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Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

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The road surface quality can be assessed with ride comfort indices because of their strong correlation. Many studies on ride comfort have focused on cars and trucks, but their results are not applicable to buses, which are characterised by inherently different vehicle dynamics. In this study, a quarter-vehicle simulation concept was used to develop a Bus Ride Index (BRI) for evaluating the effect of road irregularities on bus ride comfort. A BRI model was developed to optimise ride comfort depending on seat configuration and air suspension and validated according to technical data. The results show a good regression relationship between BRI and the International Roughness Index (IRI). New IRI thresholds with regard to ride comfort and bus operating speeds were established to serve as a benchmark to develop better pavement maintenance strategies for bus lanes and to estimate road quality based on acceleration data.