by a vehicle driver. Three instrumented vehicles were tested on 29 urban and low-speed test sections. The testing included multiple passes over each test section at each of two speeds, which were typically in the range from 25 mi/hr (40 km/hr) to 45 mi/hr (72 km/hr). The instrumentation provided simultaneous measurements of road profile and accelera- tion at several interfaces between the vehicle and the driver. The analysis produced standard metrics for quantifying driver discomfort from measured accelerations at driver/vehicle interfaces. The experimental results produced two observations with practical implications. First, the vibration experienced by the driver on the majority of the test sections included content that classified as âtransient.â The presence of transient content implies that the user perception of comfort on a road section depends on the severity of localized roughness in addition to the average roughness level. Peak localized roughness using a short-interval roughness profile correlated favorably with a standard measure of transient vibration. It is recommended that, in addition to average roughness, surveys of roughness in urban and low-speed roadways record the location and magnitude of severe peaks in the short-interval roughness profile using an averaging base length of 25 ft (7.6 m). Second, for travel speeds down to 25 mi/hr (40 km/hr), adjustment of the IRI algorithm toward lower simulated travel speed improved correlation to measured discomfort experi- enced by the driver. As actual vehicle travel speed decreases, a portion of the content within road profiles that affects the IRI (the long-wavelength part of the range) corresponds to frequencies with a diminishing influence on vehicle response. Likewise, a portion of content with a lesser effect on the IRI (i.e., near or beyond the short-wavelength limit) corresponds to a range of frequencies with an increasing effect on vehicle response. Adjustment of the IRI algorithm to lower simulated travel speeds improved correlation to measured discomfort in the three test vehicles compared to the IRI, because it better aligned the frequency response of the index to the frequency response of the vehicles. Further adjust- ment of the IRI algorithm to predict a temporal response (i.e., inches/second) instead of a spatial response (i.e., inches/mile) at the test vehicle travel speed further improved correla- tion, because it predicted intensity of vehicle response more closely. Use of an IRI-based temporal index, which is called âGolden Car Average Rectified Velocity (GCARV),â is rec- ommended as a specific scale for estimating the functional status of urban and low-speed roads. This option maintains the relevance of the underlying IRI algorithm to a broad range of vehicle types and to a broad range of vehicle responses. However, use of GCARV produces roughness on an unfamiliar scale, and field experience and further research are needed to establish thresholds for various applications. Reporting A survey of road profile features from urban and low-speed roadways showed that local- ized roughness of built-in roadway features accounted for a significant portion of the overall roughness. Some built-in features cause roughness because they impose constraints on the
3 design profile that are in conflict with roadway smoothness (e.g., crowned intersections). Others cause roughness when they are not built to within tolerance or are poorly maintained (e.g., utility covers). In both cases, the roughness measured at these features affects ride qual- ity and should be included when reporting functional performance. Many of the built-in features studied in this research caused roughness concentrated within a small area (i.e., localized roughness) much greater than the segment-wide aver- age. In such cases, localized roughness may not increase the segment-wide average in pro- portion to the degradation in ride quality or vehicle durability caused by the underlying feature. As such, it is recommended that network-level surveys capture a measure of local- ized roughness in addition to segment-wide averages. Although roughness at built-in features affects the functional performance of a roadway segment, its implications to pavement structural health are much different than roughness associated with construction defects or those caused by surface distress. Automated identifi- cation of built-in roadway features is recommended as a resource for interpreting roughness measurement within urban roadway asset management.
