<?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?subject=Pedestrians+and+Bicyclists" 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>Pulse of the Pedal: Electrocardiogram-Based Assessment of Stress in Urban Bicycling</title><link>http://pubsindex.trb.org/view/2709301</link><description><![CDATA[As awareness of cycling’s multifaceted benefits—spanning health, environmental, economic, and social domains—continues to grow, adoption rates are steadily increasing. Despite these benefits, urban cycling environments pose significant challenges, as cyclists share road space with motor vehicles and pedestrians, thereby increasing the risks of crashes and conflicts. Most existing research has focused on infrastructure aspects; however, few studies have explored physiological dimensions by measuring cycling stress across a ride. This research addresses this gap by using electrocardiogram (ECG) sensors to measure cyclists’ physiological stress levels while accounting for fatigue during cycling an urban route, with heart rate (HR) serving as the primary indicator. Twenty-two participants completed the same urban route twice in different directions while wearing an ECG device. Results from statistical analyses reveal that specific intersections and directional sequences through which cyclists move have a significant influence on their stress levels. Stress tends to increase with the accumulation of physical fatigue. Moreover, stress levels indicated by HR were also elevated at unsignalized intersections with unclear right-of-way, and at major signalized intersections with high traffic volume. These findings demonstrate that considering physiological data provides valuable insights into cycling experiences and can inform transportation planning and intersection design for safer, more comfortable urban cycling experiences.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709301</guid></item><item><title>Evaluating the Safety Impact of Roadway Rightsizing in Jefferson County, Kentucky</title><link>http://pubsindex.trb.org/view/2709128</link><description><![CDATA[This research provides a safety assessment of rightsizing projects that took place in Jefferson County, Kentucky. Rightsizing has become increasingly popular as a solution for multimodal access improvements and enhancing roadway safety. A cross-sectional before–after analysis was applied to a 15-year panel dataset from 2010 to 2024 to estimate the impact of rightsizing on crash frequency. A matched control group was developed using traffic volume and segment length using nearest-neighbor approach. Negative binomial safety performance functions were estimated with untreated sites and adjusted with annual calibration factors for seasonal changes consideration. Empirical Bayes methods were applied to correct for regression-to-the-mean bias and estimate counterfactual crash frequencies. Crash modification factors (CMFs) were calculated and disaggregated by crash type (all, bicycle, pedestrian, and intersection-related) and severity level (KA, BC, O). The analysis reveals that rightsizing treatments were associated with a 32% reduction in fatal and severe injury crashes, and consistent crash reductions at intersections. However, elevated CMFs across all severity levels for bicycle crashes suggest increased risk, potentially because of higher exposure without corresponding protective infrastructure. Pedestrian findings varied by severity level. The findings highlight crash severity reduction potential for rightsizing while indicating a requirement for including facilitative infrastructure for protection of vulnerable road users. The study includes practical recommendations for transportation agencies considering rightsizing as part of a broader safety and multimodal mobility initiative.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709128</guid></item><item><title>Examining the Role of the Built Environment in Cycling Injury Severity: Older Adults (60+) Versus Individuals Aged 10 to 59 in a Super-Aged City</title><link>http://pubsindex.trb.org/view/2706341</link><description><![CDATA[Cycling offers well-documented benefits, including reduced congestion and air pollution, enhanced mobility, and improved physical health. Reflecting these advantages, cycling participation has increased across all age groups in many developed countries. However, this growth has been accompanied by a rise in cycling crashes, raising significant urban safety concerns—particularly for older adults. Although numerous studies have investigated factors influencing the injury severity of cycling crashes, the built environment has consistently emerged as a key determinant. Nevertheless, limited research has specifically explored how micro-level built-environment characteristics are associated with the injury severity of bicycle crashes, especially among older adults. This study investigates the association between built-environment characteristics and the injury severity of bicycle crashes involving older adults, analyzing 10,502 crash cases in Seoul from 2018 to 2023 using a binomial logistic regression model. To capture detailed built-environment attributes, we applied DeepLabV3+ for semantic segmentation of Google Street View images collected from four directions at each crash location. The results indicated that higher proportions of road surfaces, obstacles, and vegetation were associated with increased injury severity among older adults (60+), whereas the presence of traffic devices reduced injury severity. Among individuals aged 10 to 59, greater building density was linked to lower injury severity. A common risk factor across both age groups was collisions with motor vehicles. These findings underscore the necessity of age-sensitive safety interventions. For older adults in particular, measures such as separating cycling paths from obstacles and increasing the installation of traffic control devices may help improve cycling safety.]]></description><pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706341</guid></item><item><title>Evaluating Pedestrian and Cyclist Friendliness in Urban Neighborhoods Using an Active Travel Index System: Application in 52 Chinese City Sub-Districts</title><link>http://pubsindex.trb.org/view/2706120</link><description><![CDATA[Active transportation plays a critical role in promoting sustainable, healthy, and equitable urban mobility, yet comprehensive tools for evaluating pedestrian and cyclist environments remain limited. This study develops an Active Travel Index to assess the walkability and cyclability of urban neighborhoods by integrating objective infrastructure measures with subjective user perceptions. To determine key indicators of the Active Travel Index, we analyzed multi-source data collected from four districts in three Chinese cities. The data included a customized geographic information system database of sidewalks and bicycle lanes of the five urban districts, panoramic video-based streetscape mapping covering 697 km road segments and 86 intersections, and 2,520 user surveys. This analysis resulted in an Active Travel Index framework composed of 13 indicators across three dimensions: network completeness, facility quality, and active travel vitality. Indicators reflect conditions such as path density, surface quality, crossing convenience, ride disturbances, accessibility to destinations, travel mode share, and user satisfaction. Indicator weights were determined through the analytic hierarchy process with expert input. In the application, the Active Travel Index was used to produce ranked evaluations of 52 subdistricts of the three cities, highlighting specific deficiencies and guiding policy recommendations. Results revealed substantial intra- and inter-city disparities in active travel infrastructure, particularly in surface continuity, vehicle intrusions, and design consistency. This index offers a replicable tool for diagnosing urban travel environments and informing infrastructure investments. By bridging technical assessments with user experiences, the Active Travel Index supports more inclusive and data-driven decision-making in transportation planning and performance monitoring.]]></description><pubDate>Wed, 27 May 2026 13:06:57 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706120</guid></item><item><title>Multimodal Signal Control in Coordinated and Free Transit Priority Corridor: SmartPGH Case Study on Pedestrian Timing Strategies</title><link>http://pubsindex.trb.org/view/2706172</link><description><![CDATA[This study supports the City of Pittsburgh’s SmartPGH initiative by evaluating advanced pedestrian signal strategies designed to enhance safety and multimodal mobility on high-demand urban corridors. The objective is to identify the best trade-offs between pedestrian timing treatments and signal control modes (actuated free versus coordinated) for varying pedestrian and vehicular demand throughout the day. While prior research has implemented clearance extension logic in simple midblock environments, this study advances the field by applying and testing pedestrian strategies in complex, multiphase intersections using real-world controller logic and time-of-day profiles. The evaluated strategies include pedestrian protection logic (PPL), passive pedestrian detection (PPD), dynamic pedestrian-exclusive phases (DEP), and pedestrian recall (PR). A novel contribution is the implementation of PPL within a fully actuated controller, which selectively delays conflicting vehicle phases to protect pedestrians still crossing, without interrupting concurrent vehicle movements. Evaluation is conducted through a software-in-the-loop simulation framework that integrates microsimulation with Maxtime signal controllers. Results demonstrate that PPD scenarios consistently offer the lowest person delay across a.m., midday, and p.m. periods while maintaining strong vehicle and bus performance. The addition of PPL and DEP further improves pedestrian service, particularly under high pedestrian volumes, with moderate trade-offs in vehicle delay. These findings provide actionable insights for transportation agencies seeking to implement intelligent pedestrian signal strategies as part of broader smart city goals to promote safety and efficiency.]]></description><pubDate>Wed, 27 May 2026 10:48:02 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706172</guid></item><item><title>Investigating Nonmotorist Crash Exposure at Highway–Rail Grade Crossings Using Artificial Intelligence-Based Object Detection and Generalized Linear Count Models</title><link>http://pubsindex.trb.org/view/2703807</link><description><![CDATA[A critical aspect of crash prediction models for highway–rail grade crossings (HRGCs) is crash exposure, which is a measure of train and highway traffic. Although data on motor vehicle traffic (e.g., annual average daily traffic) and train traffic at HRGCs are invariably available, nonmotorist traffic data at HRGCs are not readily available. Current Federal Railroad Administration and other HRGC crash models focus on train and motor vehicle traffic, overlooking nonmotorized traffic. Therefore, there is a need to gather nonmotorized traffic data to improve HRGC crash prediction models. To address this gap, nonmotorist traffic video data were recorded in this study at various urban and suburban HRGCs in Nebraska, followed by the application of an artificial intelligence-based You Only Look Once (version 8) algorithm for automated nonmotorist traffic volume detection. Data on HRGC characteristics, including surrounding area population density and land use, were collected to create a comprehensive HRGC safety database for nonmotorists. Three negative binomial models were estimated to analyze pedestrian, bicyclist, and combined nonmotorist exposure in relation to daily volumes, utilizing physical, dynamic, and temporal characteristics of HRGCs. Results indicated that sidewalks, greater visibility, and cloudy weather conditions were associated with increased nonmotorist traffic volume. Conversely, higher vehicular traffic levels, wet road conditions, low population density, and more traffic lanes correlated with lower nonmotorist traffic. This study established an initial framework for nonmotorist traffic monitoring and identified key environmental and technical challenges in automated detection at HRGCs; based on these findings, recommendations for addressing technical limitations were provided for future research.]]></description><pubDate>Tue, 19 May 2026 09:02:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2703807</guid></item><item><title>A Comprehensive Review of Vehicle–Pedestrian Interactions: Crash Analysis and Conflict Assessment Approaches</title><link>http://pubsindex.trb.org/view/2701383</link><description><![CDATA[Pedestrian–vehicle collisions remain a critical issue in transportation safety, contributing disproportionately to global traffic-related fatalities and injuries. Unlike vehicle occupants, pedestrians lack physical protection and are therefore more susceptible to severe or fatal outcomes when involved in crashes. Understanding the mechanisms of vehicle–pedestrian interactions and contributing factors to pedestrian crashes is essential for uncovering the crash nature and informing the development of effective safety countermeasures and technologies. This review synthesizes recent advancements in the study of pedestrian safety research at intersections, covering historical crash data–based modeling and analysis, as well as conflict-based studies using field observations and simulation. Key topics include the application of statistical and machine-learning models in crash likelihood and severity analysis, the use of surrogate safety measures, and the integration of conflict analysis frameworks such as extreme value theory. Critical challenges related to pedestrian safety modeling methodologies and evaluation metrics, the evolving safety implications in connected and automated vehicle environments, and the practical applications of these insights for policy and infrastructure design are discussed in depth. By reviewing methodological innovations and highlighting emerging research directions, this paper offers a comprehensive foundation for advancing pedestrian safety research and guiding the development of data-driven, context-sensitive policy, operation, and infrastructure solutions.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701383</guid></item><item><title>Multi-Pedestrian Tracking Based on Improved YOLOv8 and OC-SORT</title><link>http://pubsindex.trb.org/view/2701297</link><description><![CDATA[Multi-pedestrian tracking is an important task for the environment perception systems of autonomous vehicles. In the multi-pedestrian tracking task, mutual occlusion, posture changes, small size, and poor lighting conditions usually pose challenges. To overcome these problems, we propose a detection-based multi-pedestrian tracking method, that is, combining the improved You Only Look Once (YOLO) v8 object detection algorithm with the improved observation-centric simple online and real-time tracking (OC-SORT) algorithm. Specifically, first, we improve the YOLOv8 pedestrian detector by constructing a C2f-Clo block, introducing an explicit visual center block, and designing a lightweight shared convolutional detection head. Second, we improve the OC-SORT tracker using a height-modified intersection over union. Results of experiments on the MOT17 and MOT20 pedestrian tracking datasets show that our method achieves 7.1% and 6.5% HOTA boosts, 8.5% and 8.8% MOTA improvements, 5.5% and 6.1% MOTP increases, 5.2% and 6.7% IDF1 boosts, and 648 and 692 IDSW decreases, respectively, compared with the baseline.]]></description><pubDate>Wed, 13 May 2026 17:00:16 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701297</guid></item><item><title>Signal Control Optimization Method Considering the Effect of Pedestrian Crossing Violation</title><link>http://pubsindex.trb.org/view/2701228</link><description><![CDATA[The continuous increase in traffic organization refinement requirements is prompting signal control for pedestrian friendliness to receive more attention. Under signal control, pedestrian waiting tolerance time significantly affects signal performance. This study examines the effect of pedestrian crossing on vehicle operations during passing phases and proposes a signal control optimization method considering pedestrian crossing violations. This study establishes a crossing intention model based on pedestrian waiting tolerance time, calculates the number of crossing violations, proposes a method to calculate vehicle delays caused by crossing violations, and optimizes pedestrian crossing time allocation in the passing phase. A signal timing decision model considering pedestrian violations was established. The model includes vehicle delay signal optimization (VDSO), pedestrian violation-based signal optimization (PVSO), and coordinated pedestrian and vehicle signal optimization (CPVSO), with three submodels addressing different optimization objectives. These models were solved using the enhanced Fox optimization algorithm, with simulation experiments using real data. The results show that the CPVSO strategy reduces vehicle delays by 35% and pedestrian violations by 64% compared with traditional fixed signal timing. Sensitivity analysis for parameters, including pedestrian and vehicle flow, determined the main application ranges of the CPVSO, VDSO, and PVSO. This study reduces the probability of pedestrian violations during the red light period by optimizing combined pedestrian and vehicle phases while balancing vehicle passage efficiency, thereby decreasing pedestrian–vehicle collisions caused by violations.]]></description><pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701228</guid></item><item><title>Examining Risk Factors Influencing Fatal Pedestrian Crashes on Rural Highways: Matched Case-Control Study Design</title><link>http://pubsindex.trb.org/view/2701225</link><description><![CDATA[Pedestrian fatalities on high-speed rural roads are a growing concern in low- and middle-income countries (LMICs), where infrastructure often prioritizes vehicular mobility over pedestrian safety. This study investigates the effects of factors contributing to fatal pedestrian crashes on high-speed rural roads in India using a matched case-control (C-C) study design. Six high-speed road stretches were selected, and data related to crashes, road geometry, built environment, pedestrian exposure, and traffic characteristics were collected. Four models using conditional logistic regression on four datasets with varying matching ratios (1:1 to 1:4) and one model using binary logistic regression on unmatched data were developed to estimate odds ratios for potential risk factors. Key predictors of fatal pedestrian crashes included the presence of junctions, authorized median gaps, service roads, bus stops, schools, roadside eateries, canals/bridges/culverts, petrol pumps, and segments with high pedestrian population exposure. Model 4 (1:4 matched) was the best fit, demonstrating that matching improved estimate precision and controls for confounding. Segments with medians showed protective effects, while the unauthorized median gap produced a counterintuitive result. This study highlights the impact of geometric and environmental factors on pedestrian safety in the rural areas of LMICs. It also demonstrates the feasibility and effectiveness of the matched C-C design in data-constrained settings. The findings provide valuable insights to guide targeted interventions, including infrastructural enhancements, speed management, and strategic planning of bus stop locations and pedestrian crossing facilities within rural settlements along highways.]]></description><pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701225</guid></item><item><title>Analysis of Elementary School Children’s Street Crossing Safety Behavior Based on Random Parameters Ordered Logit Model</title><link>http://pubsindex.trb.org/view/2701111</link><description><![CDATA[In the context of rapid urbanization and increasingly diverse modes of transportation, urban traffic safety issues are becoming more pronounced. Elementary school children, as a vulnerable group in traffic environments, face significant challenges in ensuring safe street crossings. Therefore, it is crucial to conduct an in-depth exploration of the potential threats and hazards associated with their daily street crossing behaviors to mitigate road safety risks. This study carried out a field investigation at signalized intersections, aiming to thoroughly analyze the intrinsic connection between elementary school children’s street crossing behaviors and the severity of traffic conflicts. The random-parameter ordered logit model is used to analyze the data. The results show that intersection characteristics, unsafe behaviors, and the attributes of the road crossers significantly influence the severity of traffic conflicts. Specifically, shorter green light durations, shorter signal cycle lengths, and smaller crosswalk dimensions exacerbate conflict severity. While restricting right-turn signals can reduce moderate and minor conflicts to some extent, it may lead to more severe conflicts. Furthermore, unsafe behaviors such as running red lights and rushing across streets significantly increase the risk of severe conflicts, especially when children cross alone. Notably, the law-abiding behaviors of adults also affect the severity of conflicts. To enhance street crossing safety for elementary school children, it is recommended to extend green light durations, appropriately adjust crosswalk dimensions, and strengthen traffic safety education. Collaboration between parents and schools is essential to promote traffic safety education for children through example and supervision.]]></description><pubDate>Mon, 11 May 2026 08:51:42 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701111</guid></item><item><title>Integrating Interdependencies of Demand for Public Transportation, Shared Micro-Mobility, and Land Use Within a System of Equations Modeling Framework</title><link>http://pubsindex.trb.org/view/2698382</link><description><![CDATA[Traditional and emerging transportation services in the form of public transportation and shared micro-mobility services, respectively, along with land use are typically hinted at as significant determinants of sustainable urban planning. This study evaluates the dynamic interrelationships between the demand for public transportation, shared micro-mobility services, and land use characteristics in a car-centric urban environment using a seemingly unrelated regression and two-stage least squares modeling approach. Beyond empirical findings, the study aims to develop a system-of-equations-framework enabling the estimation of interdependent urban mobility components. By analyzing data at the ZIP code level, the study assesses the extent to which factors such as distance to urban centers, road length, types of land use, the ridership of public transportation, and use of shared bikes affect population density. The results indicate that distance to urban center is negatively influenced by building density. While public transportation ridership is positively associated with building density and service frequency—highlighting the importance of accessible and frequent public transportation in dense areas—shared micromobility usage is found to be lower in such settings. This suggests that shared micromobility plays a more complementary role to public transportation in lower-density areas, where it can help bridge access gaps and extend the reach of fixed-route services. Moreover, the analysis on elasticities shows that road infrastructure influences the impacts of urban sprawl. The provided insights on the dynamics of urban mobility and land use can inform policymakers, highlighting the need of integrating transportation and land use planning on advocating sustainable urban mobility.]]></description><pubDate>Tue, 05 May 2026 10:16:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2698382</guid></item><item><title>Does Light Influence Route-Choice Behavior? Discrete-Choice Modeling Study</title><link>http://pubsindex.trb.org/view/2694544</link><description><![CDATA[Crowd management attempts to guide pedestrian crowds effectively and efficiently. Static crowd-management measures, such as fences, are often used to guide the crowd. Another way to steer pedestrian walking behavior is through “nudging,” that is, gently coaxing people into the “preferred” direction, for instance, by lighting conditions. This paper examines the impact of light intensity (brightness) and color on pedestrian route-choice behavior using data from a virtual reality (VR) experiment. The study develops two types of discrete-choice models—a panel mixed logit model and a latent class choice model—featuring the route-choice behavior of pedestrians under varying lighting conditions in a virtual maze in a controlled virtual reality experiment. We found that pedestrians avoided red and dark corridors and chose green and blue corridors. On average, the green light most effectively “pulled” people toward a specific route. In addition, this study uncovered three segments in the population: (1) light-sensitive individuals, (2) darkness-avoiding individuals, and (3) individuals with a severe right-handed bias. We found that the impact of color and brightness levels on route-choice behavior differed greatly across segments.]]></description><pubDate>Thu, 23 Apr 2026 09:10:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2694544</guid></item><item><title>From Perception to Prediction: Modeling Pedestrian Satisfaction Using Multilevel Statistical and Sensitivity Methods</title><link>http://pubsindex.trb.org/view/2693758</link><description><![CDATA[This study presents an integrated modeling approach to evaluate pedestrian satisfaction in new urban cities characterized by rapid growth and limited multimodal connectivity. A structured questionnaire, distributed to stratified participants across residential, administrative, and service zones, captured user perceptions of 13 key urban design features, including safety, accessibility, visual coherence, and economic vibrancy. Descriptive statistics and visual analytics revealed that accessibility, protection from crime and traffic, and urban aesthetics were strong correlates of satisfaction. To model these relationships quantitatively, the study employed both ordinal and multinomial logistic regression, with the latter achieving 92.45% classification accuracy. K-means clustering and principal component analysis further uncovered latent user typologies, highlighting the heterogeneity of pedestrian priorities. Local and global sensitivity analyses, including mutual information metrics, identified easy access, protection from traffic, and crime prevention as the most influential features. Response surface modeling illustrated nonlinear interactions among key variables, emphasizing the multidimensional and synergistic nature of satisfaction outcomes. The findings showed that pedestrian experience is shaped not by isolated design features, but by their interactive effects across spatial, psychological, and infrastructural domains. The study offers actionable insights for human-centered urban design, while the presented analytical framework is scalable and supports evidence-based interventions in emerging urban contexts.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693758</guid></item><item><title>Modeling and Transferability of Pedal Cycle Volumes Using Ground Truth, Sensor, Weather, and Crowdsourced Data</title><link>http://pubsindex.trb.org/view/2692321</link><description><![CDATA[Understanding active transportation is critical for transportation planning, infrastructure development, and safety improvements. Unlike motor vehicles, which have widespread automated counting stations, cycling and walking automated counting has limited coverage. Given the limited data and unique characteristics of active transportation, it is crucial to evaluate the accuracy of counting technologies and account for temporal variations, weather effects, and transferability when estimating volumes. Data from four sites in Wisconsin were analyzed with 5 years of hourly sensor, weather, and Strava data, along with 268 h of manually processed ground truth video data. Ground truth hourly count trends showed that pedal cycles were the main users in the shared paths (78%–87%). There were peak and directional hourly trends by week or weekend days, higher volumes and a shift in the type of user were observed on weekends. Automatic sensor count data accuracy from inductive loop and infrared sensors was evaluated and compared with ground truth data. Inductive loop counting technology showed high levels of pedal cycle count accuracy (91%–92%). Infrared sensors counted passersby with a reduced degree of accuracy (54%–67%). Negative binomial regression modeling was implemented to account for overdispersion in the count data. Key predictors included time of day, day of the week, month, temperature, precipitation, and Strava counts. Site-specific models were developed, transferability across sites was assessed, and models were generalized with data from sites that shared similar characteristics applicable to high-volume, urban commuting and recreational paths. Models were not transferable to isolated sites with low volume and unreliable sensor count data.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692321</guid></item></channel></rss>