Connected Vehicle Approach for Pavement Roughness Evaluation
Publication: Journal of Infrastructure Systems
Volume 20, Issue 1
Abstract
Connected vehicles present an opportunity to monitor pavement condition continuously by analyzing data from vehicle-integrated position sensors and accelerometers. The current practice of characterizing and reporting ride quality is to compute the international roughness index (IRI) from elevation profile or bumpiness measurements. However, the IRI is defined only for a reference speed of . Furthermore, the relatively high cost for calibrated instruments and specialized expertise needed to produce the IRI limit its potential for widespread use in a connected vehicle environment. This research introduces the road impact factor (RIF), which is derived from vehicle integrated accelerometer data. The analysis demonstrates that RIF and IRI are directly proportional. Simultaneous data collection with a laser-based inertial profiler validates this relationship. A linear combination of the RIF from different speed bands produces a time-wavelength-intensity-transform (TWIT) that, unlike the IRI, is wavelength-unbiased. Consequently, the TWIT enables low-cost, network-wide, and repeatable performance measures at any speed. It can extend models that currently use IRI data by calibrating them with a constant of proportionality.
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Acknowledgments
This work is based on research supported by the North Dakota Department of Transportation (NDDOT) and the United States Department of Transportation (USDOT), Research and Innovative Technology Administration (RITA) under the Rural Transportation Research Initiative.
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© 2013 American Society of Civil Engineers.
History
Received: Dec 14, 2012
Accepted: Apr 22, 2013
Published online: Apr 23, 2013
Published in print: Mar 1, 2014
Discussion open until: May 5, 2014
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