Urban development driven by population growth and technological advancements has intensified urban heat islands (UHIs), contributing to environmental damage and health risks. This study explores the potential of cool pavements as a critical strategy for mitigating UHIs, focusing on reflective, evaporative, and energy-storing technologies. Over 400 reputable scientific articles were reviewed to analyze UHI causes; measurement methods, including remote sensing and laboratory techniques; and the effectiveness of various pavement solutions. Reflective pavements demonstrated a capacity to lower surface temperatures by 5–20 °C depending on reflectivity changes, while evaporative pavements reduced temperatures by 5–35 °C based on type and design. Advanced energy-storing pavements not only achieved a 3–5 °C temperature reduction but also generated renewable energy. This research provides a comprehensive classification of pavement cooling systems and evaluates their quantitative and qualitative benefits, emphasizing the transformative role of cool pavements in enhancing urban sustainability and reducing UHI effects.

As autonomous vehicles (AVs) become more prevalent in traffic systems and human drivers are beginning to interact with them, understanding how the presence of AVs affects traffic dynamics and safety is becoming more important. This study investigates the impact of AVs on the behavior of human-driven vehicles (HDVs) in mixed traffic environments. One key question is whether the conspicuity of an AV influences HDV car-following behavior. Using data from a naturalistic driving study conducted in Ohio, U.S., for different road types, this research compares interactions with two types of advanced driver assistance system (ADAS)-equipped vehicle: readily identifiable (RI) with visible sensor arrays, and discreet (DI) with concealed sensors. Using statistical tests such as the Kolmogorov-Smirnov test and the Mann-Whitney U test, the analysis of key metrics—including following distances, gaps, speed differences, acceleration, braking patterns, and lateral distances during overtaking events—revealed notable differences in following behavior when trailing an RI vehicle. Specifically, drivers tended to maintain greater distances and adjust their speed and acceleration patterns more when following RI vehicles than when following DI vehicles. The results also indicated that drivers braked more aggressively on freeways and non-divided roads when following RI vehicles, and they maintained greater distances when overtaking on freeways. Assessing surrogate measures of safety, such as time-to-collision and deceleration required to avoid crash, further confirmed what appears to be safer driving behavior when following RI vehicles. This suggests that human drivers interact differently around AVs depending on their visibility. These findings underscore the need to consider the visibility of AV features in traffic management and vehicle design to optimize traffic flow and enhance road safety.

Pavement distress detection plays a crucial role in pavement management and rehabilitation (M&R) by providing essential data for maintenance decision-making. The Attain dataset is introduced to facilitate the development of machine learning and deep learning models for pavement distress classification and object detection. The dataset consists of 2293 images collected using three smartphones mounted on vehicle windshields. All photos are manually annotated by pavement engineer experts. The images capture a variety of pavement conditions under different lighting and traffic scenarios, making the dataset diverse and suitable for training robust models. The dataset encompasses ten distinct pavement distress categories including alligator cracks, block cracks, longitudinal and transverse (linear) cracks, faded marking, lane/shoulder drop-off, patch and utility cut, potholes, manhole, weathering, and raveling at two levels of severity i.e., low, medium, and high. In total, 19,761 distress-type instances were detected and annotated with distress type and severity. The core competency of the Attain dataset is the wide range of pavement distress types included and the severity levels of each distress provided. The use of readily available smartphone cameras significantly reduces the cost of data collection while maintaining high-quality image acquisition. The Attain dataset aims to support researchers and practitioners in developing automated pavement distress detection systems to enhance the efficiency and accuracy of pavement maintenance processes.