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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Nebraska Risk-Informed Construction Scheduling and Impact Analysis</title>
      <link>https://rip.trb.org/View/2689393</link>
      <description><![CDATA[Assigning a reasonable contract time is central to the Nebraska Department of Transportation (NDOT)’s project delivery process. The number of working days directly affects bid prices, contractor time-related overhead, public traffic impacts, and NDOT’s construction-engineering workload. However, many NDOT projects experience time extensions or schedule adjustments. These delays often arise from weather windows, utility coordination challenges, labor or material availability issues, and unforeseen field conditions. The presence of these uncertainties means that a single deterministic duration for each activity does not adequately represent the true likelihood of early or late project completion. NDOT currently relies on deterministic schedules and historical judgment when assigning contract time. These methods assume fixed activity durations and do not fully capture the uncertainties caused by weather, utilities, material supply, labor availability, or construction sequencing constraints. As a result, some projects may receive either more contract days than needed or face unexpected time extensions that increase cost and user delay. Recent research and best practices from other state DOTs and the Federal Highway Administration (FHWA) emphasize the need for probability-based scheduling that uses production-rate data, activity dependencies, and delay risk to estimate a realistic range of completion dates. The overarching goal of this project is to develop a data-driven, probability-based scheduling tool that enables NDOT to determine reasonable contract time and proactively assess construction delay risks throughout the project lifecycle.]]></description>
      <pubDate>Tue, 02 Jun 2026 12:25:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689393</guid>
    </item>
    <item>
      <title>Decision Support for Dynamic Risks: Determinants of Model Adoption</title>
      <link>https://rip.trb.org/View/2703696</link>
      <description><![CDATA[Since the COVID-19 pandemic, significant supply chain disruptions continue to impact the U.S. economy and have negative impact on transportation networks. Sudden changes in demand or freight availability contribute to increased volatility in freight prices. In turn, volatile freight rates impact the management of transportation networks and increase the difficulty of decision making. This research addresses this problem through the development of decision support tools to proactively respond to initial indicators that predict changes in driver availability and freight cost with the goal of supporting enhanced, early actions to mitigate the risk of disruptions and promote safer transportation network operations.
Work on related prior projects has underscored the importance of forecasting sources of risk to improve the management of transportation systems and the need to understand the key decision components to maximize the value of information to the decision maker. The proposed research will rely on this prior work and make advancements towards the design of an implementable system by examining the end-user perception of decision support recommendations for transportation contracting decisions. 
The research will interview transportation professionals to identify factors that influence their current decision-making and factors that would affect their adoption of a decision support tool. The results of these interviews, in conjunction with prior findings in related research, will inform the design of features for a decision support tool. Design features will be identified for an initial prototype that is suitable for conducting future usability testing of the interactive features. This research continues progress towards the development of a dynamic decision support tool that can ultimately improve the quality of transportation management decisions and continue the legacy of leadership in America’s transportation networks. ]]></description>
      <pubDate>Fri, 15 May 2026 14:13:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703696</guid>
    </item>
    <item>
      <title>Digital Twin and Automation of Pipeline Data for Predictive Maintenance and Risk Analysis incorporating Bayesian network</title>
      <link>https://rip.trb.org/View/2655704</link>
      <description><![CDATA[Pipeline infrastructure is critical for global energy transportation, yet aging systems face increasing degradation risks from corrosion, fatigue, and environmental factors. Recent catastrophic failures have demonstrated severe safety, environmental, and economic consequences of inadequate integrity management, with annual losses reaching billions of dollars. Despite significant advances in inspection technologies, including intelligent inspection tools and sensor networks, three fundamental challenges remain unresolved: lack of interpretability in machine learning approaches, difficulty quantifying uncertainties in defect growth predictions, and the challenge of optimizing maintenance decisions with incomplete information.

This research develops a Valuation Bayesian Network (VBN) integrated with Digital Twin technology as an automated decision-support tool for pipeline integrity management. The VBN approach provides a principled foundation for uncertainty quantification, data fusion, state estimation, prediction, and maintenance planning while maintaining interpretability. The framework incorporates probabilistic degradation and failure models, logic and relational models, and surrogate models to address key performance indicators, failure risks, and remaining life estimation.

The proposed Digital Twin architecture comprises four interconnected layers. The physical layer encompasses pipeline infrastructure with distributed sensors and expert knowledge. The simulation layer employs VBN-based probabilistic models capturing time-dependent degradation processes, where state variables, including defect depth and material properties, are modeled through Markov transition models with parameters learned from historical inspection records, pipeline failure databases, and expert elicitation. The data fusion layer performs Bayesian updating by integrating inspection data, operational history, and expert knowledge, enabling adaptive parameter calibration to capture site-specific degradation patterns. The decision layer implements maintenance optimization using value of information analysis and supports counterfactual reasoning for intervention scenarios.

This framework achieves full uncertainty propagation from measurement noise through remaining life predictions, ultimately enhancing operational safety by predicting high-risk defect development and enabling timely maintenance interventions.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:27:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655704</guid>
    </item>
    <item>
      <title>Estimating Environmental Load Demands Considering Weather Extremes to Enhance Resiliency of Oklahoma Bridges</title>
      <link>https://rip.trb.org/View/2633314</link>
      <description><![CDATA[Bridges are critical components of transportation infrastructure facilitating uninterrupted flow of goods and services within communities. However, the growing frequency and intensity of natural hazards and extreme weather events are escalating the vulnerabilities of bridge infrastructure. In recent years, Oklahoma has faced an increasing frequency of extreme weather events, including tornadoes, rising temperatures, and flash floods. These threats pose significant challenges for bridge design and maintenance leading to safety and functionality concerns. Therefore, innovative strategies and solutions are needed to reduce the impact of changes in weather patterns and extreme events on bridge infrastructure. Enhancing resilience of bridge infrastructure requires the incorporation of weather factors into bridge design codes and standards. The proposed study plans to evaluate the effect of changes in weather patterns and extreme weather events on the environmental load demand related to temperature and wind speed for bridges in Oklahoma. The use of advanced climatic models to predict future changes in weather patterns and estimate environmental load demand will be explored. A risk analysis will be performed to assess the vulnerability of bridges to future predicted weather conditions. Recommendations for updating bridge design codes and standards to incorporate considerations of extreme weather events will be provided based on the findings of this study.]]></description>
      <pubDate>Tue, 02 Dec 2025 16:20:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2633314</guid>
    </item>
    <item>
      <title>Proactive Strategy For Climate Resilient Corridors: Landslide Hotspot Prioritization, Planning, And Management Tool</title>
      <link>https://rip.trb.org/View/2594026</link>
      <description><![CDATA[Landslides are frequent hazards that affect Oregon highway infrastructure, resulting in negative economic, environmental, and social impacts for Oregon communities. Goals from the Oregon Transportation Plan (OTP) include proactive preparation of lifeline routes to reduce possible hazards before events occur, starting with the strategy to map and assess multi-hazard threats to the transportation system. While Oregon Department of Transportation's (ODOT's) Climate Adaptation and Resilience Roadmap and Climate Hazard Risk Map identify resilience corridors to help prioritize investment, a higher resolution analysis to prioritize between sites along these corridors is needed for development of compelling business cases for investment and competitive funding opportunities. ODOT is already under financial strain reacting to landslide hazards as they happen. Given that these hazards are projected to increase in frequency/magnitude with climate change, reactive approaches for returning to site functionality will exacerbate ODOT’s fiscally constrained reality. High resolution landslide hazard site vulnerability analysis followed by site prioritization will provide planning and management teams practical science-based investment strategies aimed at improving safety, ensuring working emergency lifeline routes, preventing community isolation, and reducing rising maintenance costs for hazard removal.]]></description>
      <pubDate>Thu, 28 Aug 2025 15:56:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2594026</guid>
    </item>
    <item>
      <title>Multi-modal AI Agents for Railway Safety</title>
      <link>https://rip.trb.org/View/2573196</link>
      <description><![CDATA[Artificial Intelligent (AI) agents, powered by foundation models, such as ChatGPT, have transformed every aspect of everyday life, in personal and professional settings, and have also started making substantial progress in specializing and producing results in various scientific and engineering domains. In this project, continuing the effort the research team started in Year 2 which entailed the development of a prototype for a large language foundation model for railway safety, the team will work towards developing a multi-modal AI agent for railway safety, which will be able to seamlessly integrate structured and unstructured text (such as accident reports and policy documents) with image data pertaining to a railway crossing and perform a number of tasks such as analyzing, comparing, and contrasting different railway crossings with respect to their risk factors and/or accident history, and come up with safety recommendations specifically tailored to a crossing. The proposed AI agent will combine rich domain expertise and the ability to sift through and analyze vast amounts of data that no human operator or policy maker may realistically be able to, thus empowering large scale data-driven railway safety. ]]></description>
      <pubDate>Mon, 14 Jul 2025 19:24:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573196</guid>
    </item>
    <item>
      <title>Theorizing Connected Vehicle-Enabled Traffic System Vulnerability Analysis with positioning, navigation, and timing (PNT)</title>
      <link>https://rip.trb.org/View/2548670</link>
      <description><![CDATA[This project investigates the cybersecurity vulnerabilities of connected autonomous vehicles (CAVs) with a focus on positioning, navigation, and timing (PNT) disruptions. This study examines how cyber-attacks, including false messaging, acceleration manipulation, and platoon leader identity attacks, impact traffic flow and system stability. By integrating an extended Intelligent Driver Model (IDM) with machine learning techniques such as Random Forest and stacked LSTM, the research develops an anomaly detection framework capable of identifying cyber-induced disruptions in real time. Simulation results demonstrate that even short-duration cyber-attacks can lead to significant travel delays, traffic instability, and collisions. The proposed methodology enhances cybersecurity resilience in connected transportation systems, aligning with USDOT's goals of improving safety, mobility, and infrastructure protection. These findings contribute to developing robust cybersecurity strategies for emerging autonomous vehicle networks]]></description>
      <pubDate>Wed, 30 Apr 2025 16:12:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548670</guid>
    </item>
    <item>
      <title>Efficient Construction Material Testing and Inspection Based on Risk Levels</title>
      <link>https://rip.trb.org/View/2533743</link>
      <description><![CDATA[South Dakota Department of Transportation (SDDOT) previously completed research project SD91-05 Essential Testing and Inspections Levels which was conducted over 30 years ago. It would be beneficial to review SDDOT’s current construction material testing and inspection program using a “Risk-Based Analysis”. This type of analysis would focus on the value of each material test and type of inspection, thus helping SDDOT to direct resources to where they would provide the most value and reduce the risk to end quality and performance. Risks include but are not limited to time, cost, safety, quality, and scheduling. Advancements in technology and software since SD91-05 have improved processes and productivity in the highway construction industry. Current and emerging technologies should be considered in this research to improve the efficiency of SDDOT construction material testing and inspection. ]]></description>
      <pubDate>Tue, 01 Apr 2025 08:45:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2533743</guid>
    </item>
    <item>
      <title>Layered Model for Disease Transmission Safety Risk Analysis</title>
      <link>https://rip.trb.org/View/2518969</link>
      <description><![CDATA[The Office of Aerospace Medicine needs to quantitatively model disease transmission risk in commercial air travel and assess mitigations to inform public health preparedness. Preliminary work under A11J.AM.19 developed two risk modeling approaches for an inter-agency Safety Risk Management (SRM) team convening in Q2FY26. One approach, advanced through FAA research, built on work by the Munich University of Applied Sciences combing microscopic crowd simulation with airborne pathogen transmission in the Vadere platform. Further work is required to refine the model beyond SRM needs and prepare it for broader use. Tasks include finalizing parameters, model validation, improving computational efficiency and user experience, and documenting the model. 
This research provides the FAA with a validated, user-ready model to quantify disease transmission risk in air travel and evaluate mitigations in support of safety risk management. The model will enable the division and operational stakeholders to assess public health hazards using a repeatable, data-driven approach aligned with SMS and SRM processes. The outcome will support national preparedness planning and provide transferable modeling capability to operators and interagency partners.
]]></description>
      <pubDate>Tue, 04 Mar 2025 14:31:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2518969</guid>
    </item>
    <item>
      <title>Towards Effective and Realistic Security Testing for Real-World HATS Autonomy Stack</title>
      <link>https://rip.trb.org/View/2458970</link>
      <description><![CDATA["Problem statement and objectives:
The promises of highly automated transportation systems (HATS) are clear and compelling, but HATS will fail to gain the public’s trust if they are seen as uniquely vulnerable to cyberattacks. Recent works have discovered various possible security threats against HATS that can lead to severe consequences such as vehicle collisions and traffic rule violations. Thus, it is imperative to design effective and realistic security testing methods for real-world HATS, especially for those that are safety-critical, in or close to production, and also relatively new (e.g, vehicle autonomy), so that their cybersecurity problems and the associated risks can be proactively identified, understood, and addressed/regulated before wide deployment.
Scope:
In our last year’s project, we performed the first large-scale commercial HATS security testing, against the popular Traffic Sign Recognition (TSR) features in commercial vehicles today. We performed physical-sce-nario testings and in-depth analysis with real consumer vehicles from top-selling brands, which not only confirmed the existence of real-world security risks against commercial HATS today, but also uncovered various new scientific gaps. In this new 1-year period, we will build upon last year’s efforts to perform (T1) new methodology designs to address the discovered scientific gaps, especially on the generalizability of the HATS autonomy stack security threats to better understand their real-world risks; and (T2) effective and realistic simulation-based security testing method designs for real-world HATS, as the physical setup-based testing methods we used last year are fundamentally limited in its real-world scalability.
Specifcally, for this new 1-year period we plan to focus such new testing methodology designs for two highly-realistic HATS security threats: 2D image spoofing attack (using 2D images of road objects, e.g., cars/pedestrians, to spoof road objects and thus trigger unsafe/undesired driving), and adversarial sce-nario attack (using malicious driving maneuvers to trigger unsafe/undesired driving). Both threats have high realism in real world given the low attack cost and expertise requirements for the former (just need to print a normal photo), and the low legal liability risks for the latter (no need to alter legitimate road signs/objects). However, for the former, no simulation-based testing method exists today, and for the lat-ter, existing simulation-based testing methods lack scenario awareness (e.g., still optimizing for causing front collision when no obstacles are in the front), which fundamentally limits its testing effectiveness."

"Methods:
For task T1, building upon our project last year, we will focus on generalizability research on security threats against real-world TSR systems. Specifically, we plan to explore leveraging the latest generative-AI methods (e.g., diffusion models) to generate more generally effective AI attacks aross different commer-cial TSR; such a generative AI-based approach has recently been found capable of improving the transfer-ability of AI attacks in general, and we will be the first to explore this for HATS. For task T2, we will be the first to perform a formal definition of 2D image spoofing attack in HATS settings considering HATS-specific attack goals and constraints (e.g., attack image type/size/location based on driving scenario/vehicle dy-namics), and then design and develop the simulation-based testing method accordingly. For adversarial scenario attack, we will first reproduce latest testing method, and then design new scenario-aware testing methods, e.g., by designing new methods to systematically discover scenario-specific attack opportuni-ties and dynamically adjust attack objective functions.
This project involves both theoretical developments (new methodology designs above) and experimental demonstrations (detailed next). The planned simulations/experiments include but are not limited to: (1) Continued security testing efforts for real-world HATS systems/products; (2) Experimental evaluation of the new testing methods above against real-world HATS systems/products in both physical world and in-dustry-grade simulation environments. CARMEN+ center-internal collaborations will be actively pursued."]]></description>
      <pubDate>Thu, 21 Nov 2024 17:40:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2458970</guid>
    </item>
    <item>
      <title>Transportation Asset Risk and Resilience Analysis in Coastal Communities</title>
      <link>https://rip.trb.org/View/2260008</link>
      <description><![CDATA[Flood risk assessment for urban road infrastructure faces significant challenges, particularly due to the scarcity of historical inundation data and the computational inefficiencies of traditional hydrodynamic models. This study addresses these challenges by leveraging 592 modular 2D hydrodynamic flood simulations to assess both direct agency costs (infrastructure repair) and user costs (travel time delays) resulting from flood events. The methodology integrates hazard scenario generation, hazard-asset pairing, vulnerability assessment, and impact analysis to develop a holistic framework for flood risk and resilience assessment.
Harris County, TX, a flood-prone region that includes the Houston metropolitan area, serves as the testbed for this analysis. High-resolution flood simulations are paired with geospatial road network data to estimate inundation depths and associated damages for over 21,000 road segments. Depth-damage functions are applied to quantify the direct economic costs of road infrastructure damage, while a transportation resilience model calculates the societal impacts in terms of travel time delays across flood scenarios.
The results demonstrate that flood-induced infrastructure damage and travel disruptions exhibit spatial heterogeneity and nonlinear relationships with inundation depth, highlighting critical road segments that require targeted resilience interventions. By combining direct and societal costs into a unified monetary metric, this study provides stakeholders with a robust decision-support tool for prioritizing flood mitigation investments and enhancing urban resilience. The framework’s computational efficiency and scalability make it adaptable for application in other flood-prone regions, offering a valuable resource for policymakers, planners, and engineers.]]></description>
      <pubDate>Tue, 03 Oct 2023 21:47:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2260008</guid>
    </item>
    <item>
      <title>Assessment of Waterfront Asset Resiliency</title>
      <link>https://rip.trb.org/View/2255636</link>
      <description><![CDATA[The primary goal of this proposal is to enhance the understanding of hazards, vulnerabilities, and potential impacts on critical assets at the Little Egg Harbor Yacht Club and develop effective strategies for risk reduction and resilience enhancement.

The intended outcome of the project is to generate data to develop a “Waterfront Infrastructure GeoDatabase Interface” or WIGI, viewable as a customized Environmental Systems Research Institute (ESRI) ArcGIS Pro Software Application that will include a geolocated 3D visualization of identified assets with their associated attribute tables, and functionality to perform RAMCAP analysis. This tool will provide valuable insights for the Yacht Club Management to make informed decisions, plan to allocate resources effectively, and enhance the overall resilience of their waterfront site.]]></description>
      <pubDate>Tue, 03 Oct 2023 21:35:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2255636</guid>
    </item>
    <item>
      <title>Risk and Resiliency Analysis of Infrastructure by Improving RAMCAP Framework</title>
      <link>https://rip.trb.org/View/2169767</link>
      <description><![CDATA[The primary goal of this proposal is to develop a comprehensive risk and resilience assessment framework for critical transportation and coastal infrastructure, using the RAMCAP framework as a baseline. The framework will identify potential risks to the infrastructure and analyze its resilience against natural and artificial hazards.

The intended outcome of the project is to provide valuable insights into critical transportation and coastal infrastructure risks and resilience and develop a comprehensive framework for assessing these factors. The proposed framework will help decision-makers prioritize investments and interventions to improve the state of good repair and extend the life of infrastructure.]]></description>
      <pubDate>Mon, 25 Sep 2023 18:07:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2169767</guid>
    </item>
    <item>
      <title> 
 Communicable disease preparedness: M&amp;S framework for analyzing cabin health hazards </title>
      <link>https://rip.trb.org/View/2072042</link>
      <description><![CDATA[The Federal Aviation Administration (FAA) has assumed a leadership role in developing a preparedness plan for communicable disease in air travel and identifying associated research needs. The FAA’s approach to the planning effort is to use its existing Safety Risk Management (SRM) process, as documented in FAA Order 8040.4B, to determine the risk of transmission of a disease requiring flight-related contact tracing within a population of airline passengers and cabin crewmembers between the times of population formation and dispersion and the expected impacts of mitigation activities. This research project will answer the question, what is a generalizable risk analysis framework and associated set of accepted and validated modeling, simulation, and analysis (MS&A) tools for determining baseline risk and evaluating the impact of risk control measures. The project will define an analysis framework for cabin health safety hazards; conduct a survey of existing MS&A tools, data sources, and non-destructive testing methods suitable for studying pathogen movement in transport aircraft cabins; select the preferred MS&A tool set and testing methods; and plan and conduct MS&A validation and analysis studies. The resulting analysis framework and associated MS&A tools and data will be transitioned for use in communicable disease transmission preparedness planning.]]></description>
      <pubDate>Wed, 30 Nov 2022 15:31:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2072042</guid>
    </item>
    <item>
      <title>Aeromedical Collaboration Environment for Fusing Pilot Medical Certification and Operational Data </title>
      <link>https://rip.trb.org/View/2037218</link>
      <description><![CDATA[The purpose of this project is to fuse FAA pilot medical certification decisions with operational safety outcomes beyond the historical outcomes of interest of mishap and pilot incapacitation events. Achieving this objective requires enhancing the traditional aerospace medicine safety management system data environment to make it a collaborative innovation environment with joint FAA and industry participation. This project addresses the question of how the FAA can form a Public-Private Partnership (PPP) with the commercial airline industry to obtain pilot operational performance and safety data for linking to agency medical certification data to support advanced risk analyses. This project will include an industry outreach to create a PPP with one or more commercial airline partners, a feasibility assessment for creating an Aeromedical Collaborative Environment (ACE), and the design and construction of the ACE.]]></description>
      <pubDate>Sat, 08 Oct 2022 16:50:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2037218</guid>
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