<?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?cdatein=1year" 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>Revenue-Related Tools for New Mobility</title><link>http://pubsindex.trb.org/view/2709406</link><description><![CDATA[This report provides a toolkit to help transportation agencies design and evaluate a broad array of revenue-related tools, such as taxes, fees, and subsidies, that may be applied to new mobility. The research included outreach with stakeholders and case studies to understand the challenges posed by new mobility services that are rapidly emerging. This toolkit on revenue strategies for new mobility is both timely and necessary to address the disruptions straining traditional funding mechanisms for transportation. The toolkit should be of particular use to agencies seeking to align funding and policy goals with appropriate revenue-related tools and evaluate their successes and challenges in various contexts.]]></description><pubDate>Sat, 13 Jun 2026 15:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709406</guid></item><item><title>Developing a Guide to Depicting Utility Facilities in Design Plans</title><link>http://pubsindex.trb.org/view/2712237</link><description><![CDATA[At first glance, existing roadway alignments may seem to be simply comprised of pavement, shoulders, signage, and occasional utility structures like valve boxes, manhole covers, or utility poles. These visible features are readily documented and modeled using standard tools such as surveying, photography, and both 2D and 3D CAD. Yet, this apparent simplicity masks the complexity that lies beneath the surface. Subsurface utilities, buried anywhere from a few inches to several feet below the roadway and surrounding right-of-way (ROW), pose significant challenges. These hidden assets cannot be seen directly, and their location, condition, ownership, and potential impact on project cost and schedule cannot be determined through visual inspection alone. As a result, utility investigations depend on scientific methods (observation, measurement, and analysis) alongside ongoing communication among stakeholders. The aim is to support accurate, timely, and cost-effective project delivery. However, utility data is often inconsistently represented, and stakeholders may have varying expectations for how it should be depicted. While previous research has examined the causes of these inconsistencies, this study focuses on offering practical solutions and guidance. The proposed approach in this research draws from multiple disciplines: utility records research, field investigations, coordination efforts, highway planning and design, utility design and construction, and maintenance. Central to this methodology is the development of standardized depiction practices, through drawings, reports, and models, that address the diverse needs of stakeholders. The research also underscores the importance of engineering judgment in managing uncertainty, recognizing the risks associated with unseen or unknown conditions, and promoting clear communication of both known and unknown information for the benefit of all involved parties.]]></description><pubDate>Sat, 13 Jun 2026 15:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712237</guid></item><item><title>Public Transit and Ride Sharing for Rural, Tribal, and Frontier Agencies: A Guide</title><link>http://pubsindex.trb.org/view/2712234</link><description><![CDATA[This report presents guidance for transportation agencies on integrating public transportation in less populated areas with shared-use mobility (SUM) services. The guide provides examples of notable practices and lessons learned from various transportation agencies and presents information regarding SUM services and technology that can be scaled to rural, tribal, or frontier settings. The guide should be of value to state departments of transportation (DOTs) and other agencies seeking to improve available transportation services and enhance mobility options in their communities or regions via innovations in SUM.]]></description><pubDate>Sat, 13 Jun 2026 15:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712234</guid></item><item><title>Depicting Utility Facilities in Design Plans: A Guide</title><link>http://pubsindex.trb.org/view/2712238</link><description><![CDATA[This report presents guidance for state departments of transportation (DOTs) and other transportation agencies on depicting data from various sources to support design decisions and utility conflict identification and resolution. This guide supports end users in (1) depicting existing, proposed, and relocated facilities; (2) prioritizing the depiction of data from multiple sources; (3) reconciling inconsistent utility data from various sources; and (4) determining the reliability of depicted data for design standards. Because maintaining a robust utility dataset can be challenging for agencies, the guidelines in this report further enhance the mechanisms for providing depicted information at varying levels of detail to meet end users’ needs. The guide should be of value to both public and private agencies seeking to ensure that utility information is provided to stakeholders in a consistent format, with the appropriate level of comprehensiveness at the right time.]]></description><pubDate>Sat, 13 Jun 2026 15:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712238</guid></item><item><title>Training Materials to Implement Context Classifications</title><link>http://pubsindex.trb.org/view/2709407</link><description><![CDATA[This report documents the research approach and results of NCHRP Project 20‐44(51), Developing Training Materials to Implement Context Classifications. This work involved identifying state Departments of Transportation (DOTs) seeking to implement context classifications, and to receive from these DOTs feedback on training materials to support their implementation efforts (Task 1). Three state DOTs were selected, and information was collected from each to identify the extent to which it had implemented context classifications as well as potential opportunities to do so (Task 2). The research team used the findings from these activities to develop training materials and resources (Task 3), which they presented at a series of in‐person workshops with each of the three state DOTs (Task 4). Following the workshops, the research team facilitated follow‐up meetings with the state DOTs individually (Task 5) and then together as a peer group (Task 6) to understand progress made on implementation activities. The research team then created a webinar (Task 7) to advertise the final updated training materials (Task 8), which were updated based on lessons learned from previous tasks.]]></description><pubDate>Sat, 13 Jun 2026 15:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709407</guid></item><item><title>Public Transit and Ride Sharing for Rural, Tribal, and Frontier Agencies: Research into Coordination</title><link>http://pubsindex.trb.org/view/2712233</link><description><![CDATA[Transportation mobility options for residents in rural and low-density areas are often more limited as a result of lower ridership demand and fewer available resources to provide service. In rural, frontier, and tribal areas, demand for transportation service typically comes from populations that are especially reliant on transit options (such as older adults, persons with disabilities, and low-income individuals). Residents in these areas typically must travel longer distances to access education, health services, shopping, and employment; travel on these journeys can be inconvenient through public transit due to low service frequency or availability. Rural transit agencies face challenges in lack of expediency for vehicle procurements as well as recruitment and retention of drivers, while staff members at smaller rural transit agencies and transportation-providing organizations often have several sets of responsibilities and limited availability to dedicate to new service or technology implementations. Shared use mobility (SUM) services have been seen as a means to both fill existing mobility gaps and provide enhanced options for residents to travel in the form of on-demand service, smartphone apps, and other features not previously available in existing transportation programs. Goals for these new services have included travel to specific destination centers, first-and-last-mile connections to fixed-route transit systems, targeted service improvements for specific communities or population groups, and many others. However, SUM companies and technologies do not represent a cure-all solution to rural transportation challenges and need to be implemented strategically to fit rural settings rather than implementing models designed for urban areas. Offering on-demand or immediate-fulfillment service in large, less populated areas is challenging from operations and vehicle scheduling standpoints. While many communities want the convenience that on-demand SUM offers, sustainability of funding and demand for these services remains the largest challenge. The research tasks for this National Cooperative Highway Research Program (NCHRP) 08-130 project focused on gathering information about on-demand SUM services and technologies applied toward rural, tribal, and frontier settings. The project scope included activities to gather examples and perspectives on SUM and rural transportation such as outreach activities with stakeholders at rural transit agencies, cities and municipalities, nonprofit organizations, private industry, technical assistance centers, state departments of transportation (DOTs), and other entities engaged in rural transportation. The results of this project effort were the development of companion resources that can assist such agencies and organizations in planning for SUM service implementations in their own communities.]]></description><pubDate>Sat, 13 Jun 2026 15:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712233</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>Resilience-Oriented Line Planning for Multimodal Rail Transit Network Considering Uncertain Passenger Service Choice Behavior</title><link>http://pubsindex.trb.org/view/2711991</link><description><![CDATA[The train line planning problem (LPP) determines passenger travel path accessibility by optimizing train routes and stop plans. This study considers the uncertainty of passenger service choice behavior and the partial periodic operation pattern in the multimodal rail transit network (MRTN), defines system resilience as the network’s ability to resist interference, and constructs a resilience-oriented LPP model with constraints for passenger assignment that account for uncertain service choice behavior. A customized iterative solution procedure is designed to solve this model. In each iteration, a passenger assignment algorithm that integrates an available travel path search method is developed to determine passenger travel paths, and an improved adaptive large neighborhood search (IALNS) algorithm with a network decomposition strategy is designed to solve the LPP. The proposed approach is examined on Shanghai MRTN, with analysis of influences of travel path resilience requirement, uncertainty in passenger service choice behavior, and partial periodic operation strategy. The results indicate that incorporating path resilience into LPP can enhance network resilience with limited operational cost increases, accounting for passenger uncertain service choice behavior can more accurately match the transport capacity with passenger demand, and the partial periodic pattern can balance the regularity and flexibility of the line plan. Furthermore, the IALNS algorithm outperforms Gurobi on large-scale cases, and the proposed approach can well balance operational costs, generalized passenger travel time, and network resilience. Case study findings provide insights for rail operators in line planning.]]></description><pubDate>Thu, 11 Jun 2026 09:16:26 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711991</guid></item><item><title>Feasibility of Stabilized Titanium Gypsum as Roadway Subgrade Material: Evaluation of Geotechnical Properties and Leachability</title><link>http://pubsindex.trb.org/view/2711990</link><description><![CDATA[Leachates from unlined titanium gypsum (TG) dumps can pose significant environmental pollution risks to surrounding soils and groundwater. Reusing open-dumped TG as construction material is effective in alleviating the risks. This study presents a systematic evaluation of the geotechnical properties and leachability of novel binder-stabilized TG used as a roadway subgrade material. The binder consisted of reactive magnesia (MgO), ground granulated blast furnace slag (GGBS), fly ash (FA), and rice husk ash (RHA). Macroscopic and microscopic tests, including unconfined compression, one-dimensional swell, batch-type leaching, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy with energy-dispersive X-ray analysis, were conducted to evaluate the strength, swelling behavior, leachability, mineralogy, and microstructure of stabilized TG. Based on the consideration of strength and leached concentrations of chloride (Cl⁻) and sulfate ions (SO₄²⁻), in TG stabilized with 10% binder (by dry weight), the reactive MgO:GGBS:FA mass ratios in the binder were optimized as 1.3:5.2:3.5. The stabilized TG cured for 7 days under standard conditions possessed an unconfined compressive strength of 3.72 MPa and zero swell strain, whereas TG had an unconfined compressive strength of 0.73 MPa and a swell strain of 7.6%. Compared with TG, the concentrations of Cl⁻ and SO₄²⁻ leached from stabilized TG cured for 28 days decreased by 54% and 61%, respectively. Microscopic test results revealed that the formation of calcium-(ferrite)-silicate-hydrate and layered double hydroxides in stabilized TG was the primary mechanism for the immobilization of Cl⁻ and SO₄²⁻. The results are useful for facilitating safe utilization of TG in roadway construction.]]></description><pubDate>Wed, 10 Jun 2026 10:47:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711990</guid></item><item><title>Joint Latent Analysis of Behavioral Patterns in Multi-Source Origin–Destination Matrices</title><link>http://pubsindex.trb.org/view/2711989</link><description><![CDATA[Urban origin–destination (OD) data often come from two distinct sources: traditional surveys and global positioning system (GPS)-based records. The former offer rich behavioral detail but are infrequent and costly, while the latter provide continuous coverage but lack semantic context. They often lead to different findings and are rarely examined together, making it difficult to build a consistent understanding of urban travel behavior. To address this gap, we propose a dynamic joint latent factor model that decomposes multi-source OD matrices into shared spatial structures and source-specific temporal dynamics. The model identifies latent movement patterns by jointly factorizing both datasets while allowing each source to retain its unique temporal characteristics. Applied to GPS and survey OD data from Ottawa, Canada, the model achieves strong reconstruction accuracy (R²≈0.8 overall). The results uncover stable spatial patterns alongside clear differences in the traveler groups driving each latent mode. GPS factors emphasize younger and higher-income travelers, especially in the more flexible afternoon period. However, survey factors reflect routine commuting by middle-aged and middle-income households. These patterns show that the joint model isolates behavioral differences from sampling bias and provides a coherent representation of OD demand across sources. Together, these results show that the joint factorization quantifies behavioral differences from sampling bias and provides a coherent representation of OD demand across sources. This framework helps compare and interpret multi-source OD data in settings where traditional surveys are limited.]]></description><pubDate>Wed, 10 Jun 2026 10:47:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711989</guid></item><item><title>Quantifying Self-Explaining Road Performance in Spiral Tunnels: Semantic-Based Evaluation and Simulation Validation</title><link>http://pubsindex.trb.org/view/2711988</link><description><![CDATA[In spiral tunnels, high cognitive load and elevated operational risks are prevalent because of visual monotony and geometric ambiguity. This study develops a quantitative model of the self-explaining level based on the “self-explaining roads” theory, integrating environmental semantic segmentation and a three-level situational awareness model. The model introduces perceptual and comprehension attribute indicators and is validated through driving simulation experiments involving 27 participants across six spiral tunnel scenarios. The results indicate that the proposed model effectively reflects the self-explaining level of roads, with a correlation coefficient with behavioral indicators ranging from −0.234 to −0.326, indicating smoother driving behavior as the self-explaining level increases. As the curve radius increases, the self-explaining level also increases (e.g., 9.9 at radius [R] = 250 m, 21.0 at R = 500 m, 27.6 at R = 970 m). The performance in right-turn scenarios is better than that in left-turn scenarios (20.0 for right turns and 19.0 for left turns). The simulation adopted a left-side driving configuration, consistent with the design assumption for passenger vehicles in the studied tunnel scenario. In this study, drivers were guided to drive in the right lane. Additionally, entrance and exit areas introduce cognitive fluctuations because of abrupt changes in lighting and structure, highlighting the need for targeted optimization in these critical zones. This research provides a quantitative tool and methodological foundation for evaluating the safety of spiral tunnels, paving the way for future exploration into optimized design strategies and underlying cognitive mechanisms.]]></description><pubDate>Wed, 10 Jun 2026 10:47:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2711988</guid></item><item><title>Multivariate Negative Binomial Weighted Lindley Generalized Linear Model: Methodological Innovations and Applications in Traffic Safety</title><link>http://pubsindex.trb.org/view/2712069</link><description><![CDATA[The concept of a multivariate distribution is essential in statistics, allowing the simultaneous analysis of related variables. In transportation safety, such models are effective for studying crash data across multiple categories, improving predictions and evaluations of safety measures. This paper extends the negative binomial weighted Lindley (NB-WLindley) distribution, known for handling highly dispersed or sparse data, into a multivariate framework. The proposed multivariate NB-WLindley generalized linear model treats each crash category as a random variable dependent on other categories within a joint framework while preserving the marginal distributional properties. It is hierarchically defined as a mixture of NB and multivariate weighted Lindley distributions and incorporates a dependence structure to explain correlations among categories. Applications to two crash datasets show that the multivariate NB-WLindley model can simultaneously capture different crash types and severities, identifying dependencies that univariate models cannot. The study also develops a random parameters version of the model to address unobserved heterogeneity, which consistently outperforms the fixed-parameter version and yields stronger predictive performance. Overall, this work demonstrates that the proposed multivariate model offers a more flexible and accurate approach to crash analysis, enhancing the ability to capture variability and interdependence across crash categories. It provides a practical tool for improving safety assessments and supporting data-driven decision-making in transportation safety research.]]></description><pubDate>Wed, 10 Jun 2026 09:06:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712069</guid></item><item><title>Large Language Model-Based Realistic Safety-Critical Driving Video Generation</title><link>http://pubsindex.trb.org/view/2712066</link><description><![CDATA[Designing diverse and safety-critical driving scenarios is essential for evaluating autonomous driving systems. In this paper, we propose a novel framework that leverages large language models (LLMs) for few-shot code generation to automatically synthesize driving scenarios within the CARLA simulator, which has flexibility in scenario scripting, efficient code-based control of traffic participants, and enforcement of realistic physical dynamics. Given a few example prompts and code samples, LLM generates safety-critical scenario scripts that specify the behavior and placement of traffic participants, with a particular focus on collision events. To bridge the gap between simulation and real-world appearance, we integrate a video generation pipeline using Cosmos-Transfer1, which converts rendered scenes into realistic driving videos. Our approach enables controllable scenario generation and facilitates the creation of rare but critical edge cases, such as pedestrian crossings under occlusion or sudden vehicle cut-ins. Comprehensive quantitative evaluations across multiple environments demonstrate a favorable balance between visual realism, perceptual quality, and structural consistency in the generated videos. Experiment results demonstrate the effectiveness of our method in generating a wide range of realistic, diverse, and safety-critical scenarios, offering a promising tool for simulation-based testing of autonomous vehicles.]]></description><pubDate>Wed, 10 Jun 2026 09:06:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712066</guid></item><item><title>Classification of Bolt Corrosion Levels Combining Deep Learning and Multi-Feature Segmentation</title><link>http://pubsindex.trb.org/view/2712017</link><description><![CDATA[Many bolts are installed in subway tunnels, making manual inspection prohibitively costly, and deep learning models face difficulties in segmenting extremely small corroded regions, which results in low detection efficiency. To address these challenges, this study proposes a corrosion grade classification algorithm for subway tunnel bolts based on deep learning and multi-feature segmentation, which directly outputs the corrosion grade of each bolt to enhance maintenance efficiency. First, the YOLOv8 framework is improved using multi-scale channel group shuffle convolution (MSCGSC) and focal loss (FL) to develop the YOLO-MF (MSCGSC + FL) model for preliminary detection of corroded bolts. Second, the VGG16 network is employed as the backbone of U-Net, and channel shuffle is applied after the encoder–decoder concatenation to eliminate background noise of bolts using the VGG + channel shuffle (VCS)-Net model. Finally, the fusion of segmentation features, spatial features, and clustering features enables the accurate segmentation and grading of tiny corroded areas. Experiment results demonstrate that YOLO-MF and VCS-Net achieve higher accuracy in corroded-bolt detection and background noise removal. Compared with other segmentation approaches, the multi-feature fusion segmentation method improves the intersection over union by 0.1623. The corrosion grade results are directly printed on the images, facilitating maintenance operations, reducing the workload of tunnel maintenance personnel, and improving tunnel maintenance efficiency.]]></description><pubDate>Wed, 10 Jun 2026 09:06:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712017</guid></item><item><title>Novel Four-Season Durability Methods for Cement-Stabilized Clays</title><link>http://pubsindex.trb.org/view/2712016</link><description><![CDATA[The durability of stabilized subgrades is the backbone for ensuring pavement’s longevity. Its realistic evaluation is the key to accurate prediction of a pavement’s performance, aiding in the planning of maintenance and rehabilitation. Current ASTM standards for durability assessment of cement-treated soils discuss wetting–drying (WD) and freezing–thawing (FT) separately (in ASTM D559 and ASTM D560, respectively) and do not account for sequential interactions between moisture and temperature fluctuations. With weather patterns becoming more complex, most regions have already started experiencing all four seasons with varying intensities and frequencies. This makes the need for coupled durability assessments highly relevant to mimic the sequential interactions between environmental stresses induced by all four seasons. This paper thus presents two novel coupled durability methods, namely, wetting-drying-freezing-thawing (WDFT) and freezing-thawing-wetting-drying (FTWD), developed by a sequential combination of the existing ASTM standards for WD and FT. Low-plasticity clay specimens stabilized with two stabilizers, ordinary Portland cement (OPC) and Portland limestone cement (PLC), were considered for the coupled durability studies. The influence of the WDFT and FTWD cycles (0, 3, 7, and 10) on the performance of stabilized soils is determined through conventional volumetric and mass measurements. Additionally, rigorous engineering strength evaluations, including the unconfined compressive strength and resilient modulus, were employed, with pavement applications as the primary focus. Findings revealed that the sequence of environmental stressors greatly influences the performance, with WDFT being more detrimental by causing rapid volumetric changes and faster stiffness degradation in both OPC and PLC-stabilized soils, the effect being more pronounced in the latter case.]]></description><pubDate>Wed, 10 Jun 2026 09:06:00 GMT</pubDate><guid>http://pubsindex.trb.org/view/2712016</guid></item></channel></rss>