Kelvin C. P. Wang

Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing.

Accurate identification of cracks at the pixel level on intricate asphalt pavements represents a crucial challenge in the domain of intelligent pavement assessment. The current advanced deep-learning networks encounter limitations in simultaneously capturing both the global context and local features of cracks, leading to discontinuous segmentation results and suboptimal recovery of local details. This paper proposes a robust architecture named Mix-Graph CrackNet to present an efficacious solution for this challenge. The Mix-Graph CrackNet, as proposed, is designed to mix the global context and local features multiple times, allowing for a comprehensively understanding of the essential features. Specifically, this paper develops the learnable parallel convolutional-Transformer mixing module to parallelly capture the sophisticated local features as well as the crucial global context. In addition, a new fusion unit is devised in the paper and deployed in the learnable parallel convolutional-Transformer mixing module. The proposed fusion unit is capable of effectively mixing contextual features extracted at both global and local scales while retaining an abundant level of textural details germane to the crack. Moreover, this paper constructs a graph-based skip connection that functions as a shortcut connecting the encoder and decoder, with the primary objective of mitigating information decay. The experimental results are remarkable, with the Mix-Graph CrackNet achieving F-measure and Intersection-Over-Union of 90.37% and 82.43%, respectively, on 1000 testing images. Based on the performance evaluations conducted on both public and private datasets, the proposed Mix-Graph CrackNet architecture demonstrates a significantly superior detection accuracy in comparison to several state-of-the-art models for semantic segmentation.

Traditionally, pavement safety performance in terms of texture, friction, and hydroplaning speed are measured separately via different devices with various limitations. This study explores the feasibility of using a novel 0.1 mm 3D Safety Sensor for pavement safety evaluation in a non-contact and continuous manner with a single hardware sensor. The 0.1 mm 3D images were collected for pavement safety measurement from 12 asphalt concrete (AC) and Portland cement concrete (PCC) field sites with various texture characteristics. The results indicate that the Safety Sensor was able to measure pavement texture data as traditional devices do with better repeatability. Moreover, pavement friction numbers can be estimated using 0.1 mm 3D data via the proposed 3D texture parameters with good accuracy using an artificial neural network, especially for asphalt pavement. Lastly, a case study of pavement hydroplaning speed prediction was performed using the Safety Sensor. The results demonstrate the potential of using ultra high-resolution 3D imaging to measure pavement safety, including texture, friction, and hydroplaning, in a non-contact, continuous, and accurate manner.

Oklahoma Department of Transportation (ODOT) has been sponsoring the National Center of Asphalt Technology (NCAT) test track studies since its fi |

Even though 30% of the annual highway fatalities originate from inferior roadway conditions, crash costs under regular operations have seldomly been included in the life cycle cost analysis (LCCA). Among the various condition indicators, pavement roughness has been the most investigated on roadway safety, while skid resistance has been less studied. This paper presented an integral decision-making process to integrate pavement skid resistance and its corresponding safety benefits into the LCCA for pavement surface treatment selection. Built on the processing of an extensive amount of traffic crash, pavement skid, and surface condition data provided by the Oklahoma DOT (ODOT), friction demands at the investigation and intervention levels were recommended to trigger surface treatments. Later, friction deterioration models were established to evaluate the skid performance of treatments over time. Multivariate analysis results indicated that aggregate properties and treatment types were among the most important factors for pavement friction. Subsequently, an enhanced safety performance function (SPF) was developed to predict expected roadway crashes under different skid levels. Pavement friction was a statistically significant factor and had a negative effect on vehicle crashes. The predicted pavement friction variations and the corresponding safety benefits of surface treatments were calculated and included in a spreadsheet tool developed from this study. A case study was provided to demonstrate the LCCA results with and without considering the safety costs of different treatments. Neglecting crash costs in LCCA would result in agencies adopting alternatives with lower short-term agency costs but underestimating the long-term safety benefits.

Transportation agencies build statistical models and predict the average crash frequency to identify hazardous road sections and make informed decisions to reduce crashes. In this paper, safety performance models (SPFs) were built and evaluated considering various pavement and roadway characteristics, including pavement friction, which is seldom available for analysis. Four count data models—Poisson model, negative binomial (NB) model, hurdle-NB model (HNB), and zero-inflated NB (ZINB) model—were built based on roadway characteristics and crash data provided by the Oklahoma Department of Transportation (ODOT). Pavement friction, roadway geometry, surface condition characteristics, and traffic exposure were considered the contributing factors to traffic crashes. Established models were compared in terms of the goodness-of-fit, zero inflation, and statistical significance of factors. The HNB model exhibited promising fitting performance with a manageable number of influencing variables. Coefficients in the HNB model suggest that adequate pavement friction and the presence of shoulders can significantly reduce the crash frequency and thus improve roadway safety performance. Potential issues of the statistical models, such as unobserved heterogeneity and multicollinearity, were also discussed. The relation between roadway infrastructure characteristics (including pavement friction) and roadway safety revealed in this study could assist in choosing the proper statistical model for better decision-making and selecting appropriate preventive treatments for improved roadway safety.