<?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" 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>On-Demand Transit: Quality of Service Measures and Standards</title><link>http://pubsindex.trb.org/view/2767166</link><description><![CDATA[As part of Transit Cooperative Research Program (TCRP) Project A-47, “Transit Capacity and Quality of Service Manual, Fourth Edition,” the research team conducted a series of small research tasks to develop new content for the manual. This TCRP Research Results Digest (RRD) is one of 12 presenting the results of these research tasks. On-demand transit, which includes microtransit, is an emerging form of demand-responsive transit service that provides rides on demand (i.e., without an advance reservation) within a defined service area. This RRD summarizes performance measures and standards, particularly those related to quality of service, used by North American on-demand transit operators, and documents the rationales for the on-demand transit quality of service tables added to the 4th edition.]]></description><pubDate>Sat, 22 Aug 2026 12:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2767166</guid></item><item><title>Fostering Implementation of Knowledge Management: A Guide</title><link>http://pubsindex.trb.org/view/2767168</link><description><![CDATA[This report presents guidelines for state departments of transportation (DOTs) and other transportation agencies on fostering the implementation of knowledge management (KM) within their organizations. The guide provides a broad review of the evolution of KM at state DOTs since the early 2000s and documents the successes and challenges encountered along the way. It also provides refined strategies for fostering the implementation of KM based on lessons learned. The guide should be of value to agencies seeking to understand the development of KM at state DOTs, what practices have been the most successful, and the selection of strategies for future KM implementation efforts.]]></description><pubDate>Sat, 22 Aug 2026 12:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2767168</guid></item><item><title>Developing Strategies to Foster Implementation of Knowledge Management</title><link>http://pubsindex.trb.org/view/2767167</link><description><![CDATA[The objective of NCHRP Project 23-31A was to provide strategies and proven approaches to foster knowledge management (KM) investment, development, and sustainability. This document is the final research report for NCHRP Project 23-31A. It describes the activities conducted to carry out the project, and provides an overview of the major products. Appendix A summarizes the results of the literature and practice review; Appendix B summarizes the results of the stakeholder engagement activities; and Appendix C summarizes the results of a stakeholder workshop held to obtain feedback on the draft research products.]]></description><pubDate>Sat, 22 Aug 2026 12:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2767167</guid></item><item><title>Consolidating Microtransit and ADA Paratransit Services</title><link>http://pubsindex.trb.org/view/2767165</link><description><![CDATA[As part of Transit Cooperative Research Program (TCRP) Project A-47, “Transit Capacity and Quality of Service Manual, Fourth Edition,” the research team conducted a series of small research tasks to develop new content for the manual. This TCRP Research Results Digest (RRD) is one of 12 presenting the results of these research tasks. Consolidating or “commingling” (or co-mingling) general-public microtransit service and Americans with Disabilities Act (ADA) paratransit service offers the potential for operational cost efficiencies and improved mobility options for paratransit customers. This RRD describes methods used by transit agencies to consolidate their ADA paratransit (or other coordinated paratransit) trips with on-demand microtransit service.]]></description><pubDate>Sat, 22 Aug 2026 12:31:06 GMT</pubDate><guid>http://pubsindex.trb.org/view/2767165</guid></item><item><title>Determining Fleet Mix and Vehicle Size in the Rural Transit Setting</title><link>http://pubsindex.trb.org/view/2767170</link><description><![CDATA[This report presents a guide for rural transit fleet composition and vehicle procurement decisions. The report helps state departments of transportation (DOTs) and rural transit agencies by offering tools to assist with fleet optimization and decision-making. The research included a literature review, data analysis, surveys, interviews, and case studies. The report will be of immediate interest to transit practitioners and will serve as a valuable resource for state DOTs and rural transit agencies.]]></description><pubDate>Sat, 22 Aug 2026 12:31:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2767170</guid></item><item><title>A Systematic Data-Driven Framework for Evaluating Changes on Traffic Signal Settings</title><link>http://pubsindex.trb.org/view/2764035</link><description><![CDATA[Transport agencies worldwide regularly assess the state of traffic corridors and make necessary changes to enhance performance. It is important to systematically evaluate the effect of these changes to quantify their actual effectiveness and inform future decision-making. This study proposes a framework for a formal evaluation and quantification of changes in traffic signal settings while considering multiple key performance measures and likely hidden traffic patterns that could affect system performance and subsequent conclusions. The proposed framework involves: (1) data preparation procedures and recommendations, including a minimum sample size estimation; (2) a before and after analysis considering several performance measures, such as the empirical cumulative distribution function, buffer index, and planning time index; (3) a pattern analysis to account for inherent traffic flow dynamics; and (4) a seasonality analysis to capture long-term variations in traffic conditions. This study illustrates, using three case studies in South East Queensland, Australia, how overlooking certain aspects of an evaluation process can lead to incomplete or inaccurate conclusions. Two of the case studies involved upgrades in signal timings, while the third represented a control scenario with no changes. The proposed framework effectively quantified the effect of the upgrades on the first two case studies, while no statistically significant changes were observed for the third case study. Distinct traffic patterns were identified, and the corresponding effects were assessed accordingly.]]></description><pubDate>Thu, 20 Aug 2026 16:53:20 GMT</pubDate><guid>http://pubsindex.trb.org/view/2764035</guid></item><item><title>A Framework for Map Agnostic Conflation: Challenges, Application, and Suitability in the Context of Mobility-Based Performance Measures</title><link>http://pubsindex.trb.org/view/2752257</link><description><![CDATA[Map conflation has been an unfastidious yet important step in conflating both spatial and numerical roadway attributes. Oftentimes, a dataset that is pertinent to a roadway Network A is contrasted with another Network B. The process of spatially matching this pair of A and B poses certain logical and computational challenges. This paper illustrates these challenges and presents an algorithm that conflates proprietary third-party datasets (Network B) with the South Carolina Department of Transportation’s linear referencing system-based roadway network (Network A). This paper investigates the distortion of data caused by the map conflation and discusses its applicability in the context of several mobility-based performance measures. The study tested conflation pairs between the 73,899 Network A and 74,706 Network B segments, spanning 30,787 directional miles. Paired segments result in 99.23% spatial accuracy at a precision of 0.001 miles. The calculated statewide delay for the calendar year 2022 was 75,966.4 and 75,967.11 vehicle-hours for Networks A and B, respectively, which is a close match. In addition, the conflated maximum level of travel time reliability between the Network A and B segments showed high resemblance, with only about 0.5% mismatches. Eliminating semantic attribute considerations from the conflation process presented in this study created a method that is efficient, highly accurate, agnostic of input networks, and is applicable to any network with a clearly defined relationship between the segment topology and direction of travel.]]></description><pubDate>Thu, 20 Aug 2026 14:57:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2752257</guid></item><item><title>Resilience and Recovery of Points of Interest Following Hurricane Helene: A Temporal Analysis</title><link>http://pubsindex.trb.org/view/2752255</link><description><![CDATA[Natural disasters disrupt transportation accessibility, travel behavior, and local economic activity unevenly, yet limited research examines recovery at the level of individual destinations. This study assessed post-disaster mobility resilience across 5,671 points of interest (POIs) in western North Carolina following Hurricane Helene. Because POIs represent major destinations for employment, healthcare, education, retail, and services, their visitation trajectories provide a practical indicator of transportation functionality and access restoration. Using 616 days of high-resolution mobility data from May 2023 to February 2025 and integrating visitation records with precipitation, elevation, and road-closure data, we developed a forecasting and impact-measurement framework using the TimesFM foundation time-series model to generate counterfactual visitation trajectories and quantify disruption through a normalized signed area under the curve (AUC) metric. Error-propagation diagnostics showed that transformer-based models, particularly TimesFM, outperformed classical approaches for long-horizon POI forecasting. Sector-level differences were substantial. Educational services experienced the largest visitation declines, followed by healthcare and public administration, whereas administrative and waste-management services, real estate, retail, and accommodation showed visitation increases associated with displacement, supply acquisition, and recovery-related demand. An XGBoost model with feature ablation and SHAP analysis identified industry sector and pre-hurricane visitation as the most influential predictors of POI-level AUC. The model achieved an RMSE of 0.1453, providing acceptable explanatory power and meaningful directional insight. Higher pre-hurricane visitation generally reduced predicted disruption, suggesting that high-activity POIs were more likely to remain stable or recover quickly. These resilience-anchor POIs were concentrated in commercial and mixed-use properties, gasoline stations, large-format retail, and higher-education institutions. The proposed framework provides actionable insights for prioritizing access restoration, coordinating recovery resources, identifying critical establishments, and strengthening resilience planning for future extreme events.]]></description><pubDate>Thu, 20 Aug 2026 14:57:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2752255</guid></item><item><title>Assessment of Bicycle–Vehicle Conflicts in Digital-Twin Environment with Various Levels of Connectivity</title><link>http://pubsindex.trb.org/view/2762074</link><description><![CDATA[Intersections present considerable safety risks for bicyclists, where road space shared with vehicles frequently triggers conflicts. Although connected technologies promise safety benefits, they may trigger additional stress and adverse behavioral changes, making it important to investigate their impact on bicyclist–vehicle conflicts. This study introduces a novel experimental framework that integrates a high-fidelity digital twin of a one-way signalized arterial (Delaware Avenue in Newark, DE, United States) with a dual human-in-the-loop simulator, allowing real-time interaction between a human-driven vehicle and a human-operated bicycle. The digital twin combines CARLA, used for immersive participant simulation, and PTV Vissim, which replicates realistic background traffic conditions. The first step was to identify potential conflicts by considering traditional surrogate safety measures and validating them as real conflicts with self-reported feedback and physiological stress responses. Results confirmed that the experimental setup produced events meeting both kinematic and stress-based conflict criteria. The second stage focused on how connected technologies influence user experience, specifically whether collision warning information evokes additional stress, given its novelty and limited adoption in real-world contexts. Finally, we examined behavioral changes by analyzing approach and postconflict speeds, revealing how connectivity affects speed choices in conflicting situations, with drivers increasing approach speeds and bicyclists becoming more cautious. This research demonstrates the value of combining digital twins, dual human-in-the-loop simulators, and physiological sensing for studying connected safety technologies. The findings provide new insights into how connectivity influences user stress and behavior, helping the design and evaluation of future connected multimodal traffic systems.]]></description><pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2762074</guid></item><item><title>Measuring the Destination Access and Equity Impacts of Public Transit Service Changes: A Case Study of Title VI Policy at MBTA</title><link>http://pubsindex.trb.org/view/2762072</link><description><![CDATA[Under Title VI of the U.S. Civil Rights Act, public transportation agencies that receive federal funding in the U.S. are obligated to ensure that changes to transit service do not disproportionately burden populations of color or low-income households. However, existing Title VI policies only require that agencies analyze the social equity impacts of large-scale, often pre-planned changes, such as new or extended routes. The equity impacts of the often smaller-scale service changes to multiple routes that transit agencies make on a regular basis in response to operating conditions have attracted limited attention in the research literature. Using a case study of eight service changes implemented by the Massachusetts Bay Transportation Authority between December, 2022, and December, 2024, this study demonstrates that regular service changes can sometimes have substantial and racially disparate impacts on job access, in some cases even exceeding the impacts of the “major” projects that are covered by existing Title VI policies. The results also suggest that “quantity-of-service” measures, such as revenue vehicle hours, which current Title VI policies suggest using to define “major” changes and disparate impacts, are flawed indicators of the user benefits and burdens of transit service changes, with measures of destination accessibility representing a more robust alternative.]]></description><pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2762072</guid></item><item><title>Integrating Paver-Mounted Thermal Profiling and Dielectric Profiling as Process Control Tools for Asphalt Pavement Construction</title><link>http://pubsindex.trb.org/view/2762073</link><description><![CDATA[In-place density and uniformity are crucial for asphalt pavement performance. Even minor low-density areas can cause distress, despite overall compliance with specifications. Traditional quality-control methods, such as coring and nuclear gauge testing, often fail to capture the variability across the pavement mat. To overcome these limitations, this study explores the combined use of the paver-mounted thermal profiler (PMTP) and the dielectric profiling system (DPS) as continuous process control tools. The data for this research were collected from six national demonstration projects conducted by the Mobile Asphalt Technology Center (MATC), representing a range of climates, mixtures, and paving conditions. The PMTP was used during placement to develop thermal maps of mat temperature, while the DPS was employed after compaction to generate continuous dielectric profiles. Calibration of the DPS using gyratory-compacted specimens showed strong correlations with density (R² = 0.97–0.99), an improvement over calibration based on narrow-banded roadway cores. When validated against roadway cores, the DPS predictions had root mean square errors of 0.6%–1.5%, with mean bias generally within ±1%, and most projects showed moderate to strong correlation (R² = 0.65–0.92). PMTP data indicated thermal anomalies, but temperature alone cannot account for all density variability. Case studies show that combining PMTP, DPS, and a few core samples can identify localized risk zones and give contractors actionable feedback. Continuous profiling can eliminate low-density areas associated with spot testing, enhance quality control, and support specifications that account for both density and uniformity across the mat.]]></description><pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2762073</guid></item><item><title>A Guide to Using Artificial Intelligence to Enhance Transportation Asset-Management Plans</title><link>http://pubsindex.trb.org/view/2762071</link><description><![CDATA[State departments of transportation are required to develop transportation asset-management plans (TAMPs). These data-driven plans are based on asset inventories, condition, and available resources and provide a plan for investing in and managing assets over 10 years. Advances in artificial intelligence (AI) provide opportunities to develop and enhance these plans. However, selecting appropriate tools and developing verifiable, transparent applications is challenging. This paper presents a guide for state departments of transportation to understand the concepts and to use these tools to enhance their TAMPs. The paper aims to raise awareness of opportunities and potential issues and help asset managers develop the vocabulary needed to interact with AI experts. An overview of machine learning covers the different paradigms and tools. Building on the review of AI methods, the paper presents opportunities for using AI within the TAMP building blocks (asset inventory, condition assessment, deterioration modeling, and decision making), as well as two cross-cutting functions: data quality and integration and communication. The paper then presents how these building blocks are used to develop the required sections of the plan (gap analysis, life-cycle planning, risk analysis and risk management, financial plan, and investment strategies) and consistency determination. Example applications are included. Finally, implementation guidance is provided.]]></description><pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2762071</guid></item><item><title>Network-Level Vehicle Delay Estimation at Heterogeneous Signalized Intersections</title><link>http://pubsindex.trb.org/view/2762070</link><description><![CDATA[Accurate vehicle delay estimation is crucial for assessing signalized-intersection performance and guiding traffic management. Traditional machine-learning models often assume identical distributions between training and testing data. This assumption is rarely met across intersections due to differences in geometry, signal timing, and driver behavior, leading to poor generalization. To address this, this study proposes a domain adaptation (DA) framework that leverages a small labeled subset from the target intersection to improve delay estimation across diverse intersections. This study introduces a novel DA model, gradient boosting with balanced weighting (GBBW), which reweights source-domain data based on similarity to the target domain, enhancing adaptability. Using data from 57 heterogeneous intersections in Pima County, Arizona, we demonstrate that GBBW outperforms eight state-of-the-art machine-learning regression models and seven instance-based DA methods, providing more accurate and robust delay estimates. This framework improves the transferability of machine-learning models, supporting more reliable traffic-signal optimization, congestion management, and performance-based planning in real-world transportation systems.]]></description><pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2762070</guid></item><item><title>Measuring the Effect of General-Purpose Lane Reduction “Road Diet” Measures on Drivers’ Travel Behavior</title><link>http://pubsindex.trb.org/view/2762066</link><description><![CDATA[This study is an investigation of the effect of general-purpose lane reduction policies—commonly referred to as “road diets”—on drivers’ travel behavior, focusing on changes in travel route, departure time, and mode choice. A stated preference–off–revealed preference survey targeting drivers residing in Seoul was conducted in November 2024, yielding 3,466 responses, of which 650 were identified as valid. The data were analyzed using an error component mixed logit model to relax the assumption of independence from irrelevant alternatives and to account for correlation across alternatives and repeated choices at the individual level. In the model, four options were considered: maintaining private car use without changes (status quo), changing travel route, changing departure time, and switching to public transportation. The results indicate that lane reductions significantly influence drivers’ decisions, with a stronger preference for altering travel routes and departure times than for maintaining original travel behavior. However, shifts to public transportation were less pronounced, as drivers more frequently chose to adjust their routes or departure times instead. These findings highlight the importance of accounting for behavioral responses when implementing lane reduction policies and suggest that complementary measures, such as improved public transportation services and targeted incentives, may be necessary to encourage more substantial and sustained shifts toward public transport use. Future research should be conducted to examine the long-term effects of lane reduction and the interaction between such policies and public transportation improvements.]]></description><pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2762066</guid></item><item><title>A Data-Efficient Deep Learning Paradigm for Crack Segmentation Using Generative AI</title><link>http://pubsindex.trb.org/view/2762064</link><description><![CDATA[High manual annotation costs and poor generalizability often limit the robustness of deep learning models for pavement crack segmentation. This study addresses these challenges by developing and validating a diffusion-based generative artificial intelligence framework to support data-efficient crack segmentation models for infrastructure inspection. The proposed framework formalizes the generation and utilization of synthetic, pixel-level annotated crack data and is empirically evaluated across multiple learning paradigms. To implement and validate the framework, an annotated synthetic dataset (AI500) was generated using Google’s Imagen 4 Ultra and designed to capture diverse crack morphologies, lighting conditions, and surface textures. The utility of the synthetic data was systematically assessed through three experimental strategies: synthetic-only training, hybrid dataset training, and sim-to-real transfer. Model performance was evaluated using three deep learning architectures and five real-world datasets. Results show that synthetic-only models provide a robust, transferable performance baseline, achieving cross-domain generalization comparable to the target-only baseline (TOB) for models trained on real-world datasets. Augmenting AI500 with only 10% real-world data yielded performance comparable to TOB. Furthermore, sim-to-real transfer using the same 10% fraction achieved near parity with TOB. Cost analysis revealed the potential for double-digit savings without compromising model robustness. Additionally, the development of G3P-500 using Gemini 3 Pro demonstrated superior photorealism and diversity, requiring minimal human verification, with only 3.2% of pixels added to generated annotations. These findings demonstrate that high-quality synthetic data within a structured generative framework can substantially reduce annotation requirements while enabling accurate, generalizable pavement crack segmentation models. However, further validation remains necessary in more challenging environments.]]></description><pubDate>Thu, 20 Aug 2026 09:54:07 GMT</pubDate><guid>http://pubsindex.trb.org/view/2762064</guid></item></channel></rss>