<?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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc2IiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%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>Exploring the Potential of Remote Sensing and Machine Learning for Scalable Sidewalk Condition Assessment</title><link>http://pubsindex.trb.org/view/2726634</link><description><![CDATA[Sidewalk condition plays a critical role in ensuring pedestrian safety, accessibility, and compliance with regulatory standards. Conventional assessment methods typically involve manual inspections using categorical ratings, which are labor-intensive, subjective, and limited in spatial coverage. This study evaluates the use of satellite imagery and machine learning to support sidewalk condition assessments. A classification model was developed using synthetic aperture radar (SAR) imagery combined with sidewalk physical attributes, including width, slope, and material type. A random forest classifier was trained to predict four condition categories: good, fair, poor, and severe. To address the substantial imbalance in the distribution of classes, a binary formulation was also tested by grouping segments into defective and nondefective classes. Data resampling techniques combining under- and oversampling were applied to improve model performance. The results indicated that the binary model with combined sampling achieved the best performance, with a recall of 0.85 and G-mean of 0.81. Models trained on the original four classes showed lower performance owing to underrepresentation of the poor and severe categories. Feature-importance analysis highlighted SAR amplitude as the most influential predictor across all scenarios. The findings demonstrated the potential of SAR imagery to support scalable and data-driven evaluation of sidewalk conditions. This approach offers a viable complement to traditional inspection methods by enabling targeted resource allocation and broader spatial coverage in pedestrian infrastructure management.]]></description><pubDate>Mon, 13 Jul 2026 17:05:41 GMT</pubDate><guid>http://pubsindex.trb.org/view/2726634</guid></item><item><title>Machine-Learning-Based Traffic State Prediction in Car–Bicycle Mixed Traffic Using Synthetic Data</title><link>http://pubsindex.trb.org/view/2724773</link><description><![CDATA[This study explores the use of machine learning models to predict traffic conditions in mixed car–bicycle traffic environments. A synthetic dataset was developed from numerical evaluations of traffic flow theory, capturing a wide range of multimodal traffic scenarios. Random forest (RF), multi-layer perceptron (MLP), and linear regression models were trained to estimate key traffic metrics, including output flow, delay, and density. The analysis focuses on model performance under different data splits, especially when sorting by variables such as initial car flow and bicycle flow. Results show that, while RF performs well for previously observed traffic conditions, MLP offers stronger generalization to unseen traffic conditions, particularly in high-flow and high-density regimes. However, prediction performance varies depending on the input variable used for sorting and the distribution of training data. These findings underscore the importance of balanced, diverse datasets and support the use of data-driven models for traffic state estimation in multimodal urban networks.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724773</guid></item><item><title>NCHRP Research Report 1157 and NCHRP Web-Only Document 430: Into the Spotlight: Creating a Safe System for Pedestrians in Darkness</title><link>http://pubsindex.trb.org/view/2709548</link><description><![CDATA[National Cooperative Highway Research Program (NCHRP) Project 17-97, “Strategies to Improve Pedestrian Safety at Night" investigated factors associated with pedestrian risk at night and developed NCHRP Research Report 1157: Strategies to Improve Pedestrian Safety at Night: A Guide. This report focuses on the Safe Speeds and Safe Roadways pillars of the Safe System Approach and provides information and strategies related to safe road users, safe vehicles, and post-crash care. The supporting NCHRP Web-Only Document 430 provides information about the research behind the guidance. This article summarizes key findings from the research and discusses: (1) factors affecting pedestrian risk in darkness, such as, higher roadway speeds, roadway design, vision and visibility, and nighttime behaviors; and (2) applying the Safe System Approach to increase pedestrian safety at night centered around managing motorist speed and redressing underinvestment in underserved communities. The article concludes with recommended actions for local, regional, and state agencies to take now to improve pedestrian safety at night.]]></description><pubDate>Wed, 01 Jul 2026 15:07:13 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709548</guid></item><item><title>Developing a Toolkit to Institutionalize the Safe System Approach</title><link>http://pubsindex.trb.org/view/2717377</link><description><![CDATA[This research examines how transportation agencies can move beyond initial phases of Safe System Approach (SSA) implementation toward its institutionalization in planning and programming, embedding safety as a sustained organizational priority rather than a project-by-project initiative. While many agencies have adopted SSA principles in name, the research identifies persistent gaps in practice: siloed responsibilities, limited integration into budgeting and design standards, and reactive, behavior-change focused safety strategies. The study responds by advancing actionable pathways to institutionalize safety in planning and programming across agency systems, decision-making processes, and everyday operations.]]></description><pubDate>Tue, 30 Jun 2026 10:54:44 GMT</pubDate><guid>http://pubsindex.trb.org/view/2717377</guid></item><item><title>Toolkit for Institutionalizing the Safe System Approach</title><link>http://pubsindex.trb.org/view/2717376</link><description><![CDATA[This report provides a toolkit for transportation and planning agencies with strategies to institutionalize the Safe System Approach (SSA) throughout planning and programming processes. Institutionalizing SSA focuses on system-wide opportunities for injury prevention that can offer broader impact than siloed or isolated safety efforts. The toolkit includes 30 unique tools organized into four themes. The toolkit is envisioned as a menu of options to meet agencies where they are in terms of their needs and readiness. Different strategies will be more appropriate in certain jurisdictions than others, and some tools are interconnected, designed to reinforce one another and create greater impact when used in combination. The toolkit should be of particular use to state, regional, and local agencies actively working to eliminate traffic-related serious injuries and fatalities.]]></description><pubDate>Tue, 30 Jun 2026 10:54:44 GMT</pubDate><guid>http://pubsindex.trb.org/view/2717376</guid></item><item><title>Pedestrian Signing at Uncontrolled Crosswalks</title><link>http://pubsindex.trb.org/view/2719395</link><description><![CDATA[Currently, the Manual on Uniform Traffic Control Devices for Streets and Highways recommends that the type of sign used at a pedestrian crosswalk be a warning sign instead of a regulatory sign. The same sign—a pedestrian crossing (W11-2) warning sign—can be used in advance of a pedestrian crosswalk. This article describes an FHWA project that aimed to develop and evaluate alternatives to the W11-2 sign for use at crosswalks. The research team investigated regulatory sign alternatives through human factors testing using a computer-based test (CBT) and in-field evaluations by observing driver yielding. The CBT showed that regulatory sign alternatives that included explicit commands (e.g., “Yield To”) were preferred and understood better than warning sign versions. Based on the CBT results and discussions with the project stakeholders, two signs were selected for the field test to compare with a base condition of the typical W11-2 sign. The field test signs included either the stop symbol or the stop word along with the word “for” and a walking pedestrian symbol within crosswalk lines. The sign shape was rectangular with a white background. The findings from field studies revealed similar driver yielding for the three signs tested. Stated in another manner, the test signs (regulatory with black text on a white background with a rectangular shape) developed in this research and the sign currently used at pedestrian crossings (warning with black text on a yellow background with a diamond shape) had a similar impact on a driver’s decision to yield or not yield to a crossing pedestrian.]]></description><pubDate>Mon, 29 Jun 2026 09:20:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2719395</guid></item><item><title>Factors Influencing Road User Behaviors and Motivations Around Pedestrian Hybrid Beacons and Rectangular Rapid Flashing Beacons in North Carolina</title><link>http://pubsindex.trb.org/view/2719393</link><description><![CDATA[The safety and operational effectiveness of pedestrian hybrid beacons (PHBs) and rectangular rapid flashing beacons (RRFBs) are well established. However, their performance depends on pedestrians actuating these traffic control devices before crossing. Past research has mostly evaluated drivers yielding to these devices using staged crossing protocols. Further research studying how real-world pedestrians and drivers use these devices is needed. We use field video footage data to investigate factors linked to road user behaviors at pedestrian crossings and a survey of pedestrian attitudes and motivations at urban locations in North Carolina. Among other findings, we found evidence of a link between pedestrian refuges and increased actuation and yield rates, whereas pedestrian and driver behaviors worsen at crossings where a sidewalk is absent on one side. We found higher odds of yielding for PHBs, while pedestrians were less likely to actuate those devices compared with RRFBs. In general, factors such as increased traffic and longer crossing distances were associated with more actuations. Survey responses indicated that conditions and roadway elements that increase friction or safety risk during the crossing (heavy traffic, fast cars, and longer crossing distances) motivate pedestrians to actuate the devices more frequently. A comparison of pedestrian waiting times showed that pedestrians experienced 52.0% shorter wait times at actuated RRFBs compared with actuated PHBs, a finding that might help explain the higher rates of actuation at RRFB sites.]]></description><pubDate>Fri, 26 Jun 2026 08:40:59 GMT</pubDate><guid>http://pubsindex.trb.org/view/2719393</guid></item><item><title>Unveiling Hidden Risks: Using Exposure Data to Reassess Bicyclist Safety on High-Injury Corridors</title><link>http://pubsindex.trb.org/view/2717088</link><description><![CDATA[As cities work to develop safer, more connected multimodal transportation networks, prioritizing bicyclist safety has become increasingly critical. Transportation agencies traditionally focus their bicycling safety resources on corridors with high crash numbers. However, this method may overlook streets with fewer crashes but higher risk per bicyclist. Exposure-based safety metrics consider the relative level of bicycling and might offer a more comprehensive understanding of safety. Although standard practice for vehicle road safety work, few cities collect the bicyclist count data needed for similar assessments. This research compared the safety of two corridors: one identified by a hot-spot analysis with a high number of bicyclist–vehicle crashes; and a major arterial in the city’s High Injury Network (HIN), but one that is seemingly safer for bicyclists given the relative scarcity of bicyclist–vehicle crashes. Findings suggest that the HIN corridor, despite fewer bicyclist–vehicle crashes, presented a significantly higher relative risk to bicyclists—when accounting for bicyclist exposure—than the corridor identified via hot-spot analysis. We then examined routes parallel to the HIN corridor to gauge whether bicyclists were avoiding such streets and if collecting additional exposure data on the adjacent streets further enhanced our understanding of safety. Results suggest that bicyclists were avoiding the HIN corridor and that it may be more dangerous than crash-count-focused analyses would reveal. This research underscores the importance of expanding data collection efforts to include exposure metrics for bicyclists, to enhance safety evaluations, guide infrastructural improvements, and support public health by promoting safer, active transportation.]]></description><pubDate>Wed, 24 Jun 2026 10:29:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2717088</guid></item><item><title>CueTrack: Weak-Cue-Enhanced and Consistency-Aligned Framework for Robust Multi-Pedestrian Tracking</title><link>http://pubsindex.trb.org/view/2711992</link><description><![CDATA[The rapid advancement of urbanization and the growing demand for public safety present a strong impetus for multi-pedestrian tracking in surveillance systems. However, multi-pedestrian tracking still encounters several critical challenges: (1) the complexity of occluded or densely packed targets; (2) for targets exhibiting significant foreground–background contrast and subtle appearance features under occlusion, detection performance still encounters substantial challenges; and (3) when targets are occluded or crowded, there is often a high degree of overlap between objects, leading to the degradation of both spatial and appearance features, which increases the difficulty of maintaining identity consistency across frames. To cope with these challenges, we propose an enhanced tracking-by-detection framework, CueTrack, which integrates a novel detection module with a linear deformable convolution (LDConv) and high-resolution detection layer, together termed FlexDet, and introduces a confidence-based modeling strategy for more robust target association. In particular, unlike existing methods that rely solely on spatial or visual cues, our confidence-based approach adaptively compensates for the blurriness caused by frequent occlusion and crowded scenes. Extensive experiments conducted on the challenging MOT17 and MOT20 datasets have demonstrated the effectiveness of the proposed CueTrack, achieving 80.5 multi-object tracking accuracy (MOTA), 81.4 Identification F1-score (IDF1), and 65.2 higher-order tracking accuracy (HOTA) on the MOT17 dataset. This not only validates its superiority in detection accuracy and identity association, but also highlights its potential for real-world applications.]]></description><pubDate>Thu, 11 Jun 2026 09:16:26 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711992</guid></item><item><title>Understanding Distraction Types: Comparative Analysis of In-Vehicle and External Driver Distractions in Pedestrian Fatal Crashes</title><link>http://pubsindex.trb.org/view/2712067</link><description><![CDATA[Distracted driving remains a significant public safety concern, contributing to numerous severe injuries and fatalities in the world. This study analyzes 8 years of Fatality Analysis Reporting System data (2016 to 2023) from the United States using association rule mining to identify patterns associated with various types of driver distractions, specifically distinguishing between external and in-vehicle distractions. The findings indicated that in-vehicle distractions were predominantly associated with young drivers, drug and alcohol use, daylight conditions, arterial roads, high-speed limits, sport utility vehicles, and vehicles of recent model years (2019 to 2024). In contrast, external distractions were more commonly linked to middle-aged and senior drivers, local roads, lower speed limits, dark and unlit conditions, trucks, vans, buses, and vehicles from earlier model years (1980 to 1995). The study recommends strengthening primary enforcement of distracted-driving laws. Since several distracted-driving laws target in-vehicle distractions such as the use of handheld electronic devices, it is imperative to promote advanced driver-assistance technologies in newer vehicles that can mitigate external distractions in addition to in-vehicle distractions. Additional measures include improving signage, fencing, roadside assistance, and visibility in low-light areas, alongside implementing age-targeted educational campaigns to address distraction risks across different driver groups.]]></description><pubDate>Tue, 09 Jun 2026 14:35:55 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712067</guid></item><item><title>Developing Combined Crash Modification Factors: Challenges, Lessons Learned, and Recommendations</title><link>http://pubsindex.trb.org/view/2712015</link><description><![CDATA[Combining crash modification factors (CMFs) often involves merging multiple CMFs to establish a unified CMF for either the same safety treatment or the overall effect of implementing multiple safety treatments simultaneously. This paper reports the combined CMF results for five safety countermeasures: change signal phasing, convert intersection to roundabout, install bicycle lane, change shoulder width, and change posted speed. More importantly, this paper discusses four challenges from the effort to create combined CMFs from the CMFs available in the CMF Clearinghouse that are related to CMF applicability, CMFs of different magnitudes with some showing a safety improvement and some showing non-improvement, the need of information beyond the CMF Clearinghouse, and issues with multiple CMFs from the same study. Based on the lessons learned, the paper also provides suggestions to mitigate these challenges in future research efforts to create combined CMFs, as well as recommendations to researchers who develop CMFs so that key information is reported and made available to facilitate similar work in the future.]]></description><pubDate>Tue, 09 Jun 2026 10:54:11 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712015</guid></item><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></channel></rss>