Wavelength Sensitivity of a Connected Vehicle Method of Ride Quality Characterizations

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).Researchers previously demonstrated that a roughness index called the road impact factor (RIF) is directly proportional to the international roughness index (IRI) when measured under identical conditions. A RIF-transform converts inertial signals from connected vehicle accelerometers and speed sensors to produce RIF-indices in realtime. This research examines the relative sensitivities of the RIF and the IRI to variations in dominant profile wavelengths. The findings are that both indices characterize roughness from spatial wavelengths up to 2 meters with equal sensitivity. However, the RIF transform maintains its sensitivity when characterizing roughness from wavelengths beyond that. The case studies used a certified inertial profiler to collect both RIF and IRI data simultaneously from five different pavement surface types. The RIF/IRI proportionality factors distributed normally among the profiles tested. This result affirms that the RIF and IRI generally agrees. However, differences in the dominant profile wavelength among pavements will produce some spread in the degree of roughness that the indices express.Mountain Plains Consortium (MPC)https://www.ugpti.org/about/staff/viewbio.php?id=7

Error Sensitivity of the Connected Vehicle Approach to Pavement Performance Evaluations

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The international roughness index is the prevalent indicator used to assess and forecast road maintenance needs. The fixed parameters of its simulation model provide the advantage of requiring relatively few traversals to produce a consistent index. However, the static parameters also cause the model to under-represent roughness that riders experience from profile wavelengths outside of the model’s response range. A connected vehicle method that uses a similar but different index to characterize roughness can do so by accounting for all vibration wavelengths that the actual vehicles experience. This study characterizes and compares the precision of each method. The field studies indicate that within 7 traversals, the connected vehicle approach could achieve the same level of precision as the procedure used to produce the international roughness index. For a given vehicle and segment lengths longer than 50 meters, the margin-of-error diminished below 1.5% after 50 traversals, and continued to improve further as the traversal volume grew. Practitioners developing new tools to evaluate pavement performance will benefit from this study by understanding the precision trade-off to recommend best practices in utilizing the connected vehicle method.University Transportation CentreU.S. Department of Transportation (USDOT)Mountain Plains Consortium (MPC)Grant DTRT12-G-UTC08https://www.ugpti.org/about/staff/viewbio.php?id=7

Use of Connected Vehicles to Characterize Ride Quality

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The United States rely on the performance of more than four million miles of roadways to sustain its economic growth and to support the dynamic mobility needs of its growing population. The funding gap to build and maintain roadways is ever widening. Hence, the continuous deterioration of roads from weathering and usage poses significant challenges. Transportation agencies measure ride quality as the primary indicator of roadway performance. The international roughness index is the prevalent measure of ride quality that agencies use to assess and forecast maintenance needs. Most jurisdictions utilize a laser-based inertial profiler to produce the index. However, technical, practical, and budget constraints preclude their use for some facilities, particularly local and unpaved roads that make up more than 90% of the road network in the US. This study expands on previous work that developed a method to transform sensor data from many connected vehicles to characterize ride quality continuously, for all facility types, and at any speed. The case studies used a certified and calibrated inertial profiler to produce the international roughness index. A smartphone aboard the inertial profiler produced simultaneously the roughness index of the connected vehicle method. The results validate the direct proportionality relationship between the inertial profiler and connected vehicle methods within a margin-of-error that diminished below 5% and 2% after 30 and 80 traversal samples, respectively.Mountain Plains Consortium (MPC)https://www.ugpti.org/about/staff/viewbio.php?id=7