<?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?subject=Highways" 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>Responsibility Attribution for Autonomous Vehicle Crashes Based on Causal Inference: Case Study in California, USA</title><link>http://pubsindex.trb.org/view/2709310</link><description><![CDATA[In the autonomous driving environment, the attribution of responsibility becomes complex when multiple crash parties and factors are involved. This study proposes a method to attribute the responsibility of the primary crash vehicle when human drivers and autonomous driving systems coexist, and apply it to the existing Autonomous Vehicles (AVs) crash data from 2019 to 2023 in California, USA. Firstly, a causal network is constructed by integrating the Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) methods to analyze the mutual impact of factors in the crash data. Secondly, Random Forest (RF) is used to obtain the feature importance in AV crashes. Based on the relationship between factors and the main responsible parties, the responsibility among relative stakeholders can be quantified. Under the research data in California, in autonomous driving mode, human drivers of the primary crash vehicle and software developers both account for 31% of the crash. Following behind are other stakeholders at 21% and vehicle manufacturers at 17%. On this basis, adjustments can be made to the responsible proportion in relation to a specific crash. By identifying the impact factors of AV crashes and responsibility attribution, this study offers important insights into safe autonomous driving tests, AV production regulation, and the development of crash responsibility policies. The methodology framework developed in this paper is universal and can be applicable to AV crash analysis in diverse regions and AV penetration rates.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709310</guid></item><item><title>Traffic Breakdown Prediction Beyond Stochastic Capacity Models: Machine Learning Approach</title><link>http://pubsindex.trb.org/view/2709302</link><description><![CDATA[This study focuses on the application of machine learning models for a short-term prediction of traffic breakdowns on freeways. Traffic breakdowns, which occur when demand exceeds the momentary capacity, are typically predicted using probabilistic methods, but these approaches do not fully capture the short-term variability inherent in traffic flow. In this work, the methodology is advanced by employing machine learning techniques to predict traffic flow conditions, relying exclusively on lane-by-lane analysis of current detector data without utilizing any upstream or downstream information. Traffic conditions are classified into distinct categories, including breakdowns, and a neural network is employed to predict them, providing a robust method for identifying intervals in which the momentary capacity of a freeway is reached. Capacity estimates from the neural network are then compared with those from widely accepted statistical methods, revealing minimal differences, and thereby validating the effectiveness of the neural network approach in capacity analysis. Moreover, comparing the short-term flow conditions predicted based on the two approaches revealed superiority of neural network in providing significantly more accurate classifications. These findings highlight the significant potential of machine learning methods as powerful tools for momentary capacity estimation, with applications across various transportation systems management and operations strategies.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709302</guid></item><item><title>Optimized Laboratory Fabrication of Small-Specimen Geometry for Streamlining Dynamic Modulus and Cyclic Fatigue Testing of Asphalt Mixtures</title><link>http://pubsindex.trb.org/view/2709230</link><description><![CDATA[The asphalt community is focused on the paradigm shift in mixture design from the volumetrics to an optimization procedure based on performance testing called balanced mixture design. Streamlining performance testing to obtain index properties quickly and using a smaller quantity of materials is critical for the successful implementation. This paper aims to streamline dynamic modulus (|E*|) and cyclic fatigue testing by optimizing the number of 38 mm diameter specimens extracted from a single 150 mm diameter Superpave gyratory-compacted (SGC) specimen. The current provisional standard methods require vertical coring of four small specimens from a single SGC specimen. In this study, two sets of testing specimens were fabricated by coring four and five small specimens from each SGC specimen. The success rate in meeting target air voids, the |E*| analysis, and the cyclic fatigue results including cyclic fatigue index parameter (Sₐₚₚ) values were compared between the two sets of specimens. No significant or consistent differences were observed in performance testing results. Furthermore, innovative image analysis and microscopy techniques were used to study air voids distribution and aggregate structure within each specimen and to further validate the proposed coring pattern. Based on these findings, coring five 38 mm diameter testing specimens from one SGC sample is suggested to run |E*| and cyclic fatigue tests. This proposed modification to AASHTO TP 132 and TP 133 may save technicians’ time and allows for the optimal use of materials. The latter may become a significant saving when integrating these methods with laboratory long-term aging protocols and forensic studies.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709230</guid></item><item><title>Mechanistic Analysis and Design Framework for Geosynthetic Stabilized Unpaved Roads</title><link>http://pubsindex.trb.org/view/2709229</link><description><![CDATA[Geosynthetics provide mechanical stabilization benefits to paved or unpaved roads through lateral restraint of unbound aggregate particles and bearing capacity improvement over weak subgrades. The current state of the art incorporating geosynthetics into paved or unpaved road design involves conducting proper elastic layered system mechanistic analysis to determine the improvement of aggregate layer stiffness for increased traffic capacity or reduction in aggregate layer thickness. This paper presents a mechanistic analysis and design pipeline for determining the required aggregate thickness via the finite element (FE) modeling approach. An advanced FE analysis tool, C-FLEX, was employed to analyze axisymmetric multilayered unpaved road structures, accounting for the nonlinear stress-dependent behavior of unbound aggregates. The modulus enhancements were quantified for 10 different geosynthetics using the latest Bender Element sensor technology in both triaxial and large-scale tests conducted on typical dense-graded base aggregates. They were then incorporated into base course stiffness characterization via a sublayering approach for the unpaved road comprising aggregate base placed over soft subgrade. Both the measured enhanced moduli and the the extent of geosynthetic influence zones were adequately established in the sublayering approach. Further, sensitivity analysis was conducted for different aggregate modulus models and different sublayer structures, which verified the proposed design pipeline to provide satisfactory results. The method was also compared with the Giroud and Han method, which revealed the inherent difference in the two methods, given that the design here is based on the critical pavement responses and subgrade strength, while the Giroud and Han method also incorporated the field data with performance evaluation.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709229</guid></item><item><title>Enhancing Data Accessibility through Automated Personally Identifiable Information De-Identification in Crash Narratives</title><link>http://pubsindex.trb.org/view/2709228</link><description><![CDATA[Unstructured crash narratives in police reports contain rich textual information that can uncover key insights into crash circumstances, such as contributing factors and driver behavior, that are often missing from the structured fields of crash data. However, the presence of personally identifiable information (PII) within these narratives, and the lack of scalable, domain-specific redaction tools, limit their broader use because of privacy concerns and legal restrictions. To address this challenge, a scalable, privacy-preserving pipeline for automated PII de-identification from crash narratives was developed and evaluated. The proposed method utilizes a generalist model for named entity recognition using bidirectional transformer (GLiNER), which is known for its strong zero-shot, few-shot, and fine-tuned performance across diverse entity types. The model was fine-tuned on a manually annotated training set to adapt it to the crash narrative domain. It was found that combining this fine-tuned named entity recognition model with a rule-based post-processing module improved PII detection performance by resolving span misalignments and recovering entities that were initially missed. Evaluation on a test set achieved an F1 score above 80%, particularly for frequent PII categories such as names and addresses. Post-processing further reduced false negatives by 32%. The pipeline was developed and tested on local machines to ensure data confidentiality. Additionally, the workflow supports accessibility and future use through GLiNER-Studio, a user-friendly tool that enables non-programmers to fine-tune models on new datasets. This study contributes a practical solution to the need for automated PII de-identification in transportation safety data, enabling secure data sharing and ethical analytics for research and policymaking.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709228</guid></item><item><title>Exploring Aggregate Morphological Characteristics under Laboratory Polishing for Enhanced Pavement Skid Resistance</title><link>http://pubsindex.trb.org/view/2709130</link><description><![CDATA[As the use of recycled asphalt pavement (RAP) in pavement construction grows for sustainable development, it becomes essential to investigate potential frictional deterioration over time. This study evaluated the friction properties of recovered RAP material aggregates compared with raw aggregates across various polishing cycles. The micro-Deval test was employed to simulate aggregate loss of texture, while morphological and friction properties were measured using an aggregate imaging measurement system (AIMS-II), along with a British pendulum tester (BPT) and dynamic friction tester (DFT). Additionally, Fourier transform infrared spectroscopy (FTIR) was employed to assess its potential in determining the origin and composition of RAP material aggregates. A simple method was used to fabricate custom aggregate rings, allowing for accurate testing in the DFT setup. The aggregate testing results revealed notable variations across the measurement techniques. AIMS-II analysis showed that traprock (maroon-colored) exhibited the highest surface texture, while DFT and BPT results indicated that certain limestones outperformed traprock in friction properties. Additionally, the testing results demonstrated that the RAP materials were comparable to, or even outperformed, certain limestone sources. However, because of potential variability within RAP stockpiles, careful quantification is necessary to assess their suitability. FTIR analysis demonstrated its ability to distinguish between carbonate-rich and silica-rich aggregates; however, further research is needed to build a library of aggregate sources. Finally, a machine learning algorithm identified the loss of aggregate DFT₂₀ values as the most significant aggregate property representing friction loss in asphalt mixtures.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709130</guid></item><item><title>Evaluating the Safety Impact of Roadway Rightsizing in Jefferson County, Kentucky</title><link>http://pubsindex.trb.org/view/2709128</link><description><![CDATA[This research provides a safety assessment of rightsizing projects that took place in Jefferson County, Kentucky. Rightsizing has become increasingly popular as a solution for multimodal access improvements and enhancing roadway safety. A cross-sectional before–after analysis was applied to a 15-year panel dataset from 2010 to 2024 to estimate the impact of rightsizing on crash frequency. A matched control group was developed using traffic volume and segment length using nearest-neighbor approach. Negative binomial safety performance functions were estimated with untreated sites and adjusted with annual calibration factors for seasonal changes consideration. Empirical Bayes methods were applied to correct for regression-to-the-mean bias and estimate counterfactual crash frequencies. Crash modification factors (CMFs) were calculated and disaggregated by crash type (all, bicycle, pedestrian, and intersection-related) and severity level (KA, BC, O). The analysis reveals that rightsizing treatments were associated with a 32% reduction in fatal and severe injury crashes, and consistent crash reductions at intersections. However, elevated CMFs across all severity levels for bicycle crashes suggest increased risk, potentially because of higher exposure without corresponding protective infrastructure. Pedestrian findings varied by severity level. The findings highlight crash severity reduction potential for rightsizing while indicating a requirement for including facilitative infrastructure for protection of vulnerable road users. The study includes practical recommendations for transportation agencies considering rightsizing as part of a broader safety and multimodal mobility initiative.]]></description><pubDate>Tue, 02 Jun 2026 11:01:49 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709128</guid></item><item><title>Comparison of Pavement Roughness Indicators from Traditional Inertial Profilers and Emerging Connected Vehicle Sensors</title><link>http://pubsindex.trb.org/view/2709233</link><description><![CDATA[This study is an evaluation of the consistency and potential of crowdsourced connected vehicle (CV) data as an alternative to traditional inertial profiler (IP) measurements for road roughness evaluation. IP data were collected from three roadway corridors, SH 21, SH 6, and FM 2818, in Bryan, Texas, which comprised a variety of pavement types and functional classifications. Lane-specific international roughness index (IRI) values were recorded using a calibrated inertial profiler and analyzed at every 10 ft. These were compared with direction level ride values collected at an average spacing of 82 ft from a third-party CV data provider. A general agreement was found between IP and CV data on asphalt and concrete surfaces, but there were substantial differences between seal coat sections, where CV ride values overestimated roughness by 80–100 in./mi. A grouping analysis was conducted to compare segments categorized by roughness level from both datasets. The results showed a moderate match in identifying the roughest segments by the CV system, compared with those identified by IP. This match rate varied by profile and improved with broader grouping sizes. Results show that CV data might have greater sensitivity to vehicle behavior (for example, braking at intersections), while IP-based systems are calibrated to record actual road roughness. This could be one of the reasons for the moderate mismatch in identifying rougher segments using the two methods. Despite these limitations, the CV system’s high frequency of measurements, especially on highways, demonstrates its potential for cost-effective, network-level pavement monitoring.]]></description><pubDate>Mon, 01 Jun 2026 16:52:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709233</guid></item><item><title>Cold-in-Place Recycling with 100% Recycled Asphalt Pavement Rejuvenated by Soybean Oil: Laboratory and Field Evaluation</title><link>http://pubsindex.trb.org/view/2709232</link><description><![CDATA[This study investigated the feasibility and performance of using soybean oil as a bio-based recycling agent in recycled asphalt pavement (RAP) for road reconstruction in cold regions. A comprehensive demonstration project was conducted on a 5-mile section of Old State Road in Clare County, Michigan, where a 100% RAP mixture modified with soybean oil was produced and placed using a conventional asphalt paver equipped with a screed. Laboratory evaluations included balanced mix design, rutting and cracking testing, and binder performance analysis. Field application processes, including mixing and compaction, were also documented and evaluated. The asphalt mixture tests included the Hamburg wheel-tracking test (HWTT) and the indirect tensile asphalt cracking test (IDEAL-CT), while the asphalt binder tests included dynamic shear rheometer (DSR), asphalt binder cracking device (ABCD), rotational viscometer (RV), linear amplitude sweep (LAS), Fourier transform infrared spectroscopy (FTIR), and CO2 emission analysis. An optimal soybean oil dosage of 1.0 wt.% (based on the total weight of the mix) significantly improved low-temperature cracking resistance and fatigue life while maintaining rutting resistance. Results showed that soybean oil improved compaction performance and exhibited a cracking temperature approximately 3.3°C lower than that of untreated RAP based on the ABCD test. Fatigue performance was also enhanced. Fourier transform infrared spectroscopy (FTIR) analysis confirmed the chemical compatibility and interaction between soybean oil and the RAP binder. On-site application was completed smoothly without workability issues, and the final pavement met all compaction and density requirements. In summary, using soybean oil as an RAP recycling agent provides a practical and environmentally friendly solution to improve the performance of recycled asphalt mixes, especially for low-volume roads in cold climates, while supporting the sustainability of Michigan’s pavement and the growth of the soybean market.]]></description><pubDate>Mon, 01 Jun 2026 16:52:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709232</guid></item><item><title>Examining the Role of the Built Environment in Cycling Injury Severity: Older Adults (60+) Versus Individuals Aged 10 to 59 in a Super-Aged City</title><link>http://pubsindex.trb.org/view/2706341</link><description><![CDATA[Cycling offers well-documented benefits, including reduced congestion and air pollution, enhanced mobility, and improved physical health. Reflecting these advantages, cycling participation has increased across all age groups in many developed countries. However, this growth has been accompanied by a rise in cycling crashes, raising significant urban safety concerns—particularly for older adults. Although numerous studies have investigated factors influencing the injury severity of cycling crashes, the built environment has consistently emerged as a key determinant. Nevertheless, limited research has specifically explored how micro-level built-environment characteristics are associated with the injury severity of bicycle crashes, especially among older adults. This study investigates the association between built-environment characteristics and the injury severity of bicycle crashes involving older adults, analyzing 10,502 crash cases in Seoul from 2018 to 2023 using a binomial logistic regression model. To capture detailed built-environment attributes, we applied DeepLabV3+ for semantic segmentation of Google Street View images collected from four directions at each crash location. The results indicated that higher proportions of road surfaces, obstacles, and vegetation were associated with increased injury severity among older adults (60+), whereas the presence of traffic devices reduced injury severity. Among individuals aged 10 to 59, greater building density was linked to lower injury severity. A common risk factor across both age groups was collisions with motor vehicles. These findings underscore the necessity of age-sensitive safety interventions. For older adults in particular, measures such as separating cycling paths from obstacles and increasing the installation of traffic control devices may help improve cycling safety.]]></description><pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706341</guid></item><item><title>Safety Risks of Out-of-Context Curves: Three Decades of Rural Curve Research in New Zealand</title><link>http://pubsindex.trb.org/view/2706340</link><description><![CDATA[The rural road network of New Zealand contains many horizontal curves that are inconsistent with their surrounding environment. These “out of context” curves—where the safe negotiation speed is significantly lower than the prevailing approach speed—are associated with higher crash risk than in-context curves of otherwise similar geometry. Over the past three decades, New Zealand researchers and transport agencies have developed a robust body of work to understand and address this issue, although it remains underexplored internationally. This paper reviews the evolution of rural curve safety research in New Zealand, including the development of high-resolution road geometry datasets, operating speed models, and crash prediction models. It also highlights how these insights have informed national project evaluation guidance and safety prioritization frameworks, and a recent adaptation of this research to international contexts, including the United States. In particular, it was found that traditional crash prediction models such as those in the Interactive Highway Safety Design Model and the Highway Safety Manual can underestimate the observed number of crashes around out-of-context curves by at least 30%, and potentially up to 60%. The recent application of New Zealand curve context modeling to U.S. rural roads through the SafeCurves software tool addresses this limitation. This review aims to demonstrate the value of incorporating curve context into safety analysis and prioritization, highlight New Zealand research in this area, and encourage broader application of these methods to reduce crash risk on rural roads worldwide.]]></description><pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706340</guid></item><item><title>Toward Asphalt Pavement Construction Safety Improvement with Generative Artificial Intelligence</title><link>http://pubsindex.trb.org/view/2706339</link><description><![CDATA[Recent advancements in generative artificial intelligence (GenAI), particularly large language models (LLMs), have shown promise in enhancing safety analysis within the construction industry. This study explores the integration of structured information from the U.S. Pennsylvania Department of Transportation’s job safety analysis (JSA) documents with unstructured accident narratives from the U.S. Occupational Safety and Health Administration’s Integrated Management Information System (IMIS), focusing on asphalt pavement construction—a sector marked by complex operations and hazardous equipment. Multiple LLMs were employed to classify accident narratives across four dimensions: construction type, relevant JSA job and step, environmental or operational influence, and hazard type. This approach aims to identify frequently cited work activities, assess gaps in safety documentation, and improve future hazard recognition. While general job classification achieved moderate success, performance declined for step-level and contextual classifications, largely because of ambiguous language and overlapping job responsibilities in the narratives. Despite these limitations, LLMs uncovered critical patterns. Paver and roller operations emerged as high-risk activities, often influenced by environmental factors such as traffic or weather. Furthermore, exploratory hazard analysis revealed that model-suggested hazard labels were sometimes more contextually appropriate than those in the original IMIS database, indicating opportunities for data refinement. By aligning structured safety documentation with real-world incidents through GenAI, this study highlights a novel pathway for data-driven safety planning in highway construction. While expert oversight remains essential, the results demonstrate the potential of LLMs to support more adaptive, targeted, and proactive approaches to risk assessment and mitigation.]]></description><pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706339</guid></item><item><title>Max-Pressure Signal Control with Transit Priority and Lane Blockage Mitigation: Considering Dwelling Buses and Permitted Left Turns</title><link>http://pubsindex.trb.org/view/2706338</link><description><![CDATA[Urban traffic congestion remains a critical issue, intensifying with ongoing urbanization and increasing traffic volumes. While “max-pressure” (MP) control has emerged as a robust, decentralized strategy for optimizing intersection signals based on real-time queue dynamics, it traditionally overlooks transit vehicles and real-world complexities such as lane blockages (LB) because of buses dwelling at stops and permitted left-turn movements. This paper introduces an innovative extension of the MP paradigm for right-hand traffic systems, termed “MP-TSP-LB,” which explicitly integrates transit signal priority (TSP) through weighted priority schemes and accounts for LB caused by both dwelling buses and permitted left turns. The proposed approach modifies the conventional MP framework by introducing weight-based prioritization of buses without disrupting overall network stability. Additionally, the model incorporates effective lane capacities by estimating blockage probabilities, applying critical gap acceptance theory for permitted left turns and probabilistic dwell-time distributions for buses. The MP-TSP-LB model was rigorously tested through simulation on a 3 × 3 grid network using the Simulation of Urban Mobility simulation environment. Results indicate that the MP-TSP-LB model significantly enhances network performance across multiple metrics compared with baseline MP formulations. Sensitivity analysis demonstrates that the model reduces total waiting times and increases average travel speeds for both private vehicles and transit across various demand levels. Incorporating permitted left turns effects significantly improves performance under low-to-moderate demand levels, while modeling dwelling bus blockages becomes increasingly effective as transit service frequency intensifies.]]></description><pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706338</guid></item><item><title>A Physics-Guided Feature Fusion Network for Vibration-Based Bridge Scour Monitoring</title><link>http://pubsindex.trb.org/view/2706334</link><description><![CDATA[Bridge scour poses a significant risk to infrastructure safety, yet traditional underwater monitoring methods are often unreliable and impractical during critical flood events. To address these limitations, this study proposes a physics-guided feature fusion network (PG-FFN) for estimating scour depth from bridge pier acceleration data. The PG-FFN features a novel dual-branch architecture design. A temporal feature branch uses a bidirectional long short-term memory (BiLSTM) network to extract latent patterns from raw vibration signals. Concurrently, a physical feature branch processes a set of engineered, physics-based descriptors using a multilayer perceptron. These distinct feature sets are integrated through a fusion module to produce a comprehensive representation of the structural response. To ensure the model’s predictions adhere to fundamental physical principles, a composite loss function is introduced to strictly enforce monotonicity constraints, while the non-negativity of the scour depth is explicitly guaranteed by the network architecture. For proof of the concept, the proposed model was trained and evaluated on a comprehensive synthetic data set generated from a calibrated numerical model. Results show the PG-FFN achieved a normalized root mean square error (NRMSE) of 7.60% on the unseen test set, representing a 33% improvement over a standard baseline BiLSTM model, which yielded an 11.31% NRMSE. An ablation study confirmed that the feature fusion mechanism was the primary contributor to this enhanced performance. Moreover, field validation on a real flood event demonstrated the model’s robustness in capturing scour trends under practical conditions. The findings demonstrate that integrating physical constraints and domain knowledge with deep learning provides a more accurate and physically consistent framework for vibration-based bridge scour monitoring.]]></description><pubDate>Thu, 28 May 2026 10:47:37 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706334</guid></item><item><title>Improving Pavement Sustainability and Resilience: Pavement Life Cycle Cost Analysis Case Study</title><link>http://pubsindex.trb.org/view/2706168</link><description><![CDATA[The effects of climate change and extreme weather have caused flooding and inundation of many roadways, resulting in numerous pavement failures and negatively affecting the condition and functionality of pavement networks. This has driven a growing focus on creating resilient infrastructure, including the use of rigid pavement sections in flood-prone areas. The higher cost of these solutions requires justification through performance and economic analyses. In this study, a life cycle cost analysis (LCCA) of rigid and flexible pavement designs is conducted using the North Carolina pavement design procedure to develop equivalent performance across alternatives. Initial and long-term costs were evaluated using deterministic and probabilistic methods. Findings suggest that optimized jointed plain concrete pavement can be a cost-effective alternative in widened lane configurations without shoulder drains. Unbonded concrete overlays also proved to be competitive when rigid pavement pricing remained stable. The high variability in rigid pavement pricing remains a concern. Probabilistic LCCA results revealed that including shoulder drains significantly influences cost-effectiveness. Rigid pavements with high truck traffic volume are required to have shoulder drainage in accordance with the agency's pavement design guidance. In this study, the LCCAs for the rigid pavement alternatives were analyzed with and without shoulder drainage to determine its impact on the cost-effectiveness of rigid pavement. Full-depth asphalt was the most economical option in over 80% of cases when shoulder drains were included, whereas rigid alternatives became more competitive when drains were excluded. Results underscore the need for a balanced approach in pavement design requirements and competitive bidding environments to enhance sustainability, resilience, and cost-effectiveness in pavement investments.]]></description><pubDate>Wed, 27 May 2026 13:06:57 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706168</guid></item></channel></rss>