<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzcyIiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM%2BPHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8%2BPC9wZXJzaXN0cz48L3NlYXJjaD4%3D" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. 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><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Training Drivers in Advanced Driver Assistance Systems: How Training Content Shapes Driver Knowledge, Decision-Making, and Performance</title><link>http://pubsindex.trb.org/view/2693756</link><description><![CDATA[Driver understanding of Society of Automotive Engineers Level 1 and Level 2 automated driving systems is essential for safe human–automation interaction as these features are increasingly common in modern vehicles. Yet, little is known about how different training contents shape drivers’ knowledge and performance. This study investigates how variations in training content affect drivers’ knowledge, decision-making, driving performance, and subjective evaluations when using advanced driver assistance systems (ADASs), including adaptive cruise control, lane-keeping assist, and highway driving assist. Sixty participants were randomly assigned to one of three training groups: baseline training, driver-issue training, and feedback-based training. Pre- and post-training knowledge tests, simulator-based driving tests, and subjective questionnaires were used to evaluate outcomes. Results showed that feedback-based training significantly enhanced drivers’ knowledge compared with the other groups. Drivers trained with driver-issue content exhibited more stable lateral control. Training content did not meaningfully affect trust or perceived usefulness, although satisfaction differed across groups. These findings demonstrate that different contents of ADAS training influence driver understanding, driver performance, and subjective experience. This work provides guidance for designing future ADAS training programs that could help drivers to have safe driving experience.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693756</guid></item><item><title>Assessing Sweden’s Greenhouse Gas Emissions from Road Maintenance using Environmental Product Declarations and Network Life-Cycle Optimization</title><link>http://pubsindex.trb.org/view/2693755</link><description><![CDATA[To achieve the Paris Agreement’s goal of limiting the average temperature rise to 1.5°C, greenhouse gas emissions must be reduced by 43% from 2010 levels by 2030. This paper quantifies greenhouse gas emissions from road maintenance in Sweden and evaluates reduction pathways by integrating three elements: (1) lifetime estimates of maintenance operations, (2) corresponding emissions estimated from published Environmental Product Declarations, and (3) simulation of a 10-year maintenance plan that maintains current condition distribution at minimum cost across three regions (Stockholm, Skåne, Norrbotten). We assess three scenarios: a 2010 fossil-fuel baseline; a 2024 practice with predominantly biofuel-fired production and higher reclaimed asphalt utilization; and a 2024 extension that additionally replaces 5% of bitumen with a biogenic binder and employs biofuels for transport and machinery. Relative to 2010, the simulated emissions required to maintain current network condition were 28% to 30% lower under 2024 practices and 60% to 61% lower with added biogenic binder. The largest emission reductions arose from switching production heat from fossil to biofuels and from increased reclaimed asphalt. Total life-cycle impacts remained sensitive to treatment longevity, underscoring the need to integrate performance into procurement and to monitor the durability of emerging low-carbon materials. Although Sweden has reduced emissions substantially, achieving the 43% reduction target by 2030 will require measures beyond current practice.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693755</guid></item><item><title>Traffic Volume Estimation Using Deep Learning for High-Resolution Greenhouse Gas Inventory</title><link>http://pubsindex.trb.org/view/2693754</link><description><![CDATA[Traffic volume data are essential for policymakers in traffic management and for constructing a high-resolution greenhouse gas emissions inventory within the road transport sector. However, traffic volume is measured only on select roads, and coverage is limited. This study aims to construct traffic volume estimation models based on traffic speed using machine-learning approaches, including linear regression, random forest, gradient boosting models, a deep neural network (DNN), and a combined long short-term memory and DNN (LSTM-DNN) model. Among these, the LSTM-DNN model demonstrated the best performance, achieving an R² of 0.9404, a mean squared error of 94,331, and a mean absolute error of 199. While performance varied across different road grades—with somewhat higher errors for national highway, special metropolitan city road, and local road—these variations did not substantially affect downstream applications such as CO₂ emissions estimation. Validation using CO₂ emissions calculated from the estimated traffic volumes showed similar levels to those from other institutions, confirming the appropriateness of the traffic volume estimation. Notably, achieving high performance using only traffic speed and road information highlights the significance and the practical potential of this study’s approach for scalable traffic volume estimation.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693754</guid></item><item><title>Evaluation and Design of a Footing Hybrid Connection for Innovative Hollow-Core Fiber-Reinforced Polymer–Concrete–Steel Composite Columns</title><link>http://pubsindex.trb.org/view/2692377</link><description><![CDATA[To support the advancement of accelerated bridge construction in high-seismic regions, this study investigates a novel prefabricated column-to-footing connection designed for improved resiliency, constructability, and cost efficiency. The socket connection utilizes hollow-core fiber-reinforced polymer–concrete–steel (HC-FCS) columns with embedded corrugated steel pipes (CSPs). The composite HC-FCS column consists of a concrete shell sandwiched between an outer fiber-reinforced polymer tube and an inner steel tube. The inner steel tube is embedded into the footing connection of the HC-FCS column. The same authors tested the innovative socket connection on a large HC-FCS column under seismic loads, showing high ductility, strong moment and drift capacities, and promising potential for future design use. Building on previous experimental findings that demonstrated excellent seismic performance, this study employs finite element (FE) modeling in LS-DYNA software to conduct a parametric analysis of 50 large-scale column-to-footing connections. The FE models were used to critically assess the effect of seven parameters on the seismic behavior of such a novel column-to-footing connection. Consequently, design equations based on mechanical analysis of a simplified strut-and-tie model were proposed to determine the essential characteristics of the CSP in HC-FCS column-to-footing connections for practical implementation.]]></description><pubDate>Thu, 16 Apr 2026 09:24:32 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692377</guid></item><item><title>A Simple Yet Efficient K-Nearest Neighbor-Based Method for High-Resolution Traffic Time–Space Diagram Imputation</title><link>http://pubsindex.trb.org/view/2692376</link><description><![CDATA[A time–space diagram (TSD) is an efficient tool for traffic analysis and visualization, representing the macroscopic traffic state as a set of cells. However, its application is often hampered by data sparsity, which obscures high-resolution traffic dynamics. This study proposes a modified K-nearest neighbors method, characterized by an adaptive iterative process, to impute missing TSD data. To support the method’s design, analytical bounds on error propagation motivated by Green’s function-based theory are established, and a practical empirical formula for the optimal K parameter is derived. The framework’s performance was rigorously validated on diverse data sets from China (Ubiquitous Traffic Eyes), the US (Next Generation Simulation), and Germany (HighD) across 30 distinct experimental conditions. Compared against four baseline models, the proposed model demonstrates a compelling balance between high imputation accuracy and exceptional computational efficiency. Further analyses confirm the influence of neighborhood order and the systematic performance bias. The model’s potential for knowledge transfer is also demonstrated via a cross-data set imputation scheme.]]></description><pubDate>Thu, 16 Apr 2026 09:24:32 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692376</guid></item><item><title>Place of Last Drink (POLD) Implementation: Examination of Five Sites Using the POLD Implementation Framework</title><link>http://pubsindex.trb.org/view/2691795</link><description><![CDATA[Alcohol-impaired driving continues to be a significant problem in the United States. In 2022, over 13,500 fatalities were attributed to crashes where at least one driver was alcohol-impaired. Alcohol-impaired drivers were involved in 32% of traffic fatalities in 2022. Alcohol is a serious public health problem related to injury, death, and violence. One factor contributing to impaired driving is overservice at licensed alcohol establishments—research shows that over 80% of bars and restaurants will sell alcohol to someone who appears obviously intoxicated. Place of Last Drink (POLD) is an approach that law enforcement agencies can use to address alcohol-impaired driving incidents by identifying alcohol establishments that overserve alcohol. To implement POLD, a law enforcement officer collects information on the last place someone consumed alcohol before an alcohol-related traffic stop or incident. Authorities can work with an establishment identified as the POLD, require corrective action, or impose sanctions. Only a handful of studies have examined implementation of POLD, but they identify substantial differences in implementation and use of POLD data. To assess its effectiveness, it is essential to understand how POLD is implemented. This study examines case studies from five sites that are implementing POLD using a POLD implementation framework to compare and contrast implementation, identify differences. common strategies, strengths, and areas for improvement to inform and improve future implementation. The study shows there is a need to better understand how POLD is implemented and used in other sites, to develop clearer recommendations for its use.]]></description><pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691795</guid></item><item><title>Analysis of Car-Following Safety Evolution in Mixed-Driving Environment: A Resilience Perspective</title><link>http://pubsindex.trb.org/view/2691793</link><description><![CDATA[As autonomous-driving technology advances, autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to coexist for an extended period. While resilience is widely applied in macro safety analysis, its application in micro-process modeling remains underdeveloped. This study proposes a resilience-based framework comprising three phases (initial, safety decay, safety recovery) to investigate the evolution of driving risk across three car-following scenarios: HV–HV (HV following HV), HV–AV (HV following AV), and AV–HV (AV following HV). The analytical framework consists of three steps: First, car-following pairs with complete risk-evolution processes were identified. Second, cluster analysis was applied to categorize phase-specific patterns. Third, the association rule mining algorithm was used to trace risk-evolution chains, followed by a comprehensive evaluation integrating safety and efficiency. Key findings include: (1) significant and rapid safety decay being accompanied by swift recovery, and the following vehicle exhibiting significant feature differences across recovery patterns; (2) safety decay in HV–AV being slower than that in HV–HV, with HV–AV demonstrating a close and conservative driving strategy during the risk-evolution process, with HV–AV meanwhile performing worse in safety and efficiency compared with HV–HV, highlighting the interaction discordance between HV and AV; (3) excessive deceleration of HV being the primary trigger for safety degradation in AV–HV, and AV demonstrating effective adaptation to safety decay, promoting slight safety reduction with rapid recovery, thereby balancing safety and efficiency. These findings reveal principles of risk evolution in mixed-driving environments, providing a novel analytical framework for mixed-driving safety.]]></description><pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691793</guid></item><item><title>Toward Safer Highways: Data-Driven Approach to Detecting Aggressive Driving Using Connected Vehicle Technologies</title><link>http://pubsindex.trb.org/view/2691792</link><description><![CDATA[Aggressive driving behaviors such as tailgating and cutting off pose serious highway safety risks, especially for trucks. Timely detection of these behaviors can enable real-time interventions (e.g., automated driver warnings or vehicle safety system activation) to prevent crashes. This study presents a machine learning approach to detect tailgating and cut-off events using data from a high-fidelity driving simulator. Forty participants drove a truck in mild and heavy traffic scenarios within a connected vehicle (CV) environment, providing rich data for analysis. We fused four data sources—vehicle kinematics, CV-based metrics, road characteristics, and driver demographics—into five feature combinations to evaluate their predictive power. Four classification models (Artificial Neural Network, Support Vector Machine, Random Forest, and XGBoost) were trained on these feature sets. Performance evaluation across traffic scenarios shows that models leveraging CV data significantly outperform those using only traditional data, achieving high accuracy in identifying aggressive behaviors. Integrating CV features with conventional kinematic data substantially improved tailgating and cutting-off detection, underscoring the promise of CV technology for enhancing highway safety.]]></description><pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691792</guid></item><item><title>A Novel Low-Cost Double U-Net Model for Predicting Traffic Sign Retro-Intensity from Camera Data</title><link>http://pubsindex.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>http://pubsindex.trb.org/view/2691791</guid></item><item><title>Boston Blind Zone Safety Initiative: Current Fleet Analysis, Market Scan, and Proposed Direct Vision Rating Framework</title><link>http://pubsindex.trb.org/view/2692319</link><description><![CDATA[In about one-quarter of low-speed, truck-involved, vulnerable road user fatalities in the U.S., a driver’s direct vision was impaired. A driver has direct vision of an object outside the vehicle when it can be seen without the aid of mirrors or camera displays. Vehicles vary in how near drivers can see outside the vehicle to the front and to the side. This paper reports a first-in-the-U.S. effort with the U.S. Department of Transportation Volpe Center, the Boston Public Health Commission, and the Boston Transportation Department to assess the direct vision for vehicles used for Boston’s Schools, Fire, and Public Works Departments. The research team quantitatively measured direct vision in 21 vehicles using both a manual approach and a camera-based approach. Using these methods, this paper proposes a direct vision rating system that the City of Boston and other fleets can incorporate into procurement. The proposed system includes front and passenger five-star ratings based on the distance at which a child or adult would be visible directly in front of or to the passenger side of the vehicle, calibrated to federally and locally defined intersection geometric standards. A five-star vehicle enables drivers to see children in the crosswalk and children on bicycles in a buffered bicycle lane. In 11 of the 21 vehicles, drivers whose vehicle was stopped at the stop bar before a crosswalk at an intersection could not adequately see a child in the crosswalk in front or an adult on a bicycle on the side.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692319</guid></item><item><title>Framework to Correlate Hamburg Wheel Tracking Test Results for 4- and 6-in. Asphalt Mix Specimens</title><link>http://pubsindex.trb.org/view/2691043</link><description><![CDATA[The study proposes a threshold value for selecting rut-resistant Marshall-designed bituminous mixtures. It also determines the correlation between 4-in. and 6-in. Hamburg Wheel Tracking Test (HWTT) samples. Samples of 4 in. and 4 in. diameter comprising different binder grades (VG-30, VG-40 and PMB-40) with varying air voids (2.5%, 4% and 7%) were tested using HWTT. The study emphasizes the stronger correlation (R = 0.99; R2 = 0.98) between 4-in. and 6-in. samples in HWTT results, providing useful information to pavement engineers. The test result also shows that polymer-modified mixtures have better rutting resistance because of their increased viscosity while higher compactive efforts improve resistance by minimizing air voids. Overall, this study underscores the role of performance testing and specification (rut depth = 6.5 mm at 20,000 passes for the Marshall mixtures) in ensuring the long-term performance and durability of pavements. These findings provide valuable guidance for engineers and researchers aiming to increase the performance of bituminous pavements through effective material selection and design strategies.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691043</guid></item><item><title>Safety Effectiveness of Unsignalized Restricted Crossing U-Turn Intersections in Mississippi using an Empirical Bayes Before–After Study</title><link>http://pubsindex.trb.org/view/2691042</link><description><![CDATA[At-grade intersections on rural high-speed highways can be hazardous, particularly where lower-speed vehicles from minor roads must cross high-speed traffic on major roads. To enhance safety at these junctions, an alternative design known as restricted crossing U-turn (RCUT) has gained considerable interest in recent years. RCUTs can help reduce both the number and severity of crashes by rerouting minor road traffic to downstream U-turns, thereby minimizing crossing conflict points between high-speed and low-speed vehicles. This research aimed to estimate the safety performance following the implementation of eight unsignalized RCUT intersections on rural high-speed highways in Mississippi. The study considered two different definitions of the intersection influence area and employed two distinct methodologies: naïve average annual number of crashes analysis, and a rigorous Empirical Bayes (EB) method. To facilitate the crash modification factor (CMF) development, new local safety performance functions (SPFs) were developed, and the results were compared with those derived from the HSM (Highway Safety Manual) SPFs and state-specific SPFs. Results showed significant crash reductions following RCUT implementation: a 63% reduction in total crashes and a 79% reduction in fatal and injury crashes. Annual angle crashes dropped by over 96%, and left-turn crashes by 40%, although sideswipe and rear-end crashes increased slightly as a result of redirected traffic. Despite methodological differences, all analyses confirmed RCUTs’ effectiveness in improving safety, making them a promising solution for high-speed rural intersections in Mississippi.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691042</guid></item><item><title>Spatiotemporal Decomposition of Urban Traffic Congestion into Recurrent and Incidental Patterns Based on Web Map Data</title><link>http://pubsindex.trb.org/view/2691039</link><description><![CDATA[The acquisition of high-resolution city-scale traffic data is a major challenge for urban studies, as traditional sensors offer sparse coverage and commercial data sources are largely inaccessible. To address this gap, in this paper we present an automated framework to generate the high-resolution spatiotemporal data necessary for decomposing urban congestion into its recurrent and incidental components. The framework operates by systematically extracting quantitative traffic state information from the visual imagery of public web maps using computer vision. Implemented in the megacities of Shenzhen and Hong Kong, this “virtual sensor network” generated a continuous, minute-level spatiotemporal dataset of traffic state across the entire road network. This dataset was then used to perform an in-depth diagnosis of urban congestion using robust principal component analysis. In the analysis, complex traffic dynamics were decomposed into recurrent and incidental patterns, enabling the identification and diagnosis of congestion hotspots based on their intensity and duration. This work provides both a scalable methodology for generating traffic data and an analytical approach to support data-driven decision-making for urban mobility management.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691039</guid></item><item><title>Improving Direct-Tension Testing Reliability and Material Characterization of Non-Proprietary Ultra-High-Performance Concrete</title><link>http://pubsindex.trb.org/view/2691040</link><description><![CDATA[Ultra-high-performance concrete (UHPC) is increasingly used in modern infrastructure because of its outstanding strength and durability, but accurately measuring its tensile behavior remains a significant challenge. Direct-tension testing using AASHTO T 397, particularly with a 220 kip wedge grip machine, has been documented to yield success rates as low as 36%. This paper addresses direct-tension testing challenges by evaluating non-proprietary UHPC mixtures with steel fiber dosages from 0.0% to 2.0% and silica fume contents of 9%, 12%, and 15% of total binder content, all tested at 28 days. Through a two-phase experimental program, this study covers improvements to specimen preparation, machine alignment, and grip pressure. These refinements increased the test success rate to 75% with the same testing equipment. The results show that the amount of fiber is the primary factor influencing tensile strength and post-cracking performance of UHPC, while moderate changes in silica fume content have only a minor effect. This study provides a practical and repeatable test protocol for direct-tension evaluation and demonstrates that optimizing fiber dosage is essential to achieve the desirable tensile behavior of UHPC. The proposed approach enables confident material selection and promotes wider use of UHPC.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691040</guid></item><item><title>Language-Aware Deep Learning for Crash Severity Prediction Modeling in Khmer Traffic Reports</title><link>http://pubsindex.trb.org/view/2691036</link><description><![CDATA[Traffic crashes are a leading cause of death in low- and middle-income countries, where weak infrastructure and limited data hinder effective responses. Predicting crash injury severity is vital for emergency planning and policy; however, most machine learning models rely on English-language data, limiting their use in multilingual, low-resource settings. This is especially problematic for Khmer, Cambodia’s official language, which lacks word boundaries, has complex morphology, and suffers from scarce natural language processing resources. Standard models fail because of poor tokenization, semantic drift, and lack of script-specific representations. To address this, a Khmer-aware deep learning framework is proposed that integrates conditional random field-based tokenization, multigranular embeddings (character, subword, word), a dilated bidirectional long short-term memory with self-attention, and noise-robust classification to manage linguistic complexity and data variability. A labeled dataset of 1,074 Khmer-language traffic reports collected from eight Cambodian news outlets (2015–2024) is also introduced. The model achieves 95.2% accuracy, 0.952 precision, 0.952 recall, and 0.951 macro-F1, outperforming the best traditional model (eXtreme Gradient Boosting: 88.0% accuracy, 0.80 macro-F1) with nearly 60% lower error rate. Results confirm that language-specific design is essential for reliable severity prediction in low-resource languages. Exploratory analysis of media-reported crashes reveals that 40.7% were classified as fatal, 52.1% of fatalities occurred on national roads, and 73.6% involved motorcycle patterns reflective of reporting intensity rather than population-level risk. This work provides a reproducible pipeline to transform vernacular text into public health intelligence. By combining linguistic expertise with deep learning, it is demonstrated that inclusive, language-aware AI can turn local narratives into actionable, life-saving insights, setting a precedent for equitable road safety research in underserved regions.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691036</guid></item></channel></rss>