The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.

Noise associated with road infrastructure is a prominent problem in environmental acoustics, and its implications with respect to human health are well documented. Objective and repeatable methodologies are necessary for testing the efficacy of sustainable noise mitigation methods such as low noise emission pavement. The Controlled Pass-By (CPB) method is used to measure the sound generated by passing vehicles. Despite its popularity, the applicability of CPB is compromised in urban contexts, as its results depend on test site conditions, and slight changes in the experimental setup can compromise repeatability. Moreover, physical conditions, reduced space, and urban elements risk confine its use to only experimental road sites. In addition, vehicle speed represents a relevant factor that further contributes to the method’s inherent instability. The present paper aims to extend the applicable range of this method and to provide more reliable results by proposing an adjusted CPB method. Furthermore, CPB metrics such as LAmax do not consider the travelling speed of the vehicle under investigation. Our proposed method can yield an alternative metric that takes into account the duration of the noise event. A hypothetical urban case is investigated, and a signal processing pipeline is developed to properly characterize the resulting data. Speed cushions, manhole covers, and other spurious effects not related to the pass-by sound emissions of ordinary vehicles are pinpointed as well.