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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxODA1IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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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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    <item>
      <title>Modernization of Idaho StreamStats: Updates to Statewide Basin Characteristics and At-Site Peak Streamflow Statistics</title>
      <link>https://rip.trb.org/View/2742769</link>
      <description><![CDATA[The Idaho Transportation Department (ITD) is seeking to enhance the Idaho StreamStats application by incorporating updated basin characteristics, hydrography, and at-site peak streamflow statistics using current datasets, advanced geospatial technologies, and modern hydrologic analysis methods. StreamStats is an important tool used by transportation engineers, planners, and water resource professionals to estimate streamflow characteristics that support the design and maintenance of bridges, culverts, drainage infrastructure, and other transportation assets. This project will leverage high-resolution lidar-derived elevation data, updated land cover and climate datasets, improved hydrography, and current flood-frequency analysis methods to strengthen the accuracy, consistency, and reliability of hydrologic information available through StreamStats. Research activities will include updating basin characteristics, recalculating peak-flow statistics using recent streamflow records, integrating updated information into the StreamStats application, and publishing supporting datasets to promote transparency and future use. The resulting tools and workflows will provide ITD and its partners with enhanced hydrologic information to support infrastructure design, flood risk assessment, regulatory compliance, and long-term transportation planning.]]></description>
      <pubDate>Tue, 04 Aug 2026 16:20:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742769</guid>
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    <item>
      <title>Urban Flow Autonomous Wheelchair Phase 4: Fleet Management System and Health Facility Deployment</title>
      <link>https://rip.trb.org/View/2742156</link>
      <description><![CDATA[Morgan State University’s autonomous wheelchair (AW) project has completed three phases of research and development, culminating in a high-profile public demonstration at Baltimore/Washington International Thurgood Marshall Airport (BWI) in 2025, growing from a single prototype to a fleet of three LiDAR- and camera-equipped wheelchairs operable via the UrbanFlow smartphone application.

Phase 4 advances the project on three fronts, collectively moving the technology from a research prototype toward real-world, scalable deployment. First, researchers will complete a Fleet Management and Operator Dashboard, replacing the existing one-to-one operator model with a centralized system capable of controlling the full wheelchair fleet. Second, the team will deploy and test the AW system in a new indoor environment, the Morgan State University Health and Human Services (HHS) Center. Third, it will develop a Digital Twin of the UrbanFlow deployment environment, enabling virtual simulation of wheelchair navigation scenarios to accelerate testing and support future multi-site planning.

Organized across five tasks, Phase 4 will deliver a Fleet Management and Operator Dashboard enabling one operator to simultaneously oversee, dispatch, and control the entire wheelchair fleet, with live location tracking, battery and status monitoring, automated low-battery alerts, remote override, centralized dispatch, fleet analytics, and fault and maintenance flagging. The dashboard will be validated through live multi-wheelchair sessions at BWI and on campus with iterative operator usability testing. The team will map, configure, and deploy the AW system within the HHS Center to establish a fully operational navigation environment at a new site type, and will develop a Digital Twin, a virtual replica of the physical deployment environment integrated with UrbanFlow, as a simulation and planning tool.]]></description>
      <pubDate>Sat, 01 Aug 2026 10:43:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742156</guid>
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    <item>
      <title>Automated Vehicle (AV) Pooled Fund Study (PFS) Phase 2</title>
      <link>https://rip.trb.org/View/2734822</link>
      <description><![CDATA[Automated Vehicles (AVs) are an emerging technology that will dramatically change the transportation system, potentially reducing crashes, expanding access, and promoting sustainability, safety, and efficiency. With over 41 states actively advancing AV testing, research, policy, and planning, state departments of transportation face challenges understanding what strategies and actions are needed to prepare for this evolution in emerging technology. While many states are advancing strategic plans and roadmaps, strategies vary and ideas of what it means to be implementation ready differ. The original Automated Vehicle (AV) Pooled Fund Study (PFS) [TPF-5(453)] started in 2020 to collaboratively coordinate funding, strategies and research to help State DOTs understand their role in this changing environment, and how to proactively prepare. The objectives of this new pooled fund study, AV PFS Phase 2, are to continue and expand on the achievements of TPF-5(453). The key goals of the AV PFS Phase 2 are to conduct research that supports the following items: (1) Infrastructure readiness: What infrastructure investments do DOTs need to be making to plan and prepare for AVs; (2) Operations: How do AVs impact operations, traffic safety and maintenance; (3) Policy frameworks: What laws and policies do states need to enact to develop clear, more uniform policy across regions to avoid a patchwork approach; (4) Interstate freight and multi-modal harmonization – How can DOTs advance freight harmonization and interstate networks to support autonomous delivery; (5) Workforce development: How do we skill the current and future workforce for new technologies, processes and impacts; (6) Communications and engagement: How do we message this work to internal and external stakeholders and engage partners, industry and communities; (7) Partnerships: How can infrastructure owner-operators (IOOs) partner with industry, researchers, communities and non-profits to plan for this unknown future; (8) Strategic investment: How can IOOs make strategic investments with limited resources that plan for emerging technology and AV innovation; (9) Planning: How should DOTs plan for AV technology in the short and long-term horizons How can DOTs follow trends and develop shared future scenarios; (10) Industry forum: How can we develop a collaborative forum where states together can meet with AV industry to share ideas and learn about industry goals similar to the CAT Coalition’s IOO-OEM forum.]]></description>
      <pubDate>Thu, 23 Jul 2026 14:52:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2734822</guid>
    </item>
    <item>
      <title>Causally Informed Forecasting of Event Travel Dynamics for the 2028 Los Angeles Olympics Using Large-Scale Mobility Data - Phase 2</title>
      <link>https://rip.trb.org/View/2732878</link>
      <description><![CDATA[Phase 2 of this project builds on Phase 1 foundational work to develop causal inference frameworks for event-driven travel behavior, and informed by those causal insights, forecasting models for the 2028 Los Angeles Olympic and Paralympic Games. This research can directly inform the work of the White House Task Force on the 2028 Summer Olympics (established by Executive Order 14328) and broader innovations in transportation planning for large-scale events.

In Phase 1 (January–June 2026), the research team is leveraging large-scale smartphone mobility data from approximately 100,000 anonymized mobile devices to characterize travel behavior during historical major events at approximately 20 venue sites where Olympic and Paralympic Games will be held across Los Angeles County. Analyzing mobility data linked to a Points of Interest database, the team is producing descriptive analyses for two key populations: (1) event visitors, characterized by place of origin, lodging location, attendance and post-event lingering duration, and secondary activities; and (2) local residents, for whom disruptions to routine travel are analyzed before, during, and after events. From these analyses the team is constructing origin-destination (O-D) matrices disaggregated by visitor/resident status, time, and event type.

Phase 2 advances this work to develop a causal inference-informed forecasting pipeline through two interconnected research thrusts. In the first stage, the team will use quasi-experimental methods (synthetic control and difference-in differences) to estimate the causal effect of each historical event on mobility outcomes, isolating what changes in mobility were caused by the event vs. confounders including baseline trends and seasonal variation. In the second stage, an invariant prediction framework [1, 2, 3] will identify which event features reliably predict those causal effects across approximately 20 venues and diverse event types; those features then become the inputs to the forecasting model. This design is motivated by a key insight from recent work in causal transfer learning and causal representation learning: prediction performance improves when models rely on features whose relationship to the outcome remains invariant, i.e., stable across different environments, rather than based on correlational patterns that may not generalize. This causal-then-forecast approach is especially well suited to the proposed Olympic Games planning task for three reasons: there is large heterogeneity across training “environments” characterized by different venues, event scales, types, seasons, and audiences simultaneously; observations per environment are limited by modest sampling rates (1-3%) and a finite number of historical events, so restricting to causally stable features can provide performance-improving regularization; and the practical purpose is prospective planning for unprecedented scenarios (e.g., simultaneous events across multiple venues), where causally grounded models can more reliably extrapolate.

Specifically, the first thrust will employ quasi-experimental research designs, including synthetic control and difference-in-differences approaches, to estimate the causal effect of historical major events on mobility outcomes, isolating event-driven behavioral changes from baseline trends, seasonal variation, and other confounders. The second thrust uses these causal estimates to inform the development of forecasting models for predicting mobility behavior to, during, and immediately after major events at venues across Los Angeles County. In a causal transfer and representation learning framework, the causally identified event effects will be used to constrain model architectures by selecting predictive features that are invariant (i.e., that reliably predict the quantified causal effects), across event types, venues, time periods, and other sources of environmental variation. These causally informed forecasting models will generate predictions of travel demand, trip timing, O-D flows, and secondary activity patterns for both event visitors and affected local residents.

Together, these thrusts complete a research pipeline supporting scenario analysis for Olympic transportation planning spanning Phases 1 and 2: descriptive characterization of large-scale historical events, causal inference to isolate event-specific effects, and causally informed forecasting models that predict travel to, during, and after the 2028 Olympic Games. A Technical Advisory Committee (TAC) will be composed of LA Metro Olympic planning staff, mobility data providers, and Crypto.com arena data science staff, to advise on key modeling outputs and insights regarding data inputs. All data handling will comply with USC and Cuebiq privacy standards.
 
]]></description>
      <pubDate>Wed, 22 Jul 2026 17:14:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732878</guid>
    </item>
    <item>
      <title>SPR-5108: Automated Highway Asset Inventory Using High-Resolution Aerial Imagery and LiDAR Data</title>
      <link>https://rip.trb.org/View/2732647</link>
      <description><![CDATA[This project proposes to develop, validate, and scale an automated framework for extracting and mapping key highway assets from 3-inch aerial imagery and QL1 LiDAR data. Target assets include the number of travel lanes in each direction, pavement section areas, guardrails, and sound barrier walls. Hamilton County will serve as the pilot implementation area due to its diverse mix of Interstate, U.S., and State routes across urban and suburban environments.]]></description>
      <pubDate>Wed, 22 Jul 2026 15:22:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732647</guid>
    </item>
    <item>
      <title>Mississippi Summer Transportation Institute- 2027</title>
      <link>https://rip.trb.org/View/2732467</link>
      <description><![CDATA[The Mississippi Summer Transportation Institute (MSTI) Program aims at introducing a group of motivated pre-college students (9th to 12th grade) to the transportation industry. During the two-week program, students will participate in academic and enhancement activities designed to improve their skills in Science, Technology, Engineering, and Mathematics (STEM) and leadership.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:46:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732467</guid>
    </item>
    <item>
      <title>Mississippi State Transportation Institute- 2027</title>
      <link>https://rip.trb.org/View/2732446</link>
      <description><![CDATA[The Mississippi State Transportation Institute (MSTI) Program aims at introducing a group of motivated pre-college students (9th to 12th grade) to the transportation industry. During the two-week program, students will participate in academic and enhancement activities designed to improve their skills in Science, Technology, Engineering, and Mathematics (STEM) and leadership.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:43:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732446</guid>
    </item>
    <item>
      <title>Community Response Enhanced Education on Disasters (CREED): Virtual Reality Training to Enhance Transportation</title>
      <link>https://rip.trb.org/View/2732358</link>
      <description><![CDATA[Artificial Intelligence/Virtual Reality (AI/VR) simulation activities enhance critical emergency management functions—planning, forecasting, threat detection, security, and information sharing—ultimately improving the preservation of life and property. These tools create a safe, immersive environment where participants build problem-solving skills, decision-making ability, and confidence in disaster response. This project will deliver education, professional development, and continuous improvement in emergency preparedness for both aspiring and current professionals. The research team will invite high school and community college students to participate alongside university students and practitioners. Participants will engage in hands-on training and live demonstrations using advanced technologies such as virtual reality (VR) and artificial intelligence (AI)-driven simulations. The program fosters multidisciplinary collaboration among disciplines: Emergency Management Technology, Meteorology, Computer Science/Engineering, Health Science, and Journalism, and Media Studies. Through classroom instruction, workshops, and interactive training demos, students will work directly with emergency management professionals, strengthening real-world skills, supporting school-to-work transitions, and enhancing career readiness.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:29:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732358</guid>
    </item>
    <item>
      <title>Improve MDOT's Understanding of the Acceptance and Performance of Riprap</title>
      <link>https://rip.trb.org/View/2731977</link>
      <description><![CDATA[The long term performance of riprap has been an issue because some local sources of riprap have known durability issues
and will degrade/dissolve over time. In addition, the acceptance of riprap size and gradation is currently done by performing a
Wolman count. Performing the Wolman count involves walking over large rocks, which can be a safety hazard and takes a
significant amount of time to do. There are challenges in assessing the performance and durability of riprap in riverine, lightly
acidic and other environments that need to be addressed. The potential exists that there may be technological and electronic
solutions that need to be utilized to enhance or replace existing processes.]]></description>
      <pubDate>Fri, 17 Jul 2026 15:21:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731977</guid>
    </item>
    <item>
      <title>Enhanced Understanding of Geotechnical Processes</title>
      <link>https://rip.trb.org/View/2731975</link>
      <description><![CDATA[There is a need for a multidisciplinary hub for advancing geotechnical engineering practices in support of Michigan’s surface
transportation infrastructure. In alignment with the objectives outlined by the Michigan Department of Transportation (MDOT), the
Center will engage in a comprehensive suite of activities, including but not limited to education and workforce development, public
and stakeholder outreach, applied and theoretical research, implementation of innovative technologies, laboratory and field testing,
analytical modeling, and investigative services. These efforts will be guided by MDOT’s strategic priorities and may be further refined
through specific tasks detailed in this Scope of Services. The overarching goal of this center is to function as a responsive and
collaborative resource for MDOT by facilitating in the development, evaluation, and deployment of novel geotechnical solutions that
enhance the safety, durability, and sustainability of Michigan’s transportation systems. This can be done by fostering innovation and
continuous improvement in geotechnical engineering. The Center aims to bridge the gap between research and practice, ensuring
that emerging technologies and methodologies are effectively translated into real-world applications that benefit the traveling public
and support the long-term stewardship of the state’s infrastructure assets.]]></description>
      <pubDate>Fri, 17 Jul 2026 15:11:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731975</guid>
    </item>
    <item>
      <title>Linking Landslide Triggering and Runout Hazard with Surface Deformations for Optimized Infrastructure Systems Resiliency</title>
      <link>https://rip.trb.org/View/2726550</link>
      <description><![CDATA[Landslides are one of the most significant geohazards impacting North Carolina's transportation network, causing fatalities, property loss, and long-term economic disruption. These events are frequently triggered by extreme precipitation from hurricanes and tropical storms, which have historically produced hundreds to thousands of debris during a single event. For example, Hurricane Helene (2024) triggered more than 2,000 reported landslides across the Southern Appalachians, resulting in widespread road closures, bridge damage, and tens of billions of dollars in direct and indirect losses. As the frequency and intensity of extreme precipitation events increase, the risk of cascading infrastructure failures is expected to grow. Current North Carolina Department of Transportation (NCDOT) Geotechnical Asset Management (GAM) tools primarily operate reactively— tracking known unstable sites and coordinating post-disaster repairs. Therefore, there is a critical need for proactive capabilities to anticipate landslide hazards before they disrupt the network.

The objective of this project is to create a robust, scalable, and computationally efficient framework to predict landslide triggering and runout at a regional scale, supporting optimized maintenance, emergency response, and risk-informed investment decisions. This work will integrate the North Carolina Geological Survey (NCGS) Post-Helene Landslide Inventory, surface deformation mapping, and AI enhanced triggering predictions. The research will pursue four main objectives: (1) consolidate and curate a high-quality georeferenced dataset of landslide and debris flow events in North Carolina; (2) develop machine-learning models informed by physics to predict triggering susceptibility based on rainfall thresholds, slope geometry, and hydrologic conditions; (3) link surface deformation signals to slope stability through finite-element-based surrogate models; and (4) compute landslide runout using depth-averaged Material Point Method (DA-MPM) simulations that account for three-dimensional topographic effects and infrastructure exposure.

The approach follows a hierarchical and computationally efficient workflow. Regional-scale data-driven models will rapidly screen the entire state for slopes with high triggering potential. For these critical sites, limit equilibrium analysis (LEA) using existing NCGS models will identify likely failure surfaces and factors of safety. The outputs will serve as inputs to physics-based DA-MPM simulations that predict debris flow runout, impact zones, and potential consequences for NCDOT-managed assets. This strategy maximizes coverage while focusing on high-fidelity simulations where they are most needed, thereby balancing predictive power with computational cost.

The anticipated products include trained machine-learning models, enhanced infinite-slope analysis incorporating AI training, a verified and validated DA-MPM module, and geographic information system (GIS)-integrated hazard/risk maps. Integration into NCDOT's existing GAM system will enable decision-makers to: (i) develop watchlists of critical slopes, (ii) anticipate maintenance and debris removal needs, (iii) coordinate detour planning and emergency response, and (iv) communicate risk more transparently to stakeholders. Training workshops will be held with NCDOT and NCGS engineers and geologists to ensure usability and gather feedback for future system enhancements.

This project represents the first step toward a real-time, data- and physics-informed landslide early warning and infrastructure risk management system. By combining machine learning, geotechnical modeling, and large-deformation simulation, this work will strengthen North Carolina's landslide risk assessment and improve transportation resiliency, reduce lifecycle maintenance costs, and protect the safety and mobility of the traveling public.]]></description>
      <pubDate>Thu, 09 Jul 2026 09:02:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726550</guid>
    </item>
    <item>
      <title>Empirical Modeling for Improved Ground Failure Analysis</title>
      <link>https://rip.trb.org/View/2726232</link>
      <description><![CDATA[Problem Statement: Numerous bridge approaches and substructures, highway and railway embankments, and particularly roads in low-lying areas adjacent to rivers and their corresponding traffic sign and signal poles are underlain by the silt soils of the Willamette and Columbia River Valleys and below Oregon's coastal communities. These soils are susceptible to liquefaction or cyclic softening during earthquakes and will produce varying degrees of severity in the consequences such as lateral spreading displacement, global instability, and settlement. Settlement of soils will produce drag loads to bridge and traffic sign and signal pole foundations. Such damage has the potential to severely impact our critical surface transportation lifelines and reduce the efficacy of emergency responders and reduce the rate of economic recovery. The risk of seismic ground failure is exacerbated by groundwater table rise, which occurs during short-term, acute events (flooding) and the long-term effects of potential rising sea levels. Application of ground failure models to silty soils that were developed based on the responses of sandy soils can result in over-conservative estimates of the effects seismic ground failure and lead to inefficient use of limited resources as Oregon strives to maintain and improve its current resilience.
This work aims to develop the types of empirical relationships that the geotechnical community are well-familiar with but geared towards transitional silty soils, which can exhibit differing behaviors from the soils which are presently represented in available models. The objectives of this research are to produce specific design guidance, models, and spreadsheet-based tools to: (1) account for the effects of sloping ground on the calculation of the factor of safety against liquefaction/cyclic softening during earthquakes, (2) compute lateral displacements of sloping ground, and (3) calculate vertical settlements of level and sloping ground and any foundations buried within, to (4) culminate in a decision matrix for Oregon Department of Transportation (ODOT) engineers and their consultants to guide the selection of a particular model when assessing the seismic vulnerabilities of existing surface transportation infrastructure. The decision matrix and specific guidelines for conducting cyclic failure analyses and simplified displacement estimates will guide cost-effective measures to assess and improve existing surface transportation infrastructure and improve community and infrastructure resilience to increasingly combined natural hazards.
]]></description>
      <pubDate>Wed, 08 Jul 2026 17:38:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726232</guid>
    </item>
    <item>
      <title>Subsurface Analysis Planning Tool for Cost Reduction, Rapid Emergency Evaluation, And Data Support for Rural Service Areas</title>
      <link>https://rip.trb.org/View/2726188</link>
      <description><![CDATA[Transportation professionals often make important decisions about system dependability, project design, and emergency response with limited time and data. This is particularly true as it relates to the underlying soil, rock, and groundwater conditions that directly affect the design and repair of critical infrastructure. Typically, designers and engineers rely on drilling boreholes and performing in-situ tests to characterize subsurface conditions and associated problems for applications ranging from bridges to roadways to stream crossings to landslides. Unfortunately, these exploration techniques are expensive, time-consuming, inherently risky, and often accompanied by significant lead times. Further, the complex nature of this data can be very difficult to interpret and the uncertainty difficult to quantify.

This research project will leverage investments from CLiP, Oregon Department of Transportation (ODOT) SPR786, and SPR808 to improve GOSEP algorithms for predicting subsurface information for planning and emergency response, with a focused aim for extrapolation improvement in rural, data sparse regions. This project also aims to expand the GOSEP database and thereby its capabilities by adding more geotechnical data and parameter datasets as well as by improving the OCR borehole log scanner for archived handwritten borehole logs.]]></description>
      <pubDate>Wed, 08 Jul 2026 17:34:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726188</guid>
    </item>
    <item>
      <title>Statewide Multimodal Destination Access Methods and Demographic Analysis</title>
      <link>https://rip.trb.org/View/2725639</link>
      <description><![CDATA[The Oregon Department of Transportation (ODOT) does not currently have a consistent, statewide method to evaluate destination access—whether people can reliably and affordably reach essential destinations such as employment, education, health care, and key services. While agency performance measures and analyses focus primarily on infrastructure conditions and system mobility, they do not answer whether investments are improving people’s ability to access what they need for daily life. Without a standardized destination access methodology, ODOT lacks a clear, data-driven basis for monitoring progress, understanding structural access gaps, or using access outcomes to inform investment decisions. 
OBJECTIVES: (1) Establish a standardized, agency-wide methodology for multimodal destination access analysis. ODOT currently performs destination access analysis on an ad hoc basis and does not have a consistent, documented method for statewide or cross-program use. This project will develop and test a unified approach that can be used across business lines for performance reporting, planning, and investment decision-making. (2) Integrate user profile analysis to identify which populations face transportation access gaps—and to what extent. This research will move beyond single-variable demographic assumptions and instead use data-informed definitions of at-risk populations to better understand who experiences structural access barriers and why. (3) Develop a tool for viewing destination accessibility metrics. The tool can be used to view accessibility by mode and destination type by region. The combination of transportation and land use data will enable planners to understand existing accessibility conditions and needs in specific areas.  This tool would be usable for the ODOT Capital Investment Plan (CIP), local transportation system plans, and other programs where access measures offer utility. 
This research fulfills a need for a destination access methodology that supports ODOT policy, planning, and prioritization. With the results of this research, ODOT will be able to answer critical questions about how the transportation system is serving residents. Access metrics can play a critical role in vehicle miles of travel (VMT) per capita and emissions reduction strategies by informing staff on which areas have feasible multimodal access. ]]></description>
      <pubDate>Wed, 08 Jul 2026 16:48:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725639</guid>
    </item>
    <item>
      <title>Phase 2: Leveraging Surface Monitoring to Guide Emergency Response and Long-Term Strategic Planning</title>
      <link>https://rip.trb.org/View/2725299</link>
      <description><![CDATA[Conventional monitoring, site investigation, and assessment of landslides for mitigation often requires drilling along with installation of piezometers and inclinometers which is not always feasible due to the significant expense of drilling, short lifespan of subsurface instruments, safety concerns about working on an active landslide, and difficult site access. A continued need exists to expand the capabilities of near-real-time surface monitoring of ground movements for (1) existing, monitored landslides to gather longer time-series of landslide response to variable wet seasons, and (2) newly-monitored landslides that reflect uncharacterized climatic and geologic conditions to extrapolate a spectrum of landslide impacts to Oregon Department of Transportation (ODOT) right-of-way, and (3) landslide events that occur and require quantitative information for decision-making. Such data is key for evaluating highway safety, repair and mitigation needs, and strategies for reopening after failure. The absence of this information places critical infrastructure and ultimately ODOT customers at risk.

The data from this expanded monitoring would inform important ODOT planning activities and research questions. For planning purposes, this data has already demonstrated benefits for emergency response to landslide events, planning around mitigation plans and Goal 18 discussions, maintenance considerations, and reduced ODOT time for inspection of landslides. Expanding these efforts would further support ODOT’s ability to monitor problematic slopes and make informed decisions regarding prioritization, planning, and sustaining mobility.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:07:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725299</guid>
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