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 state-of-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.

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.

The development of advanced technologies has led to the emergence of autonomous vehicles. Herein, autonomous public transport (APT) systems equipped with prioritization measures are being designed to operate at ever faster speeds compared to conventional buses. Innovative APT systems are configured to accommodate prevailing passenger demand for peak as well as non-peak periods, by electronic coupling and decoupling of platooned units along travel corridors, such as the dynamic autonomous road transit (DART) system being researched in Singapore. However, there is always the trade-off between high vehicle speed versus passenger ride comfort, especially lateral ride comfort. This study analyses a new APT system within the urban context and evaluates its performance using microscopic traffic simulation. The platooning protocol of autonomous vehicles was first developed for simulating the coupling/decoupling process. Platooning performance was then simulated on VISSIM platform for various scenarios to compare the performance of DART platooning under several ride comfort levels: three bus comfort and two railway criteria. The study revealed that it is feasible to operate the DART system following the bus standing comfort criterion (a_y = 1.5 m/s²) without any significant impact on system travel time. For the DART system operating to maintain a ride comfort of the high-speed train (HST) and light rail transit (LRT), the delay can constitute up to ≈ 10% and ≈ 5% of travel time, respectively. This investigation is crucial for the system delay management towards precisely designed service frequency and improved passenger ride comfort.

Mathematical models have been used widely to investigate the vehicle-passenger-infrastructure dynamical interaction; however, the responses of various heavy-duty city bus models to estimate ride comfort induced by road roughness are still unknown. In this study, the comparison of dynamical response of buses used in city transport is investigated based on multi-degrees-of-freedom (DOF) bus models developed in MATLAB/Simulink and correlated against passenger ride comfort criteria. The results showed that 9-DOF full bus model is the best option to estimate passenger ride comfort within an error of 2%, as compared to 5-DOF half and 3-DOF quarter bus models with 7% and 20% errors using one wheel-track, and 24% and 36% errors using two wheel-tracks, respectively. The error was calculated as the difference between simulated results from three bus models and the measured data. These mathematical bus models can be customized for estimating passenger ride comfort and surface roughness of dedicated bus/bus-rapid-transit lanes.