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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Review on factors influencing the light reflection properties of road surfaces – Part 2: External factors</title>
      <link>https://trid.trb.org/View/2694474</link>
      <description><![CDATA[To limit energy costs and environmental impacts related to road lighting, luminance-based designing is the best solution to provide the right amount of light on the surfaces to be lit. But this exercise requires knowledge of the reflection properties of the road surface, which are generally unknown. Therefore, the design is often done in terms of illuminance, and luminance is estimated using the standard r-tables provided by the CIE 50 years ago. This review article presents the current state of knowledge on the reflection properties of road surfaces and the external factors (age, traffic, climatic conditions) that may influence them for road lighting applications. These factors are closely connected, and they were not addressed in isolation. However, this article illustrates the evolving and dynamic nature of the reflection properties of road surfaces over time. To characterise a road surface in a state representative of most of its life, it is preferable to wait 2 years after application, especially for bituminous roads with no initial surface treatment. Furthermore, spatial heterogeneity could be considered by taking measurements in the centre track and in the wheel track.]]></description>
      <pubDate>Mon, 18 May 2026 11:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694474</guid>
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    <item>
      <title>Review on factors influencing the light reflection properties of road surfaces – Part 1: Internal factors</title>
      <link>https://trid.trb.org/View/2694336</link>
      <description><![CDATA[To limit energy costs and environmental impacts related to road lighting, luminance-based designing is the best solution to provide the right amount of light on the surfaces to be lit. However, this exercise requires knowledge of the reflection properties of the road surface, which are generally unknown. Therefore, the design is often done in terms of illuminance, and luminance is estimated using the standard r-tables provided by the CIE (Commission Internationale de l’Eclairage) 50 years ago. This review article presents the state of knowledge on the reflection properties of road surfaces and the internal factors (family and nature of road surface, composition, type of aggregates, binders used, surface treatments) that may influence them for road lighting applications. Although some trends emerge based on the family of road surface, the wide variety of these factors, their interactions, or the extent to which they have been studied make it difficult to establish generalisable rules. The main international consensus is the need to revise the CIE standard r-tables. This article also identifies the need for further research, and the descriptive summary of the characteristics of road surfaces and their field of use that we propose is a solid basis for carrying this out in a unified manner.]]></description>
      <pubDate>Mon, 18 May 2026 11:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694336</guid>
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    <item>
      <title>Evaluating Roadway Lighting Conditions with Advanced Image Processing Techniques</title>
      <link>https://trid.trb.org/View/2562185</link>
      <description><![CDATA[Street lighting is a significant factor in road safety. However, its assessment has traditionally been labor-intensive, often relying on manual measurements with handheld light meters. This paper introduces an innovative video-based image processing approach that effectively measures digital luminance and assesses the comparative lighting conditions of the road surfaces. The study focuses on three major interchanges in Louisville, Kentucky, two of which received new solar-powered streetlights. A GPS-enabled, vehicle-mounted smartphone-based imagery collection system was used to capture continuous footage of the road surface. The data were analyzed using advanced image-processing techniques, converting video into grayscale images for precise pixel intensity analysis to estimate digital luminance levels in specified regions of interests (ROIs). This approach depicts relative luminance in the form of time-series, descriptive statistics, heatmaps, and color-mapped videos. The video-based technique offers high-resolution lighting assessment over large areas and has the potential to lower costs, relative to existing methods.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562185</guid>
    </item>
    <item>
      <title>Study to Understand the Influence of Emergency Vehicle Color, Reflectance, Signing/Arrow Boards, and Lighting Configurations in Reducing Responder-Involved Crashes</title>
      <link>https://trid.trb.org/View/2663285</link>
      <description><![CDATA[This study aimed to reduce responder-involved crashes by improving the visibility and conspicuity of Road Ranger Service Patrol (RRSP) vehicles. The research combined a comprehensive literature review, a national survey of 44 agencies, and field testing of 16 distinct scenarios across three Florida highways (I-4, SR 429, and Florida Turnpike). Scenarios tested various combinations of emergency lighting colors, flash patterns, mounting heights, arrow/message board messages, and cone placements to evaluate their impact on driver compliance with the Move Over law. Red/white and red lights, higher light placements, directional arrow boards, and cone deployments were found to significantly improve move-over rates, in some cases by over 30%. In parallel, three vehicle marking designs were developed, focusing on maximizing visibility and public recognition through strategic use of color, reflectivity, and Florida Department of Transportation (FDOT) branding. A design featuring a fluorescent yellow-green base and retroreflective chevron markings received the highest preference in stakeholder evaluations. The recommended marking strategy divides vehicle surfaces into three zones: Safety (rear chevrons), Information (service messages and contacts), and Identity (FDOT and program branding). The study also highlighted the importance of uniform vehicle design to improve compliance, public perception, and integration with emerging vehicle technologies. FDOT is encouraged to adopt these recommendations incrementally through vehicle procurement and attrition cycles to enhance safety outcomes for RRSP operators statewide.]]></description>
      <pubDate>Fri, 20 Feb 2026 08:49:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663285</guid>
    </item>
    <item>
      <title>Traffic safety analysis using long-term accident record for merging and diverging section in Ethiopian Toll road expressway</title>
      <link>https://trid.trb.org/View/2622023</link>
      <description><![CDATA[Traffic disruptions (including frequent and abrupt lane changes in critical merging, diverging and overtaking zones) often result in expressway accidents. This study analysed crash data from the Ethiopian Toll Road Enterprise (2015–2022) using statistical and multinomial logistic regression models to identify high-risk crash locations, assess the severity and investigate the contributing factors in key merging and diverging sections. The analysis considered risk factors such as driver behaviour, traffic patterns, vehicle types, road conditions and lighting. The results indicated a 22.5% increase in accidents on wet pavements compared to dry surfaces across the entire length of the expressway, for a 2.04% increase in traffic volume. Fatalities and severe injuries were more frequent in the merging areas. Over 308 days of rainy weather across 8 years, accidents in the merging and diverging zones were 9.24% more likely to occur on wet roads than on dry surfaces. These observations highlight the increased accident risk caused by frequent and abrupt lane changes under wet conditions, emphasizing the need for improved safety measures in critical areas.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622023</guid>
    </item>
    <item>
      <title>Energy-Efficient Tunnel Paving Materials Based on Road Surface Luminance</title>
      <link>https://trid.trb.org/View/2613621</link>
      <description><![CDATA[To address the issue of excessive energy consumption in tunnel lighting, this study investigates the use of energy-efficient tunnel paving materials based on road surface luminance observed by drivers, which is applied by the reflection characteristics of the road surface. Using scaled model experiments and DIAlux lighting simulations, the luminance performance of nine types of pavement materials and three types of sidewall materials was systematically evaluated. The results show that sidewall materials primarily enhance lateral visual comfort and contribute minimally to improving pavement luminance. Even with high reflectance sidewalls at a reflectance level of 0.8, the luminance observed on the pavement increased by only 7.8 percent. In contrast, pavement materials with diffuse reflection can significantly improve both luminance and uniformity. Light-colored stones pavements demonstrated the highest energy-saving performance, achieving a reduction of up to 28 percent in energy consumption per kilometer when the embedding amount reached 6  kg/m². Additionally, poured semiflexible (PSF) pavement material also improves luminance and uniformity of tunnel pavement, yielding energy savings of up to 18%.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613621</guid>
    </item>
    <item>
      <title>Exploring drivers’ psychological responses in spiral tunnel: Visual attention and subjective perceptions</title>
      <link>https://trid.trb.org/View/2606766</link>
      <description><![CDATA[The objective of this study was to investigate the challenges associated with driving in spiral tunnels through a comprehensive analysis of both visual performance and subjective perceptions of drivers. By comparing driving behavior in spiral tunnels to that in conventional curved tunnels, the study aimed to identify specific differences in visual attention, cognitive processing, and perceived workload, ultimately informing tunnel design and safety improvements. Naturalistic driving experiments were conducted in two different tunnel environments: A conventional curved tunnel and a spiral tunnel. Participating drivers were equipped with eye-tracking device to measure visual performance indicators such as average fixation duration, average pupil diameter, average saccade duration, and average saccade amplitude. Additionally, drivers’ subjective perceptions of workload were assessed using the National Aeronautics and Space Administration Task Load Index (NASA-TLX) scale, which evaluates mental, physical, temporal, and emotional demands, as well as overall performance and frustration. The results of the study revealed significant differences in drivers’ visual performance and subjective perceptions between spiral and curved tunnels. In spiral tunnels, drivers exhibited longer average fixation durations and larger average pupil diameters, indicating increased cognitive processing and visual attention requirements. Furthermore, drivers in spiral tunnels had longer average saccade durations and smaller average saccade amplitudes, suggesting a more cautious and focused visual scanning strategy due to the tight turns and limited visibility. Subjectively, drivers reported significantly higher workload across all dimensions of the NASA-TLX scale in spiral tunnels, indicating greater mental, physical, temporal, and emotional demands compared to curved tunnels. This study reveals the challenges of spiral tunnels for drivers, especially regarding visual attention and cognitive load. It suggests that improving tunnel design elements like lighting, signage, and road surfaces can lower drivers’ cognitive demands and improve their visual processing. The research also emphasizes the importance of specialized driver training for navigating these tunnels safely. In summary, the findings enhance transportation safety by offering insights into driving behavior in complex tunnels and suggesting methods to reduce risks.]]></description>
      <pubDate>Mon, 20 Oct 2025 09:36:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606766</guid>
    </item>
    <item>
      <title>Simulation and emulation of water spray for validation of optical sensors (SEVVOS)</title>
      <link>https://trid.trb.org/View/2598586</link>
      <description><![CDATA[This research investigated visibility degradation caused by vehicle-generated water spray on wet surfaces, using experimental tests, simulations, and data analysis to examine spray dynamics and their effects on camera and sensor performance. Dynamic tests faced challenges with automated contrast analysis due to insufficient resolution, lack of camera calibration, and poor lighting. Targets were too small in images, and low contrast, even without spray, prevented reliable detection. Similar issues affected static tests, although higher light levels enabled more consistent results. High-beam headlights worsened contrast degradation by illuminating spray particles. These findings emphasized the importance of proper calibration, resolution, and lighting for accurate data collection. Outdoor tests on AstaZero test tracks showed that water depth and vehicle speed significantly influence spray and visibility.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:18:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598586</guid>
    </item>
    <item>
      <title>Werbung an Fahrzeugen</title>
      <link>https://trid.trb.org/View/2589142</link>
      <description><![CDATA[Der Straßenraum ist für Werbetreibende seit jeher ein attraktiver Bereich, um Aufmerksamkeit auf die eigenen Produkte, Marken oder Dienstleistungen zu lenken. Durch verbesserte und billiger werdende Technik wie LED rücken zunehmend auch Fahrzeuge als mögliche Werbeträger in den Fokus und müssen im Hinblick auf ihre möglichen, negativen Einflüsse auf die Verkehrssicherheit und den Verkehrsfluss bewertet werden. Zur (technischen) Regelung von entsprechenden Anbauten wurden unter anderem Aspekte der Darstellung, Anbringung und möglichen Beeinträchtigung von Verkehrsteilnehmenden diskutiert und die Vorschriftenlage erläutert. Im Bereich der Sensorik und Lichttechnik liegen bereits durchaus brauchbare Lösungen vor, die in ihren Einflüssen aber noch nicht ausreichend abgeschätzt wurden. Merkmale wie die Leuchtdichte, die Farbigkeit und die Veränderlichkeit (das heißt die Darstellung bewegter Inhalte) werden bezüglich ihrer Relevanz für den Straßenverkehr thematisiert und mit Erkenntnissen aus der theoretischen Betrachtung und der Empirie untermauert. In zwei getrennten Versuchsteilen wurden anschließend zum einen die Störwirkung durch Blendung (in einer nächtlichen Versuchsumgebung) und zum anderen die Ablenkungswirkung (bei Tagessichtbedingungen und jeweils auf Stadt- und Autobahnstrecken) untersucht. Neben Paarvergleichen und Einzelratings wurden umfangreiche Simulatorversuche durchgeführt, an denen jeweils 42 Versuchspersonen teilnahmen. In die Betrachtung der Störwirkung durch Blendung flossen Faktoren wie die Veränderlichkeit, die Leuchtdichtestruktur und die Farbigkeit ein und wurden entsprechend variiert. Aus der subjektiven Blendbewertung durch die Versuchspersonen ließen sich eine höhere Ablenkung durch veränderliche, inhomogene sowie farbige Darbietungen innerhalb der Störwirkung im Vergleich zu unbunten und homogenen Flächen zeigen. Einzelne Einflüsse auf das Fahrverhalten waren ebenfalls zu beobachten. Des Weiteren zeigte sich, dass eine Blendbewertung von fahrzeuggetragener Werbung prinzipiell mittels etablierter Maße plausibel möglich ist und zukünftig mindestens die Merkmale Flächenleuchtdichte, Flächengröße, die Umgebungshelligkeit und innere Darstellungsinhalte wie die Veränderlichkeit beinhalten sollte. Die Ablenkungswirkung wurde in Bezug auf das Fahrverhalten, die Blickbewegungen und die selbstberichtete Beanspruchung während verschiedener Szenarien untersucht, in denen die Versuchspersonen Lkw mit Heckwerbung hinterherfuhren. Die Anzeigen wurde dabei bezogen auf die Veränderlichkeit (statisch versus Wechseldarstellung versus dynamisch) und die Relevanz (relevant versus nicht relevant) variiert. Vor allem in der städtischen Umgebung ließ sich über mehrere Blickbewegungsmaße hinweg zeigen, dass von dynamischerer Werbung ein höheres Ablenkungsrisiko ausgeht. In einigen Szenarien wurde den Versuchspersonen die Möglichkeit zum Überholen gegeben, hier fuhren sie bei wechselnden Darstellungen näher an den Lkw heran und überholten bei relevanten knapper. Durch die empirischen Untersuchungen konnten eine Reihe von Einflussfaktoren durch digitale Werbung an Fahrzeugen identifiziert werden, deren gleichzeitiges Auftreten in einem sogenannten Worst-Case-Szenario durchaus dazu führen kann, dass erhebliche Risiken für die beteiligten Verkehrsteilnehmenden bestehen. Um die reellen Konsequenzen, zum Beispiel bezogen auf das Unfallrisiko durch digitale Werbesysteme an Fahrzeugen abschätzen zu können, bedarf es weiterer Forschung und alternativer Forschungsansätze. (A) ABSTRACT IN ENGLISH: Public roads have always been an attractive area for advertisers to draw attention to their products, brands or services. Due to improved and cheaper technology such as LEDs, vehicles are increasingly coming into focus as potential advertising media. This new advertising spaces must be evaluated with regard to their possible negative impact on traffic safety and traffic flow. For the (technical) regulation of corresponding attachments, aspects such as the display, the installation and possible impairment of road users were discussed. Furthermore, the regulatory situation was explained. There are already viable solutions in the field of sensor and lighting technology, but their impact has not yet been sufficiently assessed. Characteristics such as luminance, color and variability (i. e. the amount of moving of the content displayed) are discussed in terms of their relevance for road traffic and substantiated with findings from theoretical and empirical studies. In two separate experimental parts, the disruptive effect of glare (in a night-time test environment) and the distraction effect (in daytime visibility conditions and on city as well as highway routes) were then investigated. In addition to pair comparisons and individual ratings, extensive simulator tests were carried out. In each of the experimental parts 42 test subjects took part. Factors such as variability, luminance structure and color were taken into account and varied accordingly when considering the disruptive effect of glare. The subjective glare assessment by the test subjects showed clear indications of greater distraction within the disruptive effect compared to achromatic and homogeneous surfaces. Occasional influences on driving behavior were also observed. Furthermore, it was shown that a glare assessment of vehicle-mounted advertising is in principle plausibly possible using established measures and should in future include at least the characteristics of surface luminance, surface size, ambient brightness and internal display content such as variability. The distraction effect was examined in relation to driving behavior, eye movements and self-reported stress during various scenarios. The test subjects had to drive behind trucks with rear advertising, which was varied in terms of variability (static versus alternating versus  dynamic) and relevance (relevant versus non-relevant). Particularly in the urban environment, several measures of eye movement showed that dynamic advertising poses a higher risk of distraction. In some scenarios, the test subjects were given the opportunity to overtake; in this case, they drove closer to the truck with changing displays and overtook more closely with relevant displays. The empirical studies identified a number of factors influenced by digital advertising on vehicles. The simultaneous occurrence of those could - in a worst-case scenario - lead to considerable risks for the road users involved. Therefore, further research and alternative research approaches are needed to assess the real-life consequences, e. g. the risk of accidents caused by digital advertising systems on vehicles. (A)]]></description>
      <pubDate>Mon, 08 Sep 2025 14:51:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2589142</guid>
    </item>
    <item>
      <title>Exploring e-scooter risk factors based on interpretable machine learning framework</title>
      <link>https://trid.trb.org/View/2567485</link>
      <description><![CDATA[The recent rise in e-scooter usage has reshaped urban mobility but has also led to a significant increase in e-scooter-related injuries, raising critical safety concerns. While existing research has primarily focused on post-crash medical outcomes and general risk comparisons, substantial gaps remain in identifying specific risk factors associated with e-scooter crashes and utilizing interpretable analytical approaches. This study addresses these gaps by analyzing 2,400 e-scooter crash records from the UK STATS19 database using advanced machine learning models to predict injury severity. SHAP analysis and dependence plots were used to explore key risk factors and their interactions. The assessment of model performance indicates that LightGBM outperforms other models, while SHAP analysis identified several key factors influencing e-scooter crash severity, including number of vehicles involved, first point of impact, rider’s gender, lighting conditions, pedestrian crossings, and road types. Dependence plots revealed that male e-scooter riders are more likely to experience severe crashes with an increasing number of vehicles involved, in low-light conditions, and at higher speed limits. In addition, crashes occurring at higher speed limits on single-carriageways and wet surfaces increase the likelihood of severe injuries. Based on these findings, the study proposes several recommendations to enhance e-scooter riders’ safety.]]></description>
      <pubDate>Wed, 20 Aug 2025 11:57:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2567485</guid>
    </item>
    <item>
      <title>Detection of Invisible/Occluded Vehicles Using Passive RFIDs</title>
      <link>https://trid.trb.org/View/2407042</link>
      <description><![CDATA[Vehicle detection in autonomous driving could be very challenging under adverse road conditions. The problem has been studied intensively. However, recent studies have shown that the problem remains unsolved, especially when the vehicles are occluded or under low-light conditions. This paper adopts a different approach to vehicle detection by taking advantage of RFID technology. Specifically, RFID tags are attached to the vehicle’s surfaces, and then a system is designed to detect, locate, and track those tags dynamically. In addition, RFIDs are allowed to store user data on chips. To fully utilize this feature, this paper develops an algorithm to select and store the most critical information in tags for recovering the boundaries of occluded vehicles and finding the vehicle’s location and orientation. The proposed method achieves the following objectives: (1) Vehicles could be detected at a relatively long distance in any conditions (including low-light or adverse weather). (2) The boundary of the occluded vehicle could be recovered. (3) Vehicles are still detectable even if they are turned off. (4) The implementation is relatively simple. The evaluation results have shown that the proposed method is able to detect a vehicle’s orientation and rotation and recover the boundary for an occluded vehicle.]]></description>
      <pubDate>Tue, 22 Jul 2025 10:32:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407042</guid>
    </item>
    <item>
      <title>Tire–Road Friction Estimation and Classification Based on a CNN using Tire Acoustical Signals for Autonomous Driving Vehicles</title>
      <link>https://trid.trb.org/View/2539694</link>
      <description><![CDATA[Advanced driver assistance systems (ADASs) and driving automation system technologies have significantly increased the demand for research on vehicle-state recognition. However, despite its critical importance in ensuring accurate vehicle-state recognition, research on road-surface classification remains underdeveloped. Accurate road-surface classification and recognition would enable control systems to enhance decision-making robustness by cross-validating data from various sensors. Therefore, road-surface classification is an essential component of autonomous driving technologies. This paper proposes the use of tire–pavement interaction noise (TPIN) as a data source for road-surface classification. Traditional approaches predominantly rely on accelerometers and visual sensors. However, accelerometer signals have inherent limitations because they capture only surface profile properties and are often distorted by the resonant characteristics of the vehicle structure. Similarly, image-based signals are susceptible to external factors such as lighting conditions, obstacles, and motion blur, which can compromise their reliability. In contrast, TPIN signals offer a more comprehensive representation of both the surface profile and texture characteristics of the road. Additionally, TPIN signals are less susceptible to environmental interferences that affect image-based methods. The TPIN signals are transformed into two-dimensional images using time–frequency analysis. These transformed images are subsequently utilized in conjunction with a convolutional neural network (CNN) architecture to evaluate the feasibility of a robust road-surface classification system. The system was implemented using MATLAB Simulink. Furthermore, this study explored the application of CNN-based artificial intelligence techniques to predict the tire–road friction coefficients across various road surfaces, providing a deeper understanding of the underlying principles governing tire–road interactions.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539694</guid>
    </item>
    <item>
      <title>Determination of Pavement Surface Type</title>
      <link>https://trid.trb.org/View/2522023</link>
      <description><![CDATA[This study evaluated and developed machine learning models for pavement surface classification to enhance accuracy and reliability using advanced computational techniques. Initially, a detailed literature review on pavement texture and measurement methods provided a foundation for developing a prototype system. This system, integrating laser scanners, high-speed cameras, and lighting systems, marked a significant advancement in capturing high-resolution texture data at highway speeds. Data collection covered 425.5 miles of pavement across Texas, including 313.7 miles of flexible and 111.8 miles of rigid pavements, resulting in over 50,000 high-resolution images and texture profiles from 15 types of flexible pavements and seven types of rigid pavements. This dataset, essential for training and validating machine learning models, was made available for future research. A hierarchical classification method was developed, with picture-based models (PBC) excelling at predicting flexible or rigid pavements (Level 1) and tining orientation (Level 5), while index-based classifiers (IBCs) performed better for intermediate levels of specificity (Levels 2-4). The best models achieved high F1 scores, confirming their effectiveness. Comprehensive validation using six diverse test sets across 20 test sites confirmed the models’ robustness and applicability. These findings significantly enhance pavement management systems, enabling more accurate and efficient maintenance strategies. Leveraging advanced machine learning techniques, the developed models can improve road safety, optimize resource allocation, extend pavement lifespan, and support future research and practical applications.]]></description>
      <pubDate>Thu, 20 Mar 2025 13:25:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2522023</guid>
    </item>
    <item>
      <title>Study on properties of self-lighting cement pavement material</title>
      <link>https://trid.trb.org/View/2511202</link>
      <description><![CDATA[The paper investigated the influence of luminescent sand and reflectance powder on the workability, mechanical properties, and luminescence performance of cement-based material. The results showed that adding luminescent sand and reflectance powder decreased the consistency and fluidity of the mixture. Furthermore, incorporating these materials can enhance the mechanical strength of self-luminous samples. Nevertheless, exceeding 40% in the content of luminous sand or surpassing 10% in the content of reflective powder will reduce its strength. Furthermore, the initial brightness of these materials can reach 0.82cd/m², and the afterglow time can reach 12 h when the luminous sand content is 50% and the reflective powder content is 20%. These results indicate that luminescent sand and reflective powder can improve the luminescence properties of cement-based materials. The incorporation of luminescent sand optimises the distribution of hydration products, enhancing the luminescence performance of the cement-based materials. These research findings will provide new insights for the design of road surfaces in low-light environments.]]></description>
      <pubDate>Fri, 28 Feb 2025 16:44:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511202</guid>
    </item>
    <item>
      <title>Research on the correlation between asphalt mixture surface texture and the light reflection coefficient of pavement</title>
      <link>https://trid.trb.org/View/2487096</link>
      <description><![CDATA[LED lamps could save energy and improve road safety by replacing traditional street lamps. Still, the reflective properties of asphalt pavement under LED light sources have yet to be studied enough. The correlation between texture indicators and optical parameters measured at different locations of asphalt mixture specimens under LED light source was investigated to study the influence mechanism of asphalt pavement type and texture characteristics on light reflection characteristics. According to normalization, the equivalent of reflection coefficients and luminance correlate with the Mean Texture Depth (MTD) and the Root Mean Square Slope (Δq). A quantitative expression model of the reflection properties was developed. The results showed that the texture characteristics influenced the luminance and reflection coefficients of different asphalt mixtures. The maximum nominal particle size was larger, the luminance was lower, and the reflection coefficient was smaller. Various types of asphalt pavements absorb light to varying degrees due to differences in void ratio and surface structure. The reflection coefficients and luminance of the asphalt mixture specimens showed a good fit with both the single-factor and multifactor expression models developed for MTD and Δq. The test results showed that the multifactor linear model had the highest overall prediction accuracy. The model revealed the mechanism by which texture indicators influence the illumination efficiency of pavement surfaces. The research results have provided a reference basis for optimizing the layout of roadway lighting equipment and a scientific basis for improving the safety of roadway lighting.]]></description>
      <pubDate>Thu, 20 Feb 2025 16:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487096</guid>
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