Characterizing Ride Quality With a Composite Roughness Index

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.

Relating subjective ride quality ratings to objective measures

“…Recently, Bridgelall (2022) introduced a composite roughness index (CRI) to characterize roughness from multidimensional movements along any path, including linear and rotational motions (Bridgelall, 2022). This work accessed and used the data from the Bridgelall (2022) experiments.…”

“…The measurements used the CRI introduced by Bridgelall (2022) because it is the only index that accounts for roughness produced by the three linear and three angular dimensions of motion (Bridgelall, 2022). Accelerations along the linear dimensions produce roughness in the lateral, longitudinal, and vertical directions whereas accelerations in the angular dimensions can produce discomfort such as head tossing, swaying, or other rotational motions.…”

“…Per the Bridgelall reference, the interpretation of RIF-index 𝑅 𝑔 𝐿 is the average g-force experienced in one of the six roughness dimensions specified after traveling a distance L along the traversal path. As described by Bridgelall (2022), the composite measure is the resultant roughness 𝑅 𝑇 𝐿 experienced as…”

“…The labels for the routes were EM (from Essentia Hospital to the Mall), SG (from Sanford Hospital to the Ground Transportation Center), UG (from University Drive to the Ground Transportation Center), MW (from the Mall to Walmart), and WM (from Walmart to the Mall). Bridgelall (2022) provides further details of the data collection, setup, and route map in his paper describing the nature of the CRI (Bridgelall, 2022), so this paper does not repeat those.…”

Perspectives on Using Connected Vehicles for Transportation Infrastructure Condition Monitoring

“…Using connected vehicles to monitor ride quality and upload images can provide a more consistent, long-term indication of the infrastructure deterioration rate (Bridgelall 2022). Cloudbased computing systems can analyze signals from a fusion of mobile sensors to generate an accurate assessment of infrastructure condition and performance.…”