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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. 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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Revolutionizing Coastal Infrastructure Durability with Pervious Concrete: A Cost-Effective, High-Performance Seawall</title>
      <link>https://rip.trb.org/View/2696019</link>
      <description><![CDATA[This project develops and validates a pervious concrete seawall system to reduce wave loads and mitigate scour-related degradation at lower cost and maintenance demand. The work integrates (i) high-fidelity finite element analysis for preliminary design, (ii) fabrication of pervious concrete with tuned porosity (15–35%) using durability-enhancing binders and engineered biochar, (iii) controlled wave flume experiments with instrumented specimens and backfill monitoring, and (iv) seawall design optimization accelerated by surrogate model and genetic algorithm.
To achieve the above mentioned integration, the research will proceed through a series of coordinated actions. First, the research team will build a high-fidelity finite element model, analyze the wave load in seawall, and achieve a preliminary design. Next, pervious concrete specimens with controlled porosity will be fabricated using the preliminary design and tested in a wave flume, which simulates real coastal conditions by generating programmable waves and measuring forces, displacements, and backfill scour behind the seawall. Finally, the team will apply a HyperNetwork, a neural architecture that dynamically generates predictive models, to estimate performance metrics such as energy dissipation and structural stability across different design configurations. The research team has rich experience in developing surrogate models for engineering applications and will complete building this HyperNetwork-based surrogate model in six months. This HyperNetwork will be used together with a genetic algorithm to search for Pareto-optimal designs that balance durability, hydraulic efficiency, and cost. This integrated approach ties together physical testing and advanced modeling to deliver practical, field-ready guidance with the objective of reducing wave-driven degradation and improving structural resilience in simple, cost-effective terms.
]]></description>
      <pubDate>Thu, 23 Apr 2026 16:44:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696019</guid>
    </item>
    <item>
      <title>Unraveling the Causes of Fatal Crashes in the U.S.: A Machine Learning Approach to Safer Roads</title>
      <link>https://rip.trb.org/View/2694440</link>
      <description><![CDATA[This project investigates the underlying causes of fatal traffic crashes in the United States using advanced machine learning (ML) techniques to enhance road safety. Each year, traffic crashes claim over 42,000 lives nationwide, inflicting significant social, economic, and health burdens. Traditional analytical methods have struggled to capture the complex, nonlinear interactions among factors such as driver behavior, vehicle characteristics, roadway design, and environmental conditions. To address this limitation, this project employs data-driven ML models to identify key determinants of fatal crashes and generate actionable insights for evidence-based safety interventions.

The research activities will proceed in four phases. First, comprehensive crash data will be collected from the National Highway Traffic Safety Administration (NHTSA) and integrated across multiple datasets to ensure completeness and consistency. Next, statistical analysis and visualization will be used to identify spatial and temporal trends in crash patterns, revealing geographic disparities and risk concentrations. In the modeling phase, several machine learning algorithms—Balanced Bagging, Balanced Random Forest, and RUSBoost—will be developed and compared against traditional logistic regression models to enhance prediction accuracy in imbalanced datasets. Finally, the top-performing model will be used to assess variable importance and generate policy-relevant recommendations.

OBJECTIVE: The objective of this project is to develop predictive models that accurately identify risk factors associated with fatal crashes and support data-informed decision-making by transportation agencies. The findings will guide targeted interventions such as improved traffic regulations, safer roadway designs, and enhanced vehicle technologies. This research will provide a scalable analytical framework for improving transportation safety and sustainability nationwide.
]]></description>
      <pubDate>Tue, 21 Apr 2026 13:45:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2694440</guid>
    </item>
    <item>
      <title>A Data-driven Approach in Improving Truck Parking Efficiency</title>
      <link>https://rip.trb.org/View/2684213</link>
      <description><![CDATA[Freight transportation systems are a critical component of the United States' economy, underscoring the importance of adequate truck parking to ensure safe and efficient operations. However, a significant disparity between truck parking demand and supply has resulted in numerous challenges, including increased road safety risks, regulatory non-compliance, and operational inefficiencies. This study aims to address this knowledge gap by conducting a comprehensive review of current truck parking management approaches, with a focus on data-driven prediction models, and truck parking pattern analysis. In collaboration with the North Carolina Department of Transportation (NCDOT), the study will analyze truck parking patterns along key freight corridors and develop data-driven solutions to enhance parking efficiency and address these pressing challenges.

This project aims to address this gap by conducting a comprehensive review of existing literature and offering a nuanced exploration of potential truck parking solutions. Using NC as a case study, the project will provide data-driven recommendations to improve the efficiency and utilization of existing parking facilities along key freight corridors. By enhancing the safety and efficiency of truck parking, this study will directly benefit truck operators, supply chain stakeholders, regulatory agencies, and local communities. The findings will serve as a foundation for informed policymaking and infrastructure planning, ensuring that North Carolina’s freight transportation network remains resilient, sustainable, and operationally efficient in the face of growing demands.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:16:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684213</guid>
    </item>
    <item>
      <title>Strategic Investment Choice to Reduce Disruptions and Increase Resiliency of Roadway
Freight Network</title>
      <link>https://rip.trb.org/View/2684218</link>
      <description><![CDATA[The proposed research will develop models and algorithms to identify systematic investment strategies by reducing link disruption failure probabilities and enhancing overall roadway resilience for freight flows. A new stochastic programming modeling framework will be developed in which disruption probabilities depend on resource allocation decision variables and new algorithms will be developed to deal with the computational challenges caused by both the large number of scenarios and the nonlinearity in both first-stage and second-stage sub-problems. The framework, including data integration, models, and solution methods, will be programmed and tested with a case based on the freight network in the State of Tennessee.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:46:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684218</guid>
    </item>
    <item>
      <title>Fusing Shipper Behavior Models between Markets and Approaches</title>
      <link>https://rip.trb.org/View/2684219</link>
      <description><![CDATA[Understanding shipper behavior is critical for informed freight transportation planning and policy development. Despite the availability of various modeling approaches—including traditional analytical methods and emerging artificial intelligence (AI) techniques—significant variability persists across commodity types, shipment distances, and market scales. This project addresses the need for a unified and systematic framework to compare, integrate, and enhance shipper behavior models. Building on the complementary expertise of the Principal Investigator (PI) and Co-Principal Investigator (Co-PI), the study will conduct comparative analyses of existing models, focusing on the integration of AI-based and analytical approaches such as multinomial logit (MNL) models. The research will examine model performance across diverse market conditions and geographies, using the Commodity Flow Survey (CFS) data as a foundational resource. Emphasis will be placed on developing fusion techniques to bridge methodological gaps and improve predictive accuracy, particularly in the face of imbalanced datasets common in freight data. By unifying modeling strategies and addressing data limitations, this work aims to deliver a robust framework with enhanced generalizability and practical utility. The expected outcomes include improved forecasting tools, better policy support, and more effective use of publicly available data for national and regional freight planning efforts.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:39:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684219</guid>
    </item>
    <item>
      <title>Enhancing Heavy Vehicle Crash Prevention in North Dakota through Machine Learning and Weather Data Integration</title>
      <link>https://rip.trb.org/View/2683255</link>
      <description><![CDATA[Heavy vehicle crashes continue to be a persistent safety concern across the Midwest, with several states reporting disproportionately high rates of incidents involving large trucks. According to the National Safety Council, in 2023, North Dakota recorded 18% of its fatal crashes involving large trucks, placing it among the highest in the nation. Neighboring states, such as Nebraska (16%) and Iowa, also face elevated risks. Illinois reported 7,509 truck accidents in 2022, ranking among the top five states nationwide. In North Dakota, the risks are especially pronounced during the winter months. In 2023, 64% of heavy vehicle crashes occurred between October and March, with 81% of these crashes taking place in rural areas. These figures highlight how weather conditions and geography amplify the risk associated with large-truck travel in the region. Further, crashes in rural areas in challenging weather conditions poses immense issues for first responders and their ability to provide timely medical care to crash victims.   

Traditional safety strategies have struggled to account for the dynamic, real-time factors that contribute to crash risk. Static approaches often fall short when adverse weather, road conditions, and traffic volume interact in unpredictable ways. This gap highlights the urgent need for predictive, data-driven solutions.  

This proposal aims to investigate the application of machine learning (ML) models, combined with weather and crash data, to predict high-risk scenarios before accidents occur, to support planning for safety and emergency response needs. By leveraging predictive analytics, North Dakota could enhance resource allocation, deploy preventive interventions, and reduce the frequency and severity of heavy vehicle crashes. The high incidence of winter crashes and the limitations of conventional methods make North Dakota an ideal proving ground for an innovative, ML-driven approach to roadway safety.  

The study will utilize historical crash records for heavy vehicles in North Dakota, including crash type, severity, date, and time, combined with corresponding weather data such as temperature, precipitation, snowfall, and visibility. Feature engineering will create representations of temporal and weather conditions relevant to crash severity. Machine learning models, including Random Forest, XGBoost, and Neural Networks, will be trained to predict crash severity. To ensure interpretability, SHAP (SHapley Additive exPlanations) will be applied to quantify the contribution of each feature to individual predictions and overall model behavior. This analysis will reveal which weather or temporal factors most strongly influence severe crashes, both globally across the dataset and locally for specific incidents. High-risk periods and conditions identified by the model, along with explanations provided via SHAP, will be visualized both temporally and geographically, offering actionable insights to support targeted preventive measures and inform DOT decision-making.  ]]></description>
      <pubDate>Tue, 24 Mar 2026 14:09:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683255</guid>
    </item>
    <item>
      <title> Simulating Accessibility from CAVs and ICTs (SACI)</title>
      <link>https://rip.trb.org/View/2680126</link>
      <description><![CDATA[Simulating Accessibility from CAVs and ICTs (SACI) develops a simulation tool that helps transportation agencies understand and plan for the transformative impacts of connected and automated vehicles (CAVs) and information and communication technologies (ICTs) on travel behavior and network demand. As CAVs and ICTs reshape how people choose destinations and routes, new models are needed to predict future demand and usage. The project develops a framework that captures cognitive, perceptual, and behavioral effects of CAVs and ICTs, implements it in an agent-based model using SILO, MITO, and MATSim simulation components, and packages the result as a software tool for use by state, regional, and local DOTs. The model uses multimodal transportation network data from the DC, Maryland, and Virginia region to assess how CAV-ICT deployment affects travel patterns and land use across the region.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:25:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2680126</guid>
    </item>
    <item>
      <title>UAM-enabled Multimodal Analysis of Transportation Systems for LA28 and beyond</title>
      <link>https://rip.trb.org/View/2676008</link>
      <description><![CDATA[The Los Angeles region has long been projected as a testing ground for urban air mobility (UAM), comprised of air taxis and drone delivery, given the region’s favorable climate, traffic problems, and tech-savvy ecosystem. The LA28 Olympic and Paralympic Games present an opportunity to make such a testing ground a reality. This project will model the potential for mode shifts, from ground to air taxi modes, with the LA28 Games as an initial case study. Modeling mode shift requires modeling the operation of an air taxi system. For that reason, this project will develop algorithms for optimal dispatch operation of a network of air taxis during LA28 and thereafter, and use those results to study the resulting mode shift from other ground-based modes of transportation. The results of this research can inform the work of the White House Task Force on the 2028 Summer Olympics (Established by Executive Order 14328), which includes the Secretary of Transportation. The results will also be relevant to both the public and private sector entities planning Olympic Games travel. By developing improved dispatch operation models for air taxis in a major urban area, and then predicting mode shifts from/to other ground modes, this research will also develop knowledge that will be helpful throughout Region 9 and the U.S. and which can help accelerate the maturing of the air taxi sector.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:34:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676008</guid>
    </item>
    <item>
      <title>Investigate Wisconsin Bridge Scour in Mobile (Alluvial) Sand-Bed Rivers</title>
      <link>https://rip.trb.org/View/2671987</link>
      <description><![CDATA[The primary objective of this research is to enhance scour prediction accuracy for bridges in Wisconsin’s mobile sand-bed rivers by developing region-specific scour envelope curves. The proposed study will address the limitations of existing scour prediction methods by incorporating Wisconsin’s unique hydraulic, geomorphic, and sedimentological conditions. By refining existing scour envelope methodologies and tailoring them to Wisconsin’s river systems, this research aims to improve scour estimation accuracy, reduce unnecessary costs, and enhance long-term bridge safety and maintenance planning. In order to provide guidance for determining the probable depth of scour under various hydraulic, geological, and structural conditions, FHWA developed Hydraulic Engineering Circular No. 18 (HEC-18): Evaluating Scour at Bridges (Richardson & Davis, 2012). HEC-18 has served as a technical standard for bridge scour analysis in the United States. It establishes a comprehensive methodology for evaluating scour at bridge foundations, including pier scour, contraction scour, and abutment scour. Additional documents are also available, including HEC-20: Stream Stability at Highway Structures (Lagasse, Schall, et al., 2001) and HEC-23: Bridge Scour and Stream Instability Countermeasures (Lagasse, Zevenbergen, et al., 2001).These documents are also adopted by the Wisconsin Department of Transportation (WisDOT) as the standard procedures for bridge scour analysis. To address the limitations of HEC-18, many state DOTs (e.g., Minnesota, Iowa, New Jersey, South Carolina) have developed regional modifications or supplemental procedures to enhance scour prediction models. While many state DOTs have developed regional modifications for HEC-18, or regional scour envelope curves, Wisconsin has not yet established a dedicated set of scour envelope curves tailored to its river systems. This study seeks to refine scour prediction in Wisconsin’s rivers by supplementing HEC-18’s methodology with locally derived scour envelope curves, improving the accuracy of scour predictions and optimizing bridge foundation designs.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:26:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2671987</guid>
    </item>
    <item>
      <title>Using Large Language Models to Generate Synthetic Data for Proactive
Pedestrian Safety Prediction: Overcoming Data Collection Barriers in Surrogate Safety Analysis</title>
      <link>https://rip.trb.org/View/2663604</link>
      <description><![CDATA[Pedestrian fatalities remain a persistent and growing safety crisis, with over 7,500 pedestrians killed on U.S. roads annually. Effective countermeasure deployment requires identifying high-risk locations before crashes occur, yet traditional crash-based analyses are insufficient due to the rarity of pedestrian crashes at individual intersections. Surrogate safety analysis using pedestrian–vehicle close calls offer a proactive alternative, but comprehensive observational data collection is prohibitively expensive and time-intensive. Video monitoring requires specialized equipment, extended deployment periods, and substantial manual processing. These practical constraints severely limit the geographic coverage, temporal scope, and contextual diversity of available datasets, ultimately hindering agencies' ability to develop reliable predictive tools that generalize across diverse intersection types and support evidence-based
safety interventions statewide. This project addresses these fundamental challenges by introducing Large Language Models (LLMs) as a novel tool to generate high-quality synthetic pedestrian–vehicle interaction data. LLMs possess extensive pre-trained knowledge spanning transportation systems, human behavior, and urban
environments, successfully demonstrated in healthcare and climate science for data augmentation. Building upon the Minnesota Traffic Observatory (MTO) dataset, where 18 intersections with 3,314 interactions involving 4,941 pedestrians, the research team will develop a validated methodology to generate contextually realistic scenarios incorporating roadway geometry, traffic control, land use, pedestrian demographics, and temporal patterns. This approach directly tackles the data scarcity problem that prevents agencies from conducting comprehensive pedestrian safety analyses across their jurisdictions.
The project has three objectives: (1) develop a transparent LLM-based synthetic transportation-targeted data generation methodology with validation protocols ensuring realism and quality; (2) evaluate whether synthetic data-augmented models improve prediction accuracy and transferability across intersections compared to observational data alone, using precision-recall AUC, calibration diagnostics, leave-one-site-out validation and other appropriate approaches; and (3) determine the mechanisms driving performance improvements: whether from introducing realistic scenario diversity or addressing rare-event limitations, to guide best practices. The framework will incorporate probability calibration, split-conformal risk control, and decision-curve analysis to deliver deployment-ready tools with quantified uncertainty for operational use.]]></description>
      <pubDate>Tue, 03 Feb 2026 15:34:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663604</guid>
    </item>
    <item>
      <title>Coastal and river bridge scour mitigation using hybrid solutions (TAMU)</title>
      <link>https://rip.trb.org/View/2663228</link>
      <description><![CDATA[Bridge piers, foundations, and abutments in coastal areas or across rivers often face heightened risk of detrimental scour development under wave and/or current loading. Along  coastlines bridges are part of essential evacuation routes, saving lives ahead of predicted storm impacts with life-threatening consequences if compromised. Further inland, many bridges across creeks and rivers that are part of rural transportation systems and low-volume road networks afford equally important transportation connections. When disaster strikes and these structures are compromised – as was painfully demonstrated in the recent Central Texas flash flood disaster – entire communities are cut-off from relief help or means to recover quickly. In most instances, bridge failure is initiated through hydraulically-induced scour formation and growth at the interface of the structural components and the surrounding sediment. If scour issues can be predicted and mitigated early, catastrophic failure can be avoided. The problem is that traditional mitigation techniques are costly or, in the case of rural bridges, may not even be included in the design. Here, the research team plans to test low-cost hybrid mitigation techniques that can help reduce scour impact to bridges caused by wave or current impact by using bio-cementation (such as Microbially-Induced Calcium Carbonate Precipitation - MICP) and/or geosynthetics in combination with the in-situ sediment.

Proposed Research: The team plans the following tasks to address the efficacy of these solutions to reduce scour: Task 1: Assess existing technological options for coastal and riverine bridge scour protection. This will be done via an in-depth literature review on scour protection with the goal of identifying various options, their advantages and limitations. 
Task 2: Conduct physical model wave flume scour tests with wave and/or current loading for different low-cost, hybrid scour protection combinations including MICP and geosynthetics in tandem with the in-situ sediments.
Task 3: Develop scour prediction equations based on the conducted physical model tests that can be used to assess the efficacy of the hybrid solutions for use in coastal and riverine bridge systems.
]]></description>
      <pubDate>Sat, 31 Jan 2026 11:25:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663228</guid>
    </item>
    <item>
      <title>A Probabilistic Intelligence-Driven Framework for Predictive Cyber Defense in Railway Systems</title>
      <link>https://rip.trb.org/View/2655703</link>
      <description><![CDATA[The rapid digital transformation of railway systems through automation, system integration, and enhanced connectivity has significantly improved operational efficiency, safety, and reliability. However, this digitalization has simultaneously expanded the cyber-attack surface, introducing new vulnerabilities in signalling, communication, and control systems. As critical national infrastructure, railways require robust protection against cyber threats to maintain operational resilience and public safety.

Railway cyber-physical environments present unique challenges distinct from traditional IT systems, characterized by strong interdependencies between digital and physical components where a single breach can cascade across subsystems, causing widespread disruption, safety hazards, and financial loss. Existing cybersecurity frameworks, often static and rule-based, are inadequate for representing the dynamic, probabilistic nature of modern cyber threats, necessitating data-informed, adaptive approaches capable of modeling complex dependencies and supporting timely decision-making.

This research develops a probabilistic modeling framework for assessing and mitigating cybersecurity risks in railway systems. The core methodology employs Bayesian Networks (BNs) to capture conditional dependencies among key threat variables, integrating both empirical data and expert knowledge to infer system vulnerabilities and potential attack outcomes. To address evolving threats, the framework extends to Dynamic Bayesian Networks (DBNs), incorporating temporal relationships that model cyberattack progression over time, enabling early threat detection and proactive defense strategies.

A central innovation is the integration of MITRE ATT&CK cyber threat intelligence, encoding real-world adversarial tactics, techniques, and procedures (TTPs) into the BN/DBN structures to enhance model realism and predictive accuracy. This research addresses three key questions: how Bayesian and Dynamic Bayesian Networks can model probabilistic relationships and temporal progression of railway cyber threats; how MITRE ATT&CK intelligence can be integrated to capture realistic adversarial behaviors; and how the proposed framework can support proactive cybersecurity risk assessment and decision-making. The resulting framework provides a systematic, interpretable foundation for probabilistic railway cybersecurity analysis, helping operators and policymakers anticipate and respond to emerging threats.]]></description>
      <pubDate>Tue, 20 Jan 2026 14:16:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655703</guid>
    </item>
    <item>
      <title>Evaluating the Cumulative Impact of Environmental Conditions on Stress Levels in Micromobility Users: An AI-Driven Multimodal Approach</title>
      <link>https://rip.trb.org/View/2652172</link>
      <description><![CDATA[Micromobility solutions, such as e-scooters and bicycles, are increasingly utilized in urban transportation, providing flexible and sustainable mobility options. However, micromobility users face significant exposure to environmental stressors, including air pollutants emitted by motorized traffic. While prior studies have explored the physiological effects of transportation emissions, the psychological impacts, particularly stress, remain underexplored. This study aims to bridge this gap by developing an AI-driven predictive model that evaluates the cumulative impact of transportation-related air pollutants on stress levels in micromobility users. By integrating wearable sensor data (e.g., electrodermal activity, heart rate variability, and skin temperature), air pollutant concentration data (e.g., PM2.5, NOx, and CO), and spatial context data (e.g., GPS and accelerometer readings), this research will leverage Temporal Fusion Transformer (TFT) models to predict real-time stress levels and generate stress heatmaps. The results will inform policymakers, transportation planners, and public health officials, contributing to more sustainable and inclusive urban transportation systems. Additionally, the project will provide hands-on research opportunities for students, fostering workforce development in AI-driven transportation health studies. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:55:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652172</guid>
    </item>
    <item>
      <title>Building Resilience Through Technology at Land Border Crossings </title>
      <link>https://rip.trb.org/View/2646952</link>
      <description><![CDATA[The 1,254-mile Texas-Mexico border is home to some of the busiest land ports of entry (LPOEs) in the world, enabling over 70% of goods traded between the United States and Mexico. However, these critical transportation nodes face persistent challenges that compromise their efficiency and resilience, including severe congestion and vulnerability to disruptions caused by extreme weather events, infrastructure failures, and security incidents. Addressing these challenges is critical to ensuring the durability of cross-border operations.  

The objective of this project is to develop a scalable, AI-powered system that enhances the operational resilience of land border crossings. The system will integrate emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) to improve real-time monitoring, disruption forecasting, and stakeholder communication. Core research tasks include the development of a Concept of Operations (ConOps) to define system functionality and stakeholder roles; the creation of predictive models to anticipate disruptions and improve operational planning; and the production of a deployment and scalability guide to support future replication.  

The anticipated outcome of this research is the development of a system capable of predicting and anticipating disruptions in order to improve operational resilience under adverse operating conditions. Additionally, the system will serve as a replicable model for other U.S. land ports of entry, offering a scalable solution that integrates predictive analytics with real-time communication protocols. The approach directly supports national and regional goals of enhancing infrastructure durability, operational efficiency, and public safety in transportation systems, while also contributing to workforce development and knowledge transfer in the domain of resilient cross border mobility. ]]></description>
      <pubDate>Mon, 12 Jan 2026 16:08:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646952</guid>
    </item>
    <item>
      <title>Impact of Pavement and Weather Conditions on Traffic Speed at Sharp Horizontal Curves  </title>
      <link>https://rip.trb.org/View/2646943</link>
      <description><![CDATA[Lane departure crashes on sharp horizontal curves are a major safety concern on both highways and freeways, accounting for a disproportionately high number of fatal and severe injury crashes. Research has shown that these crashes are often linked to speeds relative to curve geometry. While geometric design factors like curve radius and superelevation have been well studied, less attention has been given to how pavement and weather conditions influence traffic speed on these elements. Particularly, current safety models do not fully account for the effects of pavement surface conditions, such as friction, roughness, and texture, or adverse weather elements like precipitation, temperature drops, and reduced visibility. While the impact of factors like road curvature effects on traffic speed have been studied, current models often fail to integrate the complex interaction of pavement conditions and weather data in predicting traffic speeds. This results in inaccurate speed predictions, which can compromise safety and infrastructure planning. Without comprehensive, data-driven models, interventions such as speed limits, signage, or road maintenance are often poorly targeted, leading to higher risks of crashes, congestion, and inefficient resource allocation. The motivation for this project is to develop a predictive model that integrates pavement conditions, weather effects, and road geometry to estimate traffic speed at horizontal curves. This will provide safer roads by enabling better traffic management, targeted infrastructure improvements, and more efficient interventions. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:07:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646943</guid>
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