Road surface

Abstract: While emissions control regulation has led to a substantial reduction in exhaust emissions from road traffic, currently non-exhaust emissions from road vehicles are unabated. These include particles from brake wear, tyre wear, road surface abrasion and resuspension in the wake of passing traffic. Quantification of the magnitude of such emissions is problematic both in the laboratory and the field and the latter depends heavily upon a knowledge of the physical and chemical properties of non-exhaust particles. This review looks at each source in turn, reviewing the available information on the source materials and particles derived from them in laboratory studies. In a final section, some of the key publications dealing with measurements in road tunnels and the roadside environment are reviewed. It is concluded that with the exception of brake dust particles which may be identified from their copper (Cu) and antimony (Sb) content, unequivocal identification of particles from other sources is likely to prove extremely difficult, either because of the lack of suitable tracer elements or compounds, or because of the interactions between sources prior to the emission process. Even in the case of brake dust, problems will arise in distinguishing directly emitted particles from those arising from resuspension of deposited brake dust from the road surface, or that derived from entrainment of polluted roadside soils, either directly or as a component of road surface dust.

Abstract: This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.

Abstract: It is shown that typical road surfaces may be considered as realizations of homogeneous and isotropic two-dimensional Gaussian random processes. Complete description of any such process is provided by a single autocorrelation function evaluated from any longitudinal track, and a single direct spectral density function therefore provides a road surface description which is sufficient for multi-track vehicle response analysis. A new road classification method is proposed which is based on this function.

Abstract: Roads and traffic affect animal populations detrimentally in four ways: they decrease habitat amount and quality, enhance mortality due to collisions with vehicles, prevent access to resources on the other side of the road, and subdivide animal populations into smaller and more vulnerable fractions. Roads will affect persistence of animal populations differently depending on (1) road avoidance behavior of the animals (i.e., noise avoidance, road surface avoidance, and car avoidance); (2) population sensitivity to the four road effects; (3) road size; and (4) traffic volume. We have created a model based on these population and road characteristics to study the questions: (1) what types of road avoidance behaviors make populations more vulnerable to roads?; (2) what types of roads have the greatest impact on population persistence?; and (3) how much does the impact of roads vary with the relative population sensitivity to the four road effects? Our results suggest that, in general, the most vulnerable populations are those with high noise and high road surface avoidance, and secondly, those with high noise avoidance only. Conversely, the least vulnerable populations are those with high car avoidance only, and secondly, high road surface and high car avoidance. Populations with low overall road avoidance and those with high

Abstract: The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.