Safe integration of autonomous vehicles (AVs) into the current public road system may imply potential improvements to roadway infrastructure. However, the current research effort is lacking in one of the important infrastructure to vehicle (I2V) areas, that is, roadway conditions, especially pavement friction. Motivated by the fact that pavement friction plays a critical role in safe driving for conventional vehicles and the fact that rear-end crashes are the most common type of crashes involving autonomous vehicles, this paper presents a first-of-its-kind effort to evaluate the driving safety of AVs with respect to pavement friction. An explicit relation was derived between pavement friction and traffic safety in terms of the stopping sight distance. The statistical characteristics of pavement network friction were determined from field friction measurements. A typical scenario was created to define rear-end crashes involving self-driving vehicles. The probabilities of rear-end crashes were estimated using the Monte Carlo method, and their implications were discussed. It was found that there is no urgent need to increase pavement friction requirements with respect to rear-end crashes involving self-driving vehicles. The findings may also be used to improve the extent of AV operational domains (ODs) and facilitate highway agencies to assess the readiness of road infrastructure in the form of roadway condition to support autonomous driving.

The implementation of a pavement preservation program involves a learning curve with not only a determination to succeed, but also the courage to fail. Also, successful implementation of pavement preservation program requires knowledge of the performance of preservation surface treatments over time, which is critical to the select of candidate projects and the development of performance models for pavement management analysis. In addition, preservation surface treatments, such as chip seal, fog seal, microsurfacing, 4.75 mm thin or ultra-thin overlay, can not only repair certain pavement surface defects, but also change the surface characteristics of pavement and therefore affect pavement surface friction performance. Nevertheless, such information is currently not available but is essential for the Indiana Department of Transportation (INDOT) to evaluate the effectiveness of pavement preservation surface treatments. As a concentrated effort, this study focused on the long-term friction performance of preservation surface treatments, particularly those have been widely used and those have seen increasing use by INDOT. Based on the selected field pavement test sections, this study aimed to evaluate the surface characteristics, particularly the long-term friction performance for those surface treatments that have been widely used and have seen increasing use by INDOT, including chip seal, fog-chip, fog seal, rejuvenating seal, microsurfacing, ultrathin bonded wearing course (UBWC), 4.75-mm hot mix asphalt (HMA) thin overlay, and profile milling (or diamond grinding). The test sections for each type of surface treatment covered a wide range of traffic volume from light to high. The service life for the selected test sections varied from 6 months to 60 months. Friction testing was mainly conducted using ASTM E 274 locked wheel trailer. Surface texture testing was conducted using either the ASTM E 2157 circular track meter (CTM) or a laser scanner. Pavement roughness and noise tests were also conducted to address the smoothness and noise issues, particularly on microsurfacing. Detailed analysis was provided to evaluate the friction performance of 4.75-mm HMA overlays. It is believed that the test results and findings drawn from this study not only provides timely information for INDOT to improve its pavement preservation program, but also provides the original information for the potential readers to better utilize preservation surface treatments.

Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis.

Pavement (or road) markings play an important role in road safety, influencing the dynamics of road users through their skid resistance properties. This study provides a comprehensive synthesis and analysis of international perspectives on the skid resistance of pavement markings and their requirements. It examines marking skid test results across various regions, including North America, Europe, and other parts of the world, and emphasizes the impact of different materials and test environments on skid resistance. The study also reviews current skid resistance standards and guidelines, from North American state-level standards to European and global specifications. Furthermore, it discusses the safety implications of these standards for diverse road users, especially motorcyclists, cyclists, and pedestrians. In conclusion, this paper highlights the importance of further innovation and consistency in skid resistance testing and standards to improve road safety.

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.