<?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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzcyIiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%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>Optimization of Hot-Mix Asphalt Rolling Pattern Using Ground-Penetrating Radar and Markov Decision Processes</title><link>http://pubsindex.trb.org/view/2726620</link><description><![CDATA[Ground-penetrating radar (GPR) has been used for nondestructive evaluation of hot-mix asphalt (HMA) pavements including density prediction. HMA density is an acceptance quality characteristic (AQC) used across the U.S. AQCs are the basis for quality control and acceptance, and are used by agencies to determine contractors’ pay. Recently, roller-mounted GPR was introduced to monitor HMA density in real-time, enabling roller operators to make more informed decisions to avoid under- and over-compaction of HMA layers. In this study, a Markov decision process (MDP) is formulated to represent the rolling pattern optimization problem. This formulation accounts for GPR prediction error, density spatial variability, and uncertainty in density progression. The introduced MDP provides contractors and roller operators with a tool to minimize operational costs while achieving target density, thereby enhancing pavement service life and reducing maintenance activities. Data collected from several field projects were used in the MDP formulation. The benefits of using the developed MDP for compaction decisions were demonstrated using project data from Illinois, U.S. The analysis was conducted for an actual project scenario under Illinois’ quality control for performance (QCP) program and was extended to a hypothetical pay for performance (PFP) scenario to evaluate the generalizability of the approach under different risk levels. Compared with an experienced roller operator, MDP decisions reduced the construction time by 40.3% and 18.1% and increased the revenue by 9.7% and 50.2% for the QCP and PFP scenarios, respectively. Additional benefits in energy savings, reduced construction-related delays, and improved worker safety are expected.]]></description><pubDate>Mon, 13 Jul 2026 08:47:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2726620</guid></item><item><title>Assessing Functional Performance of Asphalt Pavements under Data Sparsity: A Probabilistic-Deterministic Approach</title><link>http://pubsindex.trb.org/view/2724783</link><description><![CDATA[Asphalt pavements, subjected to continuous traffic loads and environmental stressors, undergo deterioration processes that gradually compromise their structural and functional integrity. Pavement management systems (PMS) have been implemented to forecast deterioration and plan maintenance, but empirical models commonly used in PMS encounter limitations in data-scarce environments. To address this, a probabilistic-deterministic approach is proposed to evaluate the functional condition of in-service asphalt pavements. The international roughness index (IRI) was selected as a functional condition indicator. IRI measurement data were used for two modeling approaches: (i) a Markov chain-based probabilistic model for short-term IRI condition states and (ii) a deterministic model for long-term IRI prediction and remaining service life estimation. The probabilistic model categorizes the IRI into five condition states, while the deterministic model uses exponential regression with estimated pavement age as input. Results show that the Markov chain model effectively represents functional deterioration and provides short-term predictions without historical data. Analysis revealed a gradual decline in pavement condition with a corresponding rise in lower condition states across all functional classes. State roads exhibited an accelerated transition to lower states, highlighting the need for early interventions. A faster degradation rate was also observed once pavements declined to “fair” or “poor.” Validation confirmed that the short-term predictions were consistent with field observations, with absolute state proportion differences ranging from 0.0002 to 0.1116. The deterministic model demonstrated accurate IRI predictions, with R² ranging from 0.84 to 0.86. Consequently, integrating both models enables reliable condition evaluation and maintenance planning, regardless of historical data availability.]]></description><pubDate>Mon, 13 Jul 2026 08:47:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724783</guid></item><item><title>Central Europe: Application of the Unified Theory of Acceptance and Use of Technology Framework</title><link>http://pubsindex.trb.org/view/2724781</link><description><![CDATA[This study investigates the behavioral intention and actual use of ride-hailing services among Generation Z (Gen Z) in Slovakia and the Czech Republic, applying the Unified Theory of Acceptance and Use of Technology (UTAUT), updated to its 2022 version. Data were collected from Slovak and Czech respondents between May and September 2024 and analyzed using PLS-SEM. The findings reveal notable cross-country similarities and differences. Behavioral intention was significantly influenced by performance expectancy, habit, and social influence in both countries. Effort expectancy was significant only in the Czech sample, while compatibility and personal innovativeness influenced behavioral intention only in Slovakia. Price value, hedonic motivation, and facilitating conditions showed no significant effects. Actual use was driven by behavioral intention in the Czech sample and by habit in both countries. This research fills a critical gap in understanding ride-hailing adoption within Gen Z in Central Europe, a demographic previously underexplored in the UTAUT context. The results provide actionable insights for service providers aiming to better align their offerings with the expectations and behaviors of young users. By addressing regional nuances, this study contributes to the global discourse on technology adoption and transport economics, offering a significant foundation for future research in this field.]]></description><pubDate>Mon, 13 Jul 2026 08:47:45 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724781</guid></item><item><title>Evaluation of Deep Learning Strategies Using Two Different Image Data Sets for Automated Pavement Distress Detection Methodology</title><link>http://pubsindex.trb.org/view/2724839</link><description><![CDATA[This paper investigates strategies to enhance the performance of deep learning models in pavement distress detection by utilizing two distinct datasets collected and annotated from different sources (vendors). The datasets consist of two- and three-dimensional images scanned from asphalt/concrete pavements and manually labeled for multiple surface distresses. Despite using the same base model to train individually, the performance of new models varies between the two datasets for the same pavement type. It remains a question whether transfer learning, knowledge distillation, or data merging will improve weaker models by leveraging stronger ones and increase the models’ robustness across different data sources. Experiments are conducted using these strategies to assess their applicability for single-source data (e.g., from a single vendor) and their generalization for multiple sources (e.g., from two or more vendors). This paper bridges a knowledge gap by qualitatively and quantitatively evaluating the effects of these strategies on current pavement distress detection practices using image data. Optimal approaches, such as fine-tuning or data merging, are recommended for various use cases aimed at real applications.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724839</guid></item><item><title>Resilient and Mechanistic Evaluation of Cement-Recycled Glass Treated Lateritic Soil for Sustainable Pavement Design</title><link>http://pubsindex.trb.org/view/2724837</link><description><![CDATA[Waste glass has shown promising results when used to stabilize soil, particularly in pavement applications. The elastic behavior of cement with recycled glass powder (RGP) in the lateritic soils has not been investigated yet. This study illustrates the potential use of RGP as a sustainable substitute for soil stabilization in urban pavement layers. The resilient behavior of clayey soils from southern Brazil, treated with high-early strength cement at contents of 3% and 6%, and RGP at contents of 3%, 6%, and 12%, was investigated. Resilient modulus (MR) tests were performed on untreated soil, soil–cement, and soil–cement-RGP specimens. Five MR prediction models were calibrated using data from repeated load triaxial (RLT) tests. Using Multiple Layer Elastic Analysis (AEMC) software, the useful life of an urban pavement was estimated, considering the properties of the subgrade, subbase, and base layers. Results showed that adding 3% RGP to cement-soil mixtures significantly improved the MR. Mechanistic analysis demonstrated that soil–cement-RGP mixtures C3RGP3 and C3RGP6 performed with a higher service life than cement-soil mixture C3, and soil–cement-RGP mixture C6RGP3 achieved a higher service life than cement-soil mixture C6. These findings underscore RGP’s effectiveness in enhancing pavement material properties. The results align with the integration of geotechnical engineering research and Brazilian national agencies, providing an effective alternative to traditional methods that benefits organizations such as the National Department of Transport Infrastructure, the National Land Transport Agency, the Road Research Institute, and highway contractors.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724837</guid></item><item><title>Streamlining Uniaxial Cyclic Fatigue Testing of Asphalt Mixtures Using a Mechanical Clamping System</title><link>http://pubsindex.trb.org/view/2724835</link><description><![CDATA[This study presents a patented collet–chuck-based clamping system for uniaxial cyclic fatigue testing of asphalt mixtures using small specimen geometry. Conventional specimen preparation is time- and resource-intensive, requiring precise cutting and the use of epoxy adhesives to mount specimens onto loading platens. These steps are critical for test success and typically require highly trained personnel, and they introduce delays because of epoxy curing. The proposed mechanical clamping system eliminates the need for cutting and epoxy adhesives, which allows rapid, repeatable specimen mounting, streamlining the workflow and reducing testing time. The system is compatible with a standard asphalt mixture performance tester and can be fabricated using commercially available, off-the-shelf components. To evaluate its applicability, eight asphalt mixtures from three different states were tested using the conventional glued-end-plate method and the collet–chuck clamping system. The mixtures covered a wide range of material characteristics, including different nominal maximum aggregate sizes (NMAS), binder types (unmodified, polymer-modified, and highly polymer-modified), binder contents, and reclaimed asphalt pavement percentages. The results showed strong agreement between both methods, with similar damage characteristic curves and comparable normalized variance index 𝙑𝘯𝘰𝘳𝘮 values. The collet–chuck system produced improved 𝙑𝘯𝘰𝘳𝘮  values in five of the eight mixtures. In addition, no statistically significant differences were observed in the failure criteria based on pseudo stiffness versus time curve, and apparent damage index parameter between the two systems, confirming that the proposed clamping approach is a viable alternative for uniaxial cyclic fatigue testing without compromising test integrity or results validity.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724835</guid></item><item><title>Investigating Vehicle Speed Interdependence in Work Zone Merging Scenarios</title><link>http://pubsindex.trb.org/view/2724833</link><description><![CDATA[Merging behavior in work zones with lane closures involves complex interactions, often among multiple vehicles. Although the merging maneuver of a vehicle is contingent on the behavior of surrounding vehicles, much of the existing research fails to consider the interdependence of these interactions. This oversight has resulted in a significant knowledge gap within the literature on work zone merging behavior. To address this gap, this study examines how the speeds of vehicles surrounding a merging vehicle, specifically the lead and following vehicles in both the open and closed lanes, collectively influence merging speed decisions. Video data from two work zones in New South Wales, Australia, were analyzed using a simultaneous equation modeling approach to capture the endogenous relationships among vehicle speeds. The results revealed significant interdependence, particularly between the merging vehicles and following vehicles in both closed and open lanes. The findings underscored the coordinated and interdependent nature of vehicle speed adjustments during lane merging, where the merging vehicle’s speed both influences and is influenced by surrounding traffic in their current lane (closed lane) and target lane (open lane). The use of a system-level modeling approach proved essential to capture these reciprocal dynamics and the temporal interplay among vehicle responses. These findings suggest that modeling merging decisions without accounting for these mutual influences may overlook critical safety concerns, especially in mixed-traffic environments involving heavy vehicles. The study provides new insights into vehicle interactions in work zones, with implications for improving traffic management and safety assessments in such settings.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724833</guid></item><item><title>Fabrication and Evaluation of a Superhydrophobic and Luminescent Emulsified Asphalt Coating for Road Applications</title><link>http://pubsindex.trb.org/view/2724828</link><description><![CDATA[Water damage and limited nighttime visibility are critical challenges for asphalt pavements, often leading to reduced durability and safety hazards. This study introduces a novel multifunctional coating combining superhydrophobic and luminescent properties, achieved through integrating innovative additive materials. The coating, designed to be applied over an emulsified asphalt, aims to enhance water repellency, minimize ice formation, and improve nighttime visibility without additional energy consumption. A factorial design approach was employed to evaluate the effects of additive dosage rates and spray duration on the coatings’ hydrophobicity, luminescence, and anti-icing performance. Results demonstrate significant improvements in hydrophobicity, with contact angles exceeding 150°, achieving superhydrophobicity in most cases. Work-of-adhesion values were substantially reduced, indicating superior ice repellency compared with the control. The luminescent properties of the coatings were optimized, with effective-luminescent-area ratios reaching 66.5%, enhancing visibility under low-light conditions. Anti-icing tests showed extended freezing times for coated surfaces, providing additional safety benefits during snowy or icy conditions. These findings indicate the potential of the modified coating as a sustainable, multifunctional solution for modern road infrastructure, particularly in regions prone to heavy rainfall, ice formation, and low visibility.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724828</guid></item><item><title>Using Connected Vehicle Data to Quantify Speeding Behavior on Large Networks</title><link>http://pubsindex.trb.org/view/2724774</link><description><![CDATA[This study demonstrates the use of connected vehicle data to conduct high-resolution speeding behavior analyses across a statewide highway network. Using over 3.5TB of Wejo trajectory data, we extract the maximum observed speeds for individual vehicles on defined road segments and aggregate them hourly to analyze temporal patterns. To assess excessive speeding, vehicle speeds are compared with posted speed limits across 13 thresholds, ranging from 35 to 90 mph, and sample sizes within each threshold are calculated. Based on this data, the paper presents a speeding index for identifying segments with potentially unlawful or unsafe speeding behavior. The analysis covers more than 120,000 road segments and considers differences by road type, day of the week, season, and holiday. Results reveal spatial and temporal variations in speeding intensity, including elevated rates during off-peak hours and certain holiday periods. When the Wejo sample size is sufficiently large, there is a good agreement with INRIX speed data. A web-based tool has been developed that enables users to dynamically explore excessive speeding patterns and download customized data sets. Findings highlight the value of connected car data as a supplement to traditional traffic data sets and offer insights for transportation agencies seeking to enhance safety and performance monitoring using emerging data sources.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724774</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>Assessing Moisture Susceptibility and Long-Term Leaching Behavior of Municipal Solid Waste Incineration Fly Ash–Modified Asphalt Mixtures</title><link>http://pubsindex.trb.org/view/2724618</link><description><![CDATA[The heavy metal (HM) content and limited disposal options of municipal solid waste incineration fly ash (MSWIFA) pose significant environmental challenges. However, fly ash offers potential as a sustainable modifier in asphalt pavements. However, moisture susceptibility and long-term leaching necessitate a comprehensive evaluation of the material before its use. This research assessed the moisture-induced sensitivity and long-term leaching behavior of MSWIFA-modified asphalt mixtures, focusing on their practicality in moisture-laden environments. Dense-graded (DG) and gap-graded (GG) asphalt mixtures were prepared using conventional and MSWIFA-modified asphalt binders. The MSWIFA modification significantly enhanced moisture resistance in both aggregate gradations. The DG mixture with modified bitumen (DG-MB) achieved the highest tensile strength ratio (TSR) of 86%. The GG mixture with modified bitumen (GG-MB) did not meet the 80% TSR threshold, but outperformed the conventional GG, highlighting the role of MSWIFA in improving binder stiffness and asphalt–aggregate bonding. Dynamic modulus |E*| test results showed reduced stiffness loss and a higher |E*| stiffness ratio (ESR) in the MSWIFA-modified asphalt mixtures with DG-MB exhibiting 18% to 21% stiffness loss and ESRs up to 84%. The wheel tracking test for high-temperature performance revealed reduced rut depths in MSWIFA-modified asphalt mixtures. The overall leaching remained well below the regulatory limits for MSWIFA-modified asphalt mixtures, demonstrating effective immobilization by the asphalt binder conglomerate. Specifically, DG-MB mixtures achieved better immobilization of HM leaching compared with GG-MB. Overall, incorporating MSWIFA into asphalt mixtures was found to be a viable strategy for safely managing HM leaching and substantially reducing the environmental risk.]]></description><pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724618</guid></item><item><title>Using Machine Learning to Understand Electric and Hybrid Vehicles Ownership in Burdened and Nonburdened Communities</title><link>http://pubsindex.trb.org/view/2724616</link><description><![CDATA[Transitioning to electric and hybrid vehicles (EHVs) for all communities is a pivotal step toward sustainable transportation and environmental conservation. This paper aims to understand the adoption of EHVs, focusing on burdened communities (BCs) in the United States. The EHV ownership-based analysis combines two datasets—behavioral data from the Puget Sound Regional Travel Survey integrated with BCs (Justice40) data covering transportation insecurity, environmental burden, social vulnerability, health vulnerability, and climate and disaster risk burden. After creating this unique database, descriptive analysis and modeling are used to analyze the data and predict EHV ownership in the future. Specifically, we use a new method that combines particle swarm optimization (PSO) with a stacking model named PSO-Stacking, which incorporates heterogeneous base learners of machine learning and deep learning. PSO applies a customized objective function to select the optimal hyperparameters for heterogeneous learners within the stacking model, effectively addressing challenges such as multicollinearity, data imbalance, nonlinearity, and overfitting. The proposed solution covers more accurate results than standard benchmark models for EHV ownership in BCs and non-BCs. In addition, the results of the PSO-Stacking method are explained using the local interpretable model-agnostic explanations technique. Results show a negative correlation between the BCs indicators, that is, higher transportation insecurity associated with lower EHV ownership. Furthermore, BCs have higher future climate risk scores, diesel particulate matter levels, and PM₂.₅ in the air than non-BCs because of higher conventional vehicle ownership. These communities are at higher risk and can benefit from electrification, EV infrastructure, and EV policies to address environmental challenges.]]></description><pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724616</guid></item><item><title>Driving Behavior Index and Early Detection of High-Risk Drivers Using Connected Vehicle Data</title><link>http://pubsindex.trb.org/view/2724614</link><description><![CDATA[This study presents a comprehensive approach to quantifying journey-level driving behavior using connected vehicle (CV) trajectory data. A count and severity-weighted driving behavior index was developed through confirmatory factor analysis, integrating acceleration, braking, speeding, and cruising metrics to assess the overall risk level of each trip. The index was applied to over 330,000 trips across Iowa, revealing a skewed distribution, indicating that while most trips involved moderate behavior, a notable subset exhibited aggressive patterns such as frequent speed limit violations and abrupt maneuvers. The study further explored whether behaviors in the first five minutes of a trip are associated with driving tendencies during the remainder of the journey. Using quantile regression, the analysis demonstrated that early trip cruising was consistently linked to safer driving, while early speeding and braking events were associated with elevated risk throughout the remainder of the journey. Acceleration variables showed relatively weaker associations with the remainder journey behavior. These associations varied across different trip durations, suggesting that trips of similar length should be compared when applying performance metrics or behavior-based scoring methods. These findings from this study support both reactive and proactive safety strategies by enabling real-time alerts during risky trips and post-trip identification of high-risk journeys for targeted feedback or intervention. As CV datasets continue to grow in coverage and quality, future work should explore collaborations with commercial operators and integrate high-frequency telemetry to enable proactive risk management.]]></description><pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724614</guid></item><item><title>Real Time Counterfactual Generation Trend Attack for Rear-End Collision Risk Prediction</title><link>http://pubsindex.trb.org/view/2724596</link><description><![CDATA[Although autonomous vehicles leverage perceptual information to execute downstream tasks such as prediction and control to enhance driving safety, they are vulnerable to cyberattacks. Attackers can manipulate model outputs by tampering with inputs. This study proposes a Counterfactual Generation Trend Attack (CGTA) framework for predicting rear-end collision risk. It manipulates inputs within the Key Feature Region using an optimization algorithm, aiming to either elevate (increase attack) or degrade (decrease attack) the predicted driving safety. Furthermore, considering scenarios involving partial knowledge stealing where model parameters are inaccessible, an attack scheme based on model distillation is proposed. After processing trajectory data and quantifying rear-end collision risk, experiments were conducted on three mainstream prediction models. The results demonstrate that: (1) CGTA can effectively execute real time targeted attacks. Simultaneously, the resistance to attack varies under attack directions and models. The deviation of model outputs from ground truth was from 2.87% to 54.75% under increased attacks and from −3.29% to −86.62% under decreased attacks; (2) the multi-head attention (MHA) exhibited superior attack resistance. The attack effects of the prediction models reduced by 41.69% to 86.10% after incorporating MHA; and (3) although the attack effectiveness under partial knowledge stealing decreased, it realized the target attack trend. In addition, this study provides a detailed sensitivity analysis of the parameters and verifies the generalization of the CGTA. The findings reveal the feasibility and effect mechanisms of targeted cyberattacks on autonomous driving systems, while offering theoretical support for defense against such attacks.]]></description><pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724596</guid></item><item><title>Analysis of Factors Affecting Underground High-Pressure Gas Pipelines under Heavy Vehicle Loads</title><link>http://pubsindex.trb.org/view/2721767</link><description><![CDATA[As the demand for fossil fuel energy grows in China, the construction of gas pipelines has expanded significantly. These pipelines often intersect with roadways, posing critical safety risks because of potential structural failures. This study focuses on the interaction zone between high-pressure gas transmission pipelines and roads, investigating the mechanical response of pipelines under heavy vehicle loading conditions. Using Abaqus software, a pipeline-road interaction model was developed to analyze the influence of vehicle speed, load magnitude, pipe wall thickness, burial depth, and laying angles on pipeline stress distribution. The findings reveal that: heavier vehicle loads increase peak stress and vertical displacement in pipelines; higher vehicle speeds reduce these mechanical responses; pipelines installed perpendicularly (90°) to roadways exhibit minimal stress; greater burial depth reduces stress and displacement, although this effect plateaus beyond a critical depth; and thinner pipe walls amplify stress and displacement, with wall thickness being the most influential factor.]]></description><pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2721767</guid></item></channel></rss>