<?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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWRkYXRlIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM%2BPC9zZWFyY2g%2B" 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>Analysis of the Effect of Shield Tunnel Construction Method Under Viaduct on Tunnel Structure and Pile Foundations</title><link>http://pubsindex.trb.org/view/2693760</link><description><![CDATA[To ensure the safe operation of elevated bridges during shield tunneling, this study investigates the west extension of Shijiazhuang Metro Line 1, where the tunnel passes beneath an existing elevated bridge. Field monitoring data serve as validation. A numerical model was developed using FLAC3D based on site-specific geological conditions to examine three schemes: (1) a 0.7 m clearance between the tunnel and pile foundation; (2) earth pressure balance shield (EPBS) cuts through the bridge foundation piles; and (3) pre-reinforcement of the surrounding soil using a U-shaped Metro Jet System around the cut pile. In Scheme 1, the pile exhibits longitudinal deformation aligned with the tunnel’s advance direction. Vertical deformation presents an S-shaped profile, and lateral displacement remains negligible. The most pronounced pile deformation occurs from 2.4 m behind to 0 m ahead of the pile. In Scheme 2, lateral deformation remains constrained, but vertical and longitudinal deformations increase markedly. Maximum surface settlement occurs directly above the pile, and the tunnel lining becomes elliptical. In Scheme 3, relative to Scheme 2, longitudinal and lateral pile deformations exhibit little change, while vertical pile deformation, surface settlement, and tunnel lining distortion are significantly reduced.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693760</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>Transportation Job Ads: Do They Align with the Sector’s Technology-Driven Transformation?</title><link>http://pubsindex.trb.org/view/2693757</link><description><![CDATA[This paper offers insights into the alignment of sought-after versus ideal transportation workforce skills through an analysis of 8,132 job advertisements in the United States. Required skill sets for various jobs were extracted from online job-posting websites by using text mining tools. These data were compared with (1) the Occupational Information Network (O*NET) database that documents the workforce skill expectations of transportation industry professionals, (2) an industry survey capturing expert insights into disciplinary knowledge needed in the future transportation workforce. The analysis also investigated emerging transportation job titles to assess the industry’s hiring landscape driven by technological transformation. The findings aligned with literature about the high percentage of opportunities for middle-skill jobs, and transportation jobs generally requiring a mix of soft and hard skills, reflecting the industry’s diverse demands. Comparison with the industry survey indicated that the ideal workforce skills and necessary integration of disciplines such as social sciences were not explicitly stated in job ads, especially under preferred/required skills that determine the main candidate pool. Some transportation job ads include technology-related skills such as Python and SQL. However, many emerging jobs are yet to appear in the transportation industry. For emerging job positions, salary offers were found to be noncompetitive with other industries. The advertisements on emerging job titles were also found to be more explicit about the required skills and disciplines compared with transportation job advertisements. Overall, this paper identifies the gaps between ideal transportation workforce needs and the hiring landscape and provides recommendations to bridge those gaps.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693757</guid></item><item><title>Training Drivers in Advanced Driver Assistance Systems: How Training Content Shapes Driver Knowledge, Decision-Making, and Performance</title><link>http://pubsindex.trb.org/view/2693756</link><description><![CDATA[Driver understanding of Society of Automotive Engineers Level 1 and Level 2 automated driving systems is essential for safe human–automation interaction as these features are increasingly common in modern vehicles. Yet, little is known about how different training contents shape drivers’ knowledge and performance. This study investigates how variations in training content affect drivers’ knowledge, decision-making, driving performance, and subjective evaluations when using advanced driver assistance systems (ADASs), including adaptive cruise control, lane-keeping assist, and highway driving assist. Sixty participants were randomly assigned to one of three training groups: baseline training, driver-issue training, and feedback-based training. Pre- and post-training knowledge tests, simulator-based driving tests, and subjective questionnaires were used to evaluate outcomes. Results showed that feedback-based training significantly enhanced drivers’ knowledge compared with the other groups. Drivers trained with driver-issue content exhibited more stable lateral control. Training content did not meaningfully affect trust or perceived usefulness, although satisfaction differed across groups. These findings demonstrate that different contents of ADAS training influence driver understanding, driver performance, and subjective experience. This work provides guidance for designing future ADAS training programs that could help drivers to have safe driving experience.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693756</guid></item><item><title>Assessing Sweden’s Greenhouse Gas Emissions from Road Maintenance using Environmental Product Declarations and Network Life-Cycle Optimization</title><link>http://pubsindex.trb.org/view/2693755</link><description><![CDATA[To achieve the Paris Agreement’s goal of limiting the average temperature rise to 1.5°C, greenhouse gas emissions must be reduced by 43% from 2010 levels by 2030. This paper quantifies greenhouse gas emissions from road maintenance in Sweden and evaluates reduction pathways by integrating three elements: (1) lifetime estimates of maintenance operations, (2) corresponding emissions estimated from published Environmental Product Declarations, and (3) simulation of a 10-year maintenance plan that maintains current condition distribution at minimum cost across three regions (Stockholm, Skåne, Norrbotten). We assess three scenarios: a 2010 fossil-fuel baseline; a 2024 practice with predominantly biofuel-fired production and higher reclaimed asphalt utilization; and a 2024 extension that additionally replaces 5% of bitumen with a biogenic binder and employs biofuels for transport and machinery. Relative to 2010, the simulated emissions required to maintain current network condition were 28% to 30% lower under 2024 practices and 60% to 61% lower with added biogenic binder. The largest emission reductions arose from switching production heat from fossil to biofuels and from increased reclaimed asphalt. Total life-cycle impacts remained sensitive to treatment longevity, underscoring the need to integrate performance into procurement and to monitor the durability of emerging low-carbon materials. Although Sweden has reduced emissions substantially, achieving the 43% reduction target by 2030 will require measures beyond current practice.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693755</guid></item><item><title>Traffic Volume Estimation Using Deep Learning for High-Resolution Greenhouse Gas Inventory</title><link>http://pubsindex.trb.org/view/2693754</link><description><![CDATA[Traffic volume data are essential for policymakers in traffic management and for constructing a high-resolution greenhouse gas emissions inventory within the road transport sector. However, traffic volume is measured only on select roads, and coverage is limited. This study aims to construct traffic volume estimation models based on traffic speed using machine-learning approaches, including linear regression, random forest, gradient boosting models, a deep neural network (DNN), and a combined long short-term memory and DNN (LSTM-DNN) model. Among these, the LSTM-DNN model demonstrated the best performance, achieving an R² of 0.9404, a mean squared error of 94,331, and a mean absolute error of 199. While performance varied across different road grades—with somewhat higher errors for national highway, special metropolitan city road, and local road—these variations did not substantially affect downstream applications such as CO₂ emissions estimation. Validation using CO₂ emissions calculated from the estimated traffic volumes showed similar levels to those from other institutions, confirming the appropriateness of the traffic volume estimation. Notably, achieving high performance using only traffic speed and road information highlights the significance and the practical potential of this study’s approach for scalable traffic volume estimation.]]></description><pubDate>Fri, 17 Apr 2026 08:57:14 GMT</pubDate><guid>http://pubsindex.trb.org/view/2693754</guid></item><item><title>Evaluation and Design of a Footing Hybrid Connection for Innovative Hollow-Core Fiber-Reinforced Polymer–Concrete–Steel Composite Columns</title><link>http://pubsindex.trb.org/view/2692377</link><description><![CDATA[To support the advancement of accelerated bridge construction in high-seismic regions, this study investigates a novel prefabricated column-to-footing connection designed for improved resiliency, constructability, and cost efficiency. The socket connection utilizes hollow-core fiber-reinforced polymer–concrete–steel (HC-FCS) columns with embedded corrugated steel pipes (CSPs). The composite HC-FCS column consists of a concrete shell sandwiched between an outer fiber-reinforced polymer tube and an inner steel tube. The inner steel tube is embedded into the footing connection of the HC-FCS column. The same authors tested the innovative socket connection on a large HC-FCS column under seismic loads, showing high ductility, strong moment and drift capacities, and promising potential for future design use. Building on previous experimental findings that demonstrated excellent seismic performance, this study employs finite element (FE) modeling in LS-DYNA software to conduct a parametric analysis of 50 large-scale column-to-footing connections. The FE models were used to critically assess the effect of seven parameters on the seismic behavior of such a novel column-to-footing connection. Consequently, design equations based on mechanical analysis of a simplified strut-and-tie model were proposed to determine the essential characteristics of the CSP in HC-FCS column-to-footing connections for practical implementation.]]></description><pubDate>Thu, 16 Apr 2026 09:24:32 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692377</guid></item><item><title>A Simple Yet Efficient K-Nearest Neighbor-Based Method for High-Resolution Traffic Time–Space Diagram Imputation</title><link>http://pubsindex.trb.org/view/2692376</link><description><![CDATA[A time–space diagram (TSD) is an efficient tool for traffic analysis and visualization, representing the macroscopic traffic state as a set of cells. However, its application is often hampered by data sparsity, which obscures high-resolution traffic dynamics. This study proposes a modified K-nearest neighbors method, characterized by an adaptive iterative process, to impute missing TSD data. To support the method’s design, analytical bounds on error propagation motivated by Green’s function-based theory are established, and a practical empirical formula for the optimal K parameter is derived. The framework’s performance was rigorously validated on diverse data sets from China (Ubiquitous Traffic Eyes), the US (Next Generation Simulation), and Germany (HighD) across 30 distinct experimental conditions. Compared against four baseline models, the proposed model demonstrates a compelling balance between high imputation accuracy and exceptional computational efficiency. Further analyses confirm the influence of neighborhood order and the systematic performance bias. The model’s potential for knowledge transfer is also demonstrated via a cross-data set imputation scheme.]]></description><pubDate>Thu, 16 Apr 2026 09:24:32 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692376</guid></item><item><title>Analysis of Car-Following Safety Evolution in Mixed-Driving Environment: A Resilience Perspective</title><link>http://pubsindex.trb.org/view/2691793</link><description><![CDATA[As autonomous-driving technology advances, autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to coexist for an extended period. While resilience is widely applied in macro safety analysis, its application in micro-process modeling remains underdeveloped. This study proposes a resilience-based framework comprising three phases (initial, safety decay, safety recovery) to investigate the evolution of driving risk across three car-following scenarios: HV–HV (HV following HV), HV–AV (HV following AV), and AV–HV (AV following HV). The analytical framework consists of three steps: First, car-following pairs with complete risk-evolution processes were identified. Second, cluster analysis was applied to categorize phase-specific patterns. Third, the association rule mining algorithm was used to trace risk-evolution chains, followed by a comprehensive evaluation integrating safety and efficiency. Key findings include: (1) significant and rapid safety decay being accompanied by swift recovery, and the following vehicle exhibiting significant feature differences across recovery patterns; (2) safety decay in HV–AV being slower than that in HV–HV, with HV–AV demonstrating a close and conservative driving strategy during the risk-evolution process, with HV–AV meanwhile performing worse in safety and efficiency compared with HV–HV, highlighting the interaction discordance between HV and AV; (3) excessive deceleration of HV being the primary trigger for safety degradation in AV–HV, and AV demonstrating effective adaptation to safety decay, promoting slight safety reduction with rapid recovery, thereby balancing safety and efficiency. These findings reveal principles of risk evolution in mixed-driving environments, providing a novel analytical framework for mixed-driving safety.]]></description><pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691793</guid></item><item><title>Place of Last Drink (POLD) Implementation: Examination of Five Sites Using the POLD Implementation Framework</title><link>http://pubsindex.trb.org/view/2691795</link><description><![CDATA[Alcohol-impaired driving continues to be a significant problem in the United States. In 2022, over 13,500 fatalities were attributed to crashes where at least one driver was alcohol-impaired. Alcohol-impaired drivers were involved in 32% of traffic fatalities in 2022. Alcohol is a serious public health problem related to injury, death, and violence. One factor contributing to impaired driving is overservice at licensed alcohol establishments—research shows that over 80% of bars and restaurants will sell alcohol to someone who appears obviously intoxicated. Place of Last Drink (POLD) is an approach that law enforcement agencies can use to address alcohol-impaired driving incidents by identifying alcohol establishments that overserve alcohol. To implement POLD, a law enforcement officer collects information on the last place someone consumed alcohol before an alcohol-related traffic stop or incident. Authorities can work with an establishment identified as the POLD, require corrective action, or impose sanctions. Only a handful of studies have examined implementation of POLD, but they identify substantial differences in implementation and use of POLD data. To assess its effectiveness, it is essential to understand how POLD is implemented. This study examines case studies from five sites that are implementing POLD using a POLD implementation framework to compare and contrast implementation, identify differences. common strategies, strengths, and areas for improvement to inform and improve future implementation. The study shows there is a need to better understand how POLD is implemented and used in other sites, to develop clearer recommendations for its use.]]></description><pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691795</guid></item><item><title>A Novel Low-Cost Double U-Net Model for Predicting Traffic Sign Retro-Intensity from Camera Data</title><link>http://pubsindex.trb.org/view/2691791</link><description><![CDATA[Retroreflectivity is essential for the visibility of transportation infrastructure, ensuring road safety, especially under low-light conditions. Traditional methods for measuring retroreflectivity, such as nighttime visual inspections and retroreflectometer measurements, are labor-intensive, subjective, and pose safety risks. With the introduction of lidar technology, traffic sign retroreflectivity can be assessed more efficiently, as lidar-derived reflectivity values demonstrate a strong linear correlation with retroreflectivity. This study leverages a lidar device to propose a Double U-Net framework for predicting pixel-level reflectivity from daytime red, green, blue (RGB) images, providing a localized and accurate prediction. To train the Double U-Net model, a structured data set of over 7,600 images of transportation infrastructure was created, incorporating lidar-derived depth and reflectivity data. Given the sparsity of low-resolution lidar point clouds, linear interpolation was applied to generate pixel-level depth and reflectivity images. The proposed Double U-Net framework employs a two-stage architecture, where depth is predicted from cropped images in the first stage, and then combined with the original image and class embeddings in the second stage to generate pixel-level reflectivity predictions. A weighted loss function balances depth and reflectivity errors, enhancing prediction accuracy and robustness. The model achieved a median mean square error (MSE) of 0.0162 with interpolated data, 0.02233 with raw data, a median structural similarity index measure (SSIM) of 0.5413, and a Mann-Whitney U Test alignment of 58.2% with raw reflectivity data at a 0.001 significance level. The model effectively captures localized defects on traffic signs, providing a more detailed analysis compared with traditional methods.]]></description><pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691791</guid></item><item><title>Toward Safer Highways: Data-Driven Approach to Detecting Aggressive Driving Using Connected Vehicle Technologies</title><link>http://pubsindex.trb.org/view/2691792</link><description><![CDATA[Aggressive driving behaviors such as tailgating and cutting off pose serious highway safety risks, especially for trucks. Timely detection of these behaviors can enable real-time interventions (e.g., automated driver warnings or vehicle safety system activation) to prevent crashes. This study presents a machine learning approach to detect tailgating and cut-off events using data from a high-fidelity driving simulator. Forty participants drove a truck in mild and heavy traffic scenarios within a connected vehicle (CV) environment, providing rich data for analysis. We fused four data sources—vehicle kinematics, CV-based metrics, road characteristics, and driver demographics—into five feature combinations to evaluate their predictive power. Four classification models (Artificial Neural Network, Support Vector Machine, Random Forest, and XGBoost) were trained on these feature sets. Performance evaluation across traffic scenarios shows that models leveraging CV data significantly outperform those using only traditional data, achieving high accuracy in identifying aggressive behaviors. Integrating CV features with conventional kinematic data substantially improved tailgating and cutting-off detection, underscoring the promise of CV technology for enhancing highway safety.]]></description><pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691792</guid></item><item><title>Boston Blind Zone Safety Initiative: Current Fleet Analysis, Market Scan, and Proposed Direct Vision Rating Framework</title><link>http://pubsindex.trb.org/view/2692319</link><description><![CDATA[In about one-quarter of low-speed, truck-involved, vulnerable road user fatalities in the U.S., a driver’s direct vision was impaired. A driver has direct vision of an object outside the vehicle when it can be seen without the aid of mirrors or camera displays. Vehicles vary in how near drivers can see outside the vehicle to the front and to the side. This paper reports a first-in-the-U.S. effort with the U.S. Department of Transportation Volpe Center, the Boston Public Health Commission, and the Boston Transportation Department to assess the direct vision for vehicles used for Boston’s Schools, Fire, and Public Works Departments. The research team quantitatively measured direct vision in 21 vehicles using both a manual approach and a camera-based approach. Using these methods, this paper proposes a direct vision rating system that the City of Boston and other fleets can incorporate into procurement. The proposed system includes front and passenger five-star ratings based on the distance at which a child or adult would be visible directly in front of or to the passenger side of the vehicle, calibrated to federally and locally defined intersection geometric standards. A five-star vehicle enables drivers to see children in the crosswalk and children on bicycles in a buffered bicycle lane. In 11 of the 21 vehicles, drivers whose vehicle was stopped at the stop bar before a crosswalk at an intersection could not adequately see a child in the crosswalk in front or an adult on a bicycle on the side.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2692319</guid></item><item><title>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><item><title>Framework to Correlate Hamburg Wheel Tracking Test Results for 4- and 6-in. Asphalt Mix Specimens</title><link>http://pubsindex.trb.org/view/2691043</link><description><![CDATA[The study proposes a threshold value for selecting rut-resistant Marshall-designed bituminous mixtures. It also determines the correlation between 4-in. and 6-in. Hamburg Wheel Tracking Test (HWTT) samples. Samples of 4 in. and 4 in. diameter comprising different binder grades (VG-30, VG-40 and PMB-40) with varying air voids (2.5%, 4% and 7%) were tested using HWTT. The study emphasizes the stronger correlation (R = 0.99; R2 = 0.98) between 4-in. and 6-in. samples in HWTT results, providing useful information to pavement engineers. The test result also shows that polymer-modified mixtures have better rutting resistance because of their increased viscosity while higher compactive efforts improve resistance by minimizing air voids. Overall, this study underscores the role of performance testing and specification (rut depth = 6.5 mm at 20,000 passes for the Marshall mixtures) in ensuring the long-term performance and durability of pavements. These findings provide valuable guidance for engineers and researchers aiming to increase the performance of bituminous pavements through effective material selection and design strategies.]]></description><pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate><guid>http://pubsindex.trb.org/view/2691043</guid></item></channel></rss>