Abstract
There are many important applications that require ride quality characterization. However, the only international standard that specifies a roughness index is not suitable for applications beyond assessing the ride quality of paved roads. Other potential applications include automated ride quality characterization of gravel roads, bike or wheelchair paths, railways, rivers, airways, hyperloops, and elevator channels. This work proposes a composite index that characterizes roughness from multidimensional movements along any path. Statistical tests demonstrate two important properties—that the index is consistent based on an ever-decreasing margin-of-error of the mean, and distinguishable among different paths. A low-cost sensor package of accelerometers, gyroscopes, and a speedometer produced the data for spatio-temporal transformation. The experiments conducted on buses revealed that both the consistency and distinguishability of the index improves with the number of measurements. The approach is best suited for applications that can use in-situ sensors or crowdsensing to automate ride quality characterization.
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