Abstract
Rough pavements have a substantial influence on public approval, safety, and the economy. It is imperative to understand road surface conditions, particularly in decision-making processes for road maintenance, asset management, maintenance planning, and programming. In interpreting road pavement conditions, there are various methods of determining metrics and parameters. Thus, ride quality is one of the essential requirements for travelers to check road pavements. Addressing the identified research gaps is needed: 1) sensitivity of IRI values, 2) inaccuracy between no. of vehicles’ wheels, 3) characteristics of Android-based smartphones used, and 4) age of pavement. Results show the need for developing an index of roughness, representing urban roads’ unique features and predicting users’ road conditions. It is an impressive win for a model that would cover all the identified gaps—further recommends that future researchers consider the cause of when, how, and why a pavement deteriorates, given that there are different pavement situations. Thus, it complements the analysis results significantly and provides a better picture of the paving conditions.
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