No abstract

No abstract

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.

The sound produced by Unmanned Aerial Systems (known as UAS or Drones) is often considered to be one of the main barriers (alongside privacy and safety concerns) preventing the widespread use of these vehicles in environments where they may be in close proximity to the general public. To better understand the potential environmental noise impact of commercial UAS operations, work undertaken by the University of Salford has focused on two key areas. Firstly, how to characterise and measure the sound produced by UAS during outdoor flight conditions and secondly, better understanding of the dose response of UAS noise when the listener is in either an indoor or outdoor environment. The paper describes a field measurement campaign undertaken to measure several UAS performing flyovers at different speeds and take-off weights. The methodology of the measurement campaign was strongly influenced by emerging guidance and has been used to calculate the directivity of sound propagation which may be of significant benefit when modelling environmental noise impacts. This paper also presents details of a listening experiment designed to investigate the subjective response to a number of UAS operations when the listener is simulated to be either in an indoor or outdoor position. The results of the listening experiment have been analysed using linear regression analysis to understand which 'loudness' metric either conventional (LAeq, LASmax or LAE) or more specialised loudness metrics such as Loudness (N5), Perceived Noise Level (PNL) or Effective Perceived Noise Level (EPNL) are most appropriate for estimating perceived 'loudness' and 'annoyance'. The results of this experiment indicate that both LAeq, LASmax were equally good at predicting the perceived loudness and annoyance with an Adjusted R Squared value of 0.90 and 0.93 for loudness and annoyance respectively. Loudness metric performed marginally better with adjusted R Squared values of 0.96 and 0.90 for annoyance and loudness respectively.

The number of applications for drones under R&D have growth significantly during the last few years; however, the wider adoption of these technologies requires ensuring public trust and acceptance. Noise has been identified as one of the key concerns for public acceptance. Although substantial research has been carried out to better understand the sound source generation mechanisms in drones, important questions remain about the requirements for operational procedures and regulatory frameworks. An important issue is that drones operate within different airspace, closer to communities than conventional aircraft, and that the noise produced is highly tonal and contains a greater proportion of high-frequency broadband noise compared with typical aircraft noise. This is likely to cause concern for exposed communities due to impacts on public health and well-being. This paper presents a modelling framework for setting recommendations for drone operations to minimise community noise impact. The modelling framework is based on specific noise targets, e.g., the guidelines at a receiver position defined by WHO for sleep quality inside a residential property. The main assumption is that the estimation of drone noise exposure indoors is highly relevant for informing operational constraints to minimise noise annoyance and sleep disturbance. This paper illustrates the applicability of the modelling framework with a case study, where maximum A-weighted sound pressure levels LAmax and sound exposure levels SEL as received in typical indoor environments are used to define drone-façade minimum distance to meet WHO recommendations. The practical and scalable capabilities of this modelling framework make it a useful tool for inferring and assessing the impact of drone noise through compliance with appropriate guideline noise criteria. It is considered that with further refinement, this modelling framework could prove to be a significant tool in assisting with the development of noise metrics, regulations specific to drone operations and the assessment of future drone operations and associated noise.