Error sensitivity of the connected vehicle approach to pavement performance evaluations

Abstract

The international roughness index (IRI) 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 characterise roughness can do so by accounting for all vibration wavelengths that the actual vehicles experience. This study characterises and compares the precision of each method. The field studies indicate that within seven traversals, the connected vehicle approach could achieve the same level of precision as the procedure used to produce the IRI. For a given vehicle and segment lengths longer than 50 m, 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 the best practices in utilising the connected vehicle method.

Keywords:

• Connected vehicle
• GPS
• inertial profiler
• measurement precision
• pavement management
• probe vehicles
• ride quality
• smartphone

Acknowledgements

The authors would also like to express their sincere appreciation to Mr. Thomas Burchett of Fugro Roadware for his contribution in data collection at MnROAD facilities.