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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Initial Analytical Investigation of Cantilever and Butterfly Steel Overhead Sign Trusses with Respect to Remaining Fatigue Life</title>
      <link>https://trid.trb.org/View/2709181</link>
      <description><![CDATA[Fatigue failure of highway sign structures due to sustained wind-loading events has been recognized in many states. In fact, the American Association of State Highway and Transportation Officials specifies that the structural component should be designed for infinite life by maintaining wind-induced stress below their constant amplitude fatigue threshold. However, because existing structures are typically not designed for fatigue, the condition of all critical and fatigue-prone components must be evaluated for safety. Visual inspection requires extensive time and effort and may not detect unnoticed fatigue cracks, so growing attention has focused on analytical inspection tools to examine all critical members and connections for remaining fatigue life to ensure public safety. The reliability of these analytical tools depends on the accuracy of wind-loading models applied during the life span of the structure. This study devised a fill-interpolate-extend approach to furnish a wind-loading data ensemble for the duration of analysis. The ensemble established a reliable synthetic wind model to generate fatigue cycle counts. In addition, a comprehensive analytical framework, including structural modeling, stress extraction/processing, and fatigue damage simulation, was integrated to yield an affordable tool that is applicable to various sign structures topologies. The resulting software for noncantilever overhead structures as well as cantilever and butterfly assemblies were successfully verified to predict real cases for fatigue damage, reflecting the in-situ condition of the structures.]]></description>
      <pubDate>Mon, 08 Jun 2026 08:32:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709181</guid>
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    <item>
      <title>Influences of illuminated speed limit markings on driving behaviors—A driving simulation study</title>
      <link>https://trid.trb.org/View/2704289</link>
      <description><![CDATA[Roadside speed limit signs and conventional painted markings are widely used, but their effectiveness is often compromised under low-visibility conditions, leading to insufficient speed awareness and compliance. To address this issue, this study introduces illuminated speed limit markings of two different sizes and compares their effects on driving behavior with conventional signs under different conditions. Using linear mixed models (LMM) and generalized linear mixed models (GLMM), the authors examined the effects of driver characteristics, environmental factors, and sign types on five key driving performance indicators, moderating effects of weather and time. Results show that illuminated markings significantly outperform conventional signs under low-visibility conditions: small illuminated markings reduce speed variation and improve compliance in foggy weather, while large ones excel at nighttime. Both sizes of illuminated markings induce more pronounced deceleration tendencies and smoother speed adjustments. These findings highlight the potential of illuminated markings to enhance traffic safety, especially under low-visibility conditions. Future field trials are needed to validate their real–world effectiveness and practical feasibility.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:56:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704289</guid>
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    <item>
      <title>City of Philadelphia Digital Right-of-Way and Mobility Improvement Project</title>
      <link>https://trid.trb.org/View/2705982</link>
      <description><![CDATA[Through the Digital Right-of-Way (ROW) and Mobility Improvement Project, the City of Philadelphia developed and implemented a set of new technologies to test digital management of the right-of-way (ROW). Traditionally, as in other cities, Philadelphia issues regulations on how roadways and sidewalks may be used or blocked, and posts physical signs that users must read, interpret, and follow to use the street safely and legally. The Smart Cities team, in partnership with the Streets Department, Open Mobility Foundation, and several vendors, tested communicating these right-of-way regulations and closures digitally, such that a user’s phone or vehicle could receive them, parse out relevant information for specific location/day/time and turn it into guidance for the user - for instance, should the user park or not, and if a user is blocking a bike lane or not. One of the most important deliverables for the project was the development of the Right-of-Way Data Specification (ROWDS), a formal way to standardize and structure City ROW rules for computers to use, and its implementation through Road Rules, a software product by the company INRIX. These technologies have helped increase the spatial resolution (the level of detail for mapping small areas) of the underlying basemap to match what the Streets Department needs to operate. The project also demonstrated that higher spatial resolution can help the city better manage street-closure permitting. As this project is operationalized through a citywide deployment, it will pave the way for an improved user experience for everyone who uses the roads and a more efficient permitting system for the City of Philadelphia.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:02:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705982</guid>
    </item>
    <item>
      <title>Scoping Study: Vertical Visibility Constraints -- Vertical Curvature Traffic Control Devices</title>
      <link>https://trid.trb.org/View/2709249</link>
      <description><![CDATA[Horizontal and vertical curves can obscure key roadway features or activity that may lie ahead of unaware drivers. Roadway curvature is a significant factor in roadway departure crashes, injuries, and fatalities. As land use has developed and activities on roads have changed, the potential for conflicts has grown. It is impractical and beyond the resources of roadway authorities to improve all roadway alignments to attain optimal sight distance. This is a growing concern as active transportation increases in many rural areas, especially those experiencing increased tourism. Horizontal curvature on roadways where drivers’ views are obstructed has been thoroughly researched, leading to well-accepted strategies for traffic control devices in the Manual on Uniform Traffic Control Devices (MUTCD). However, similar research has yet to be conducted for vertical curves.

OBJECTIVE; The objective of this research is to develop a scoping study to clearly define and refine the research needs, objectives, and expected products necessary to address vertical visibility constraints, including exploring the relevance of crash data to vertical curves and developing a research work program to explore solutions. The intent of potential larger, follow-on, NCHRP study is to obtain data from vertical-curvature-related crashes to assess the details of occurrence, frequency, and severity, and to better understand road user needs, rather than relying on approaches used in prior studies.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:49:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709249</guid>
    </item>
    <item>
      <title>In-Service Evaluation of Temporary Sign Support Systems Against Wind Load</title>
      <link>https://trid.trb.org/View/2704032</link>
      <description><![CDATA[This short study analyzes the performance of multiple temporary sign support systems under windy conditions and provides recommendations to the Bureau of Safety Programs and Engineering of the Illinois Department of Transportation (IDOT). The efforts included a literature review that summarizes past studies on the use of temporary sign support systems and the effects of winds on their structural integrity and stability as well as current practices on the use of such systems. Then, a set of field experiments were performed to test a list of temporary sign support systems under different natural and truck-generated wind loads, revealing critical conditions where they can fail. Finally, we extended a finite-element analysis to evaluate the structural impact of natural and truck-generated winds on selected sign support systems. Slow-ramping or longer-duration gusts produce greater sign deflections than short-duration gusts, and winds induced by a single truck or a three-truck platoon generate no significant sign deflection. These findings are summarized into deployment recommendations that account for the types of sign support systems and wind gust speeds. These recommendations will help IDOT make deployment decisions based on historical maximum gust speed data in each IDOT district per season.]]></description>
      <pubDate>Fri, 29 May 2026 13:40:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704032</guid>
    </item>
    <item>
      <title>YOLO-TS: Real-Time Traffic Sign Detection With Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion</title>
      <link>https://trid.trb.org/View/2658856</link>
      <description><![CDATA[Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, they often compromise between speed and accuracy. To address this issue, we present a novel real-time and efficient road sign detection network, YOLO-TS. This network significantly improves performance by optimizing the receptive fields of multi-scale feature maps to align more closely with the size distribution of traffic signs in various datasets. Moreover, our innovative feature-fusion strategy, leveraging the flexibility of Anchor-Free methods, allows for multi-scale object detection on a high-resolution feature map abundant in contextual information, achieving remarkable enhancements in both accuracy and speed. To mitigate the adverse effects of the grid pattern caused by dilated convolutions on the detection of smaller objects, we have devised a unique module that not only mitigates this grid effect but also widens the receptive field to encompass an extensive range of spatial contextual information, thus boosting the efficiency of information usage. Moreover, to address the scarcity of traffic sign datasets, especially under adverse weather conditions, we introduce two novel datasets: Generated-TT100K-weather and CAWTSSS. Extensive evaluations conducted on challenging public benchmarks—including TT100K, CCTSDB2021, and GTSDB—as well as on our proposed datasets, demonstrate that YOLO-TS surpasses current state-of-the-art methods in both accuracy and inference speed. The code, datasets and weights are available at https://github.com/Heqiang-Huang/YOLO-TS]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658856</guid>
    </item>
    <item>
      <title>SignEye: Traffic Sign Interpretation From Vehicle First-Person View</title>
      <link>https://trid.trb.org/View/2658815</link>
      <description><![CDATA[Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navigation instructions. However, current works are limited to basic sign understanding without considering the egocentric vehicle’s spatial position, which fails to support further regulation assessment and direction navigation. Following the above issues, we introduce a new task: traffic sign interpretation from the vehicle’s first-person view, referred to as TSI-FPV. Meanwhile, we develop a traffic guidance assistant (TGA) scenario application to re-explore the role of traffic signs in ADS as a complement to popular autonomous technologies (such as obstacle perception). Notably, TGA is not a replacement for electronic map navigation; rather, TGA can be an automatic tool for updating it and complementing it in situations such as offline conditions or temporary sign adjustments. Lastly, a spatial and semantic logic-aware stepwise reasoning pipeline (SignEye) is constructed to achieve the TSI-FPV and TGA, and an application-specific dataset (Traffic-CN) is built. Experiments show that TSI-FPV and TGA are achievable via our SignEye trained on Traffic-CN. The results also demonstrate that the TGA can provide complementary information to ADS beyond existing popular autonomous technologies.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658815</guid>
    </item>
    <item>
      <title>The assessment of fire damaged street furniture in Lahaina, Maui, using YOLOv12 and 360° imagery</title>
      <link>https://trid.trb.org/View/2701476</link>
      <description><![CDATA[Using 360° imagery before and after the 2023 Lahaina fire disaster, damage to street furniture including traffic signs, control devices, street lighting, utility poles, and other objects, is assessed. In addition to describing the collection of panoramic 360° images using a Mosaic X camera, the detection and characterization of objects using YOLOv12 and other software are described. Accuracy, reliability, and bias associated with data collection and analysis are discussed for the most common types of street furniture. The research and machine vision tools are helpful for emergency management and for routine and repair and maintenance operations. The technologies and processes contribute to the development of digital roadway twins and novel applications for transportation planning, operations, repair, and construction of critical roadway infrastructure.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701476</guid>
    </item>
    <item>
      <title>Benchmarking Computer Vision-Based Approaches to Derive Engineering-Oriented Condition from Existing UDOT Assets Data</title>
      <link>https://trid.trb.org/View/2685461</link>
      <description><![CDATA[Condition assessment of how transportation infrastructure supports safe and reliable road and highway operation. Departments of Transportation across the country rely heavily on manual inspections, which are time-consuming and costly. This study evaluated whether modern computer vision (CV) methods can support traffic sign condition assessment along Utah highways. High-resolution roadway images collected using a camera-mounted vehicle were curated and annotated for three sign types (regulatory, warning, and guide) and four defect conditions (fading, delamination, missing letters/symbols, and broken signs) based on the Manual on Uniform Traffic Control Devices (MUTCD) standards. This study compared two different CV algorithms of YOLO11 and RT-DETR for traffic-sign detection and defect classification. Overall, the CV models showed promising performance for defect cases where an adequate number of training data existed. For example, for fading, YOLO11 and RT-DETR achieved 75% F1 on the validation. Binary classification of delamination (i.e., delamination versus no delamination) yielded similar performance for both models (68% F1). In contrast, the models showed poor performance to identify missing letters/symbols due to texture overlap with delamination and a limited number of annotated sign images with such defects. The results suggested that data quality and label definition had a greater impact on model performance than the choice of algorithms for the studied models.]]></description>
      <pubDate>Fri, 15 May 2026 17:01:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685461</guid>
    </item>
    <item>
      <title>Definitions for Cycling Infrastructure</title>
      <link>https://trid.trb.org/View/2667222</link>
      <description><![CDATA[This paper presents a set of harmonized definitions for cycling infrastructure developed by the UNECE Group of Experts on the Cycling Infrastructure Module (2022–2024). Drawing on recent developments across UNECE member countries, the Group recognized significant changes in cycling practices, infrastructure types, signage, and regulations. In response, it formulated standardized definitions intended for broad international use. The definitions cover both linear infrastructure such as cycle tracks, greenways, cycle lanes, sharrows, mixed-traffic roads, cycle streets, contraflow cycling streets, bus‑and‑cycle lanes, and cycle route networks, and non‑linear infrastructure, including cycle crossings, grade‑separated crossings, advanced stop lines, two‑stage turn provisions, cycle parking, and cyclist traffic‑light exemptions. Each definition is accompanied by an explanatory note detailing its source, examples of practical application, and relevant signage based on the 1968 Convention on Road Signs and Signals or national regulations. To support clarity, the paper also includes illustrative images of signs and infrastructure.]]></description>
      <pubDate>Mon, 04 May 2026 11:19:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667222</guid>
    </item>
    <item>
      <title>Traffic facility dynamic optimisation for freeway dense interchanges: generic approach study</title>
      <link>https://trid.trb.org/View/2643310</link>
      <description><![CDATA[To address space constraints in dense freeway interchange sections, this study proposes the Traffic Control Device Selection Model for Traffic Information (TCDSM-TI). It overlays optimised signs on existing information via selected facilities to convey road details effectively. A driving simulation experiment involving 39 drivers evaluated various designs. The results indicate the following: (1) Iterative TCDSM-TI optimisation, statistical validation, and comprehensive assessment are required because of the varied optimisation effects of different sign combinations and sequences. (2) Optimal sign combinations are crucial for enhancing route clarity and driving stability. (3) Sign effectiveness is closely linked to road geometry, with multiple exit warning signs enabling smoother speed adjustments. A 2.5 km exit guide sign should be optimised for continuous guidance, while ground text guides and navigation prompts should improve comfort and stability. This study supports the optimised design and practical application of traffic facilities in dense freeway interchanges, thereby mitigating the negative impacts of complex road conditions on drivers.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643310</guid>
    </item>
    <item>
      <title>A Novel Low-Cost Double U-Net Model for Predicting Traffic Sign Retro-Intensity from Camera Data</title>
      <link>https://trid.trb.org/View/2691791</link>
      <description><![CDATA[Retroreflectivity is essential for the visibility of transportation infrastructure, ensuring road safety, especially under low-light conditions. Traditional methods for measuring retroreflectivity, such as nighttime visual inspections and retroreflectometer measurements, are labor-intensive, subjective, and pose safety risks. With the introduction of lidar technology, traffic sign retroreflectivity can be assessed more efficiently, as lidar-derived reflectivity values demonstrate a strong linear correlation with retroreflectivity. This study leverages a lidar device to propose a Double U-Net framework for predicting pixel-level reflectivity from daytime red, green, blue (RGB) images, providing a localized and accurate prediction. To train the Double U-Net model, a structured data set of over 7,600 images of transportation infrastructure was created, incorporating lidar-derived depth and reflectivity data. Given the sparsity of low-resolution lidar point clouds, linear interpolation was applied to generate pixel-level depth and reflectivity images. The proposed Double U-Net framework employs a two-stage architecture, where depth is predicted from cropped images in the first stage, and then combined with the original image and class embeddings in the second stage to generate pixel-level reflectivity predictions. A weighted loss function balances depth and reflectivity errors, enhancing prediction accuracy and robustness. The model achieved a median mean square error (MSE) of 0.0162 with interpolated data, 0.02233 with raw data, a median structural similarity index measure (SSIM) of 0.5413, and a Mann-Whitney U Test alignment of 58.2% with raw reflectivity data at a 0.001 significance level. The model effectively captures localized defects on traffic signs, providing a more detailed analysis compared with traditional methods.]]></description>
      <pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691791</guid>
    </item>
    <item>
      <title>Evaluating a New Road Sign and Traffic Markings for Motorcycle Safety on Untreated Roads</title>
      <link>https://trid.trb.org/View/2655602</link>
      <description><![CDATA[Objective: This research investigated effects for new traffic markings on the user behaviour of motorcycle riders. Background: Across motorised vehicles, motorcycles represent the most vulnerable road users. Method: A road sign and traffic markings were installed at six trial sites. Data from video cameras at each site provided measures of rider behaviour in relation to speed, road position, brake use, and use of the traffic markings, before and after installations. Throughout this research 4652 motorcycle riders travelled through the sites. Of these 1542 riders were analysed in more detail to investigate the effects of the road safety intervention on rider behaviour. Results: At five sites speed was reduced by a significant margin. At four sites there were significant improvements in road position at the final traffic marking. At five of the trial sites on the apex of a bend, there were significant improvements in road position. Braking behaviour decreased at two of the trial sites. For use of the traffic markings a significant increase was observed across all the trial sites. Across the behaviour measures, the changes were still present 4 weeks later. At a comparison site no changes in behaviour were observed. Conclusion: The findings provide evidence of improved rider behaviour which are placed in reference to the Safe System principles for road safety and casualty reduction. Application: This research has generated international interest for installing the road sign and traffic markings in other regions and contributes to the Scottish Government’s Road Safety Framework to 2030 by reducing motorcycle casualties.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655602</guid>
    </item>
    <item>
      <title>User-Centered Smart Traffic Sign Development Implementation Study</title>
      <link>https://trid.trb.org/View/2684223</link>
      <description><![CDATA[Flaggers maintain traffic flow through a work zone area despite a shutdown of lanes by providing temporary traffic control. In terms of occupational safety, flaggers have one of the highest risk jobs in the country, with 41 out of every 100,000 workers killed on the job each year. This project developed and tested technology for automatically detecting and documenting the occurrence of near-intrusions into a flagger-controlled work zone. The project developed a low-cost portable device for automatically tracking vehicle trajectories, detecting potential intrusions, and providing audio-visual alerts to warn any errant drivers who might cause a danger to flaggers and workers in the construction zone. A radar sensor on the device is deployed by using a telescoping pole and collects simultaneous measurements from approaching vehicles in multiple lanes. Tests were conducted in six real-world traffic scenarios, including work zones at one rural location (Cook County), three urban locations (Saint Paul, White Bear Lake, and Eden Prairie), a synthetic urban zone involving pedestrian crossings (Saint Paul) and one suburban/rural location (Mound). Detailed results and analysis are presented in this report. The results indicate that multiple design iterations have improved the device and enabled it to work reliably – Very few false alarms (if any) are triggered and the intrusion detection curves implemented in the system are verified to work well. The vehicles which were alerted using audio-visual warnings in the last work zone test responded appropriately with a majority of them slowing down in response to the alarms.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:12:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684223</guid>
    </item>
    <item>
      <title>Traffic Sign Management for Local Urban Streets Handbook</title>
      <link>https://trid.trb.org/View/2676798</link>
      <description><![CDATA[n 2010, the Local Road Research Bureau (LRRB) developed the Traffic Sign Maintenance/Management Handbook. The focus of this current project will be to review and update the 2010 handbook, with a specific focus on local urban streets and guidance on using the minimum signage appropriate to the setting. Street signage is a critical part of maintaining a safe roadway, ensuring drivers are fully informed of conditions. However, in a densely developed urban setting with significant pedestrian activity, on-street parking, and frequent driveways, excessive signage can clutter the right-of-way and compromise public safety. The MN Manual on Uniform Traffic Control Devices (MN MUTCD) provides guidance on signage types, numbers, placement, and size. Although a comprehensive resource, its focus is more on higher-speed, limited-access highways than on local streets. It requires interpretation, and there is often no specific guidance for local urban streets.]]></description>
      <pubDate>Fri, 13 Mar 2026 08:45:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676798</guid>
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