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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>Risk Evaluation of Transportation Systems to Tornadoes to Facilitate SAFE Digital Twin Development</title>
      <link>https://rip.trb.org/View/2703879</link>
      <description><![CDATA[This project will develop tornado hazard curves for tornado-prone regions. To achieve this, the tornado genesis model will be applied to generate a large number of tornado starting points. Then, the tornado track model will be applied to simulate track parameters (e.g., path width, length, heading direction and intensity). Next, for each tornado track, a wind field model will be established to obtain the information on the entire wind field, including wind velocity and pressure. For this, CFD simulations will be first applied to model a small number of tornadoes with representative flow structure and intensities; a surrogate model will then be developed using the multi-fidelity machine learning modeling technique, to replace the time-consuming CFD simulations. Finally, the generated data from the synthetic tornado tracks for a great number of years will be processed statistically to develop tornado hazard curves and tornado hazard maps for the states in Mainland America. The developed tornado hazard curves will help Department of Transportation properly assess the damage to vehicles on the road or in parking lots, informing stakeholders of preparation for future tornadoes. The developed curves can be integrated into catastrophe modeling to better estimate the risk of vehicles under tornadoes and thus better price the automobile insurance premium. In addition, these curves can improve the building codes related to a tornado-resistant design. ]]></description>
      <pubDate>Mon, 18 May 2026 17:13:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703879</guid>
    </item>
    <item>
      <title>Federal-Industry Waterway Governance Mapping</title>
      <link>https://rip.trb.org/View/2698370</link>
      <description><![CDATA[This project will produce the first comprehensive governance map of the U.S. inland waterway system, documenting how federal agencies, waterway commissions, port authorities, operators, industry associations, and advisory bodies exercise authority, coordinate responsibilities, and influence decisions across planning, operations, maintenance, and emergency response. While the inland waterway system depends on a complex interplay of federal ownership, federally authorized navigation channels, industry-operated vessels, federally maintained locks and dams, state commissions, port authorities, cooperative working groups, and advisory committees, there is currently no resource that synthesizes this institutional architecture into a clear, accessible structure. The research will analyze agency documentation, statutory authorities, standing committee structures, and operational guidance, complemented by targeted interviews with practitioners, to clarify how decisions flow through the system and how organizations interact across routine and non-routine conditions. The final product will provide a governance map and a narrative analysis that identifies gaps, redundancies, and friction points in the institutional landscape. This work will support planners, policymakers, and operators and offer a foundational understanding of how governance arrangements shape reliability, resilience, and system performance.]]></description>
      <pubDate>Fri, 01 May 2026 19:58:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2698370</guid>
    </item>
    <item>
      <title>Using Unmanned Aerial Systems to Identify and Manage Protected Species Habitat</title>
      <link>https://rip.trb.org/View/2604538</link>
      <description><![CDATA[Unmanned aerial systems (UAS) or drones provide a unique but mostly unexploited opportunity to improve the Texas Department of Transportation (TxDOT)'s right-of-way management and its current practices for planning, designing, constructing, and maintaining habitat. The research team will develop guidance on the use of UAS to identify and manage protected species habitat and determine the efficacy of multi- and hyper-spectral imagery for identifying and mapping endangered plant populations and potential pollinator conservation areas.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:28:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604538</guid>
    </item>
    <item>
      <title>Investigation of Vulnerable Road User Fatalities and Serious Injuries on Freeways</title>
      <link>https://rip.trb.org/View/2601433</link>
      <description><![CDATA[Although pedestrians, bicyclists, and other vulnerable road users are not “supposed” to be present on freeways and other high-speed limited access roadways, a substantial proportion of all vulnerable road user (VRU) crashes occur in these environments. Due to high speeds, casualty severity is often high, resulting
in many deaths and severe, life-changing injuries. These events heavily burden victims, families, and medical insurance programs funded by employers and taxpayers.

Spot-checks of the North Carolina Department of Transportation (NCDOT) Bicyclist and Pedestrian Crash Map and findings from research in other states indicate these casualties involve wide-ranging circumstances. A few examples include individuals attempting to cross a freeway or other high-speed limited-access roadway at grade, drivers walking to find help with a disabled vehicle, on-the-job incidents involving road workers and first responders, and crashes involving unhoused people who camp on the right-of-way.

This action-focused project will conduct a thorough review of previous research on freeway/expressway VRU casualties, develop a typology of non-overlapping categories that can be used to analyze them, compile the North Carolina VRU casualty data for freeways and other highspeed limited access roadways, review crash narratives to verify that they occurred on such a roadway (and not, for example, on the arterial level of a freeway overpass), manually assign each fatality and serious injury to one of the categories in the typology, prepare maps that illustrate their location and nature, and identify both locationally-specific and statewide actions that can be taken by NCDOT and other agencies to reduce the frequency and severity of VRU crashes on high-speed limited access roadways.​]]></description>
      <pubDate>Thu, 18 Sep 2025 00:57:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601433</guid>
    </item>
    <item>
      <title>Pavement Conditions Assessment and Prediction (PCAP): A geospatial machine learning approach to inform decision-making</title>
      <link>https://rip.trb.org/View/2570737</link>
      <description><![CDATA[The Maine Department of Transportation (MaineDOT) continues to observe an increased rate of pavement deterioration on its 8,800 mile roadway network, which is the largest and most heavily used component of the transportation system under the MaineDOT’s jurisdiction. Pavement deterioration is governed by a variety of factors, including traffic load, quality and design of the pavement structure, increased frequency of climatic events like freeze-thaw cycles, topographic influences and drainage, and geologic considerations like the native subgrade soils. While these factors have been identified individually as potential attributes to pavement degradation and distress, it is likely the confluence of several attributes that impute the greatest rate of degradation on pavement systems. However, the combination(s) of attributes linked to varying degrees of the pavement degradation rate remain poorly understood and must be identified to make informed decisions regarding resource allocation. 
This project seeks to identify and link the combination(s) of attributes described in the preceding section (e.g. pavement design/structure/quality, traffic loading, environmental stressors) to temporal and spatial differences in the rate of pavement degradation on MaineDOT’s highway network; i.e. to understand the relative influence of attributes imputing pavement distress. By working with the MaineDOT, UMaine will use existing and/or collect new pavement quality data (geo-located cracking index values) using the Automatic Road Analyzer (ARAN) to quantify the degree of pavement distress. ARAN data surveyed across the state will allow an assessment of variations in pavement quality across pavement types (e.g. new construction, rehabilitation, spot improvements, LCP, preservation paving), regions/space (i.e. for consideration of climate, geology, drainage, wetness, soil, and traffic loading) and epochs (time since last paving or improvement). 
The project is expected to consist of three components:
Phase 1a (3-6 months): A literature review of existing studies and methods that incorporate data-driven analyses of spatial and/or temporal differences in the rate of pavement degradation. 
Phase 1b (18-21 months): Data-collection and integration, mapping & visualization, and predictor selection and attribution of factors influencing pavement degradation rates via machine learning. 
Phase 2: (18 months): Extension and refinement of Phase 1b to develop a tractable forecasting model to predict the degradation rate of pavement systems.



]]></description>
      <pubDate>Wed, 02 Jul 2025 11:39:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2570737</guid>
    </item>
    <item>
      <title>How Complete are Your City’s Streets? Evaluating the Completeness of Urban Streets Using Big Data and Computer Vision</title>
      <link>https://rip.trb.org/View/2553162</link>
      <description><![CDATA[The main objectives of this project are: (1) development and validation of detection methods on the presence and width of individual elements of complete streets at street level, (2) development of a numeric index and typology to rate the completeness of streets, (3) curation of a publicly accessible database of various elements of complete street data in Atlanta metro; and (4) demonstration of how the rating/typology can be used to help users easily communicate and utilize the data in planning and policy decisions. Additionally, this project will provide an interactive map dashboard of non-residential urban streets in the Atlanta metropolitan region to visualize and communicate the data with the stakeholders. Understanding the current condition of complete street networks is an imperative first step in planning and policy interventions. The presence and the conditions of the eight elements of complete street segments, as reflected in the rating system, will offer critical information about which areas in the street network should be prioritized for complete street upgrades and what specific form these upgrades would entail. The knowledge of complete street elements will also help in assessing whether investing in complete street design and construction influence people’s mode choices towards more active mobility and transit. While there are numerous elements that form the concept of complete streets such as public transit facilities, pedestrian and bicyclist accommodations, traffic calming, and streetscaping, this project focuses on the elements that determine the allocation of street space, such as sidewalks, bike lanes, and street parking, and save other non-surface objects for future studies, such as signboards, walk signals, and other fixtures.

This project plans to collect data on both the presence of complete streets elements and their cross-sectional width where applicable. This detection uses aerial and street view images together as one input. The primary data source for image data will be Google Street View and Google Maps API. By validating the detected result through the comparison with the well-established data, this project will test the potential of the methodology as a low-cost alternative to the existing data collection system. All data will be collected at the street segment level. The relationship between complete streets and travel behavior outcomes in terms of urban vitality and public health will be demonstrated by using urban vitality data (e.g., daily median spend for each POI from Safegraph) and mobility pattern (foot traffic data from ADVAN Research). All data will be collected at the street segment level.]]></description>
      <pubDate>Thu, 15 May 2025 14:42:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553162</guid>
    </item>
    <item>
      <title>Enhancing Transportation Safety with InSAR Land Subsidence Monitoring</title>
      <link>https://rip.trb.org/View/2475283</link>
      <description><![CDATA[Land subsidence is a gradual downward movement and deformation of the Earth's surface. It is driven by geophysical processes such as sediment compaction, tectonic activity, erosion, and human factors like excessive groundwater extraction, mining, and urban development. This study addresses the urgent need to quantify and mitigate the impacts of land subsidence on transportation infrastructure through an integrated approach utilizing Geographic Information Systems (GIS), Interferometric Synthetic Aperture Radar (InSAR), and the Analytic Hierarchy Process (AHP). Focusing on East Baton Rouge Parish, Louisiana, the research examines areas prone to subsidence from 2017 to 2020, specifically targeting critical infrastructure such as Interstate 10, Interstate 12, and major bridges over the Mississippi River. Using a multi-criteria decision analysis framework through AHP, the study systematically prioritizes factors contributing to subsidence, including soil composition, land use/land cover, groundwater extraction rates, and slope stability, leading to the development of detailed susceptibility maps. Integrating machine learning algorithms further enhances the predictive accuracy of risk assessments and infrastructure planning. 
The following tasks will be performed to achieve the objectives of this study: Task 1: preprocess high-resolution Sentinel-1 SAR datasets for InSAR analysis, which generates detailed deformation fields through interferometric processing and time-series analysis. Task 2: apply AHP to assign weights to various subsidence drivers. Task 3: integrate spatial datasets within GIS to create risk maps. Task 4: validate the risk maps using ground truth data from global navigation satellite system observations and historical subsidence records. Task 5: perform temporal analysis of subsidence trends to forecast future deformation patterns, enabling the development of proactive intervention strategies. Task 6: report and share the results. 
The outcomes of this study include practical susceptibility maps and predictive models, offering valuable insights for transportation and urban planning stakeholders. These tools enhance infrastructure resilience by aiding in maintenance prioritization, optimizing land use, and informing policy decisions, ultimately supporting sustainable development by addressing subsidence risks, ensuring the long-term safety and efficiency of transportation networks, and advancing geospatial and remote sensing methodologies for land deformation studies.
]]></description>
      <pubDate>Fri, 20 Dec 2024 19:40:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2475283</guid>
    </item>
    <item>
      <title>Improve highway safety by reducing the risks of landslides (Phase 2).</title>
      <link>https://rip.trb.org/View/2440012</link>
      <description><![CDATA[Geologic hazards including slope failures, landslides, mudflows, debris flows, etc. and hydrological hazards related to floods and stormwater surge can be destructive to transportation infrastructure and threaten property and human life along the highway and roads. Landslides alone cause thousands of deaths and many billions of dollars in damage every year. 

Morgan State University team proposes a multi-phase (multi-year) project focusing on safety of transportation infrastructure systems by preventing geohazard, specifically slope failure and landslides and minimizing impacts of geohazard. This project will employ an integrated approach of geotechnical and Artificial Intelligence (AI)/Machine Learning methods for assessing conditions of geotechnical assets, such as cut slopes and embankment of the Maryland Department of Transportation (DOT SHA) and delineating landslides and high-risk areas. 

The objectives (tasks) of the proposal include: (1) with AI/Machine Learning approaches assess the risks of landslides based on soil/rock types, weather conditions, mechanical properties of slope materials, stream gage station flow data, pavement material and design, and the status of existing retaining structures along the selected highway sections, using Maryland as case studies, (2) identify and map the high-risk areas based on controlling factors such as geometry and mechanical properties of soil or rock, and triggering factors, including gravitational and hydraulic forces, using available survey data, remote sensing and LIDAR data and other factors like transportation modes, (3) design and test protocols for real time monitoring at selected sites in consultation with DOT SHA staff, and (4) recommend strategies for reducing the risks of landslides with real-time monitoring for the high-risk areas, and improving the safety of the transportation infrastructure. All the methods and strategies can be transferred to other states or regions with similar geological conditions and engineering configurations. Phase 1 of this project will primarily cover task 1 and part of task 2. Phase 2 will continue part of task 1 and task 2. This project will primarily complement the ongoing project sponsored by the Maryland DOT SHA (see more information in TRID) led by Zhuping Sheng in collaboration with Carnegie Mellon University (CMU) (Dr. Sean Qian). In this phase the research team will also expand their collaboration with CMU team (Dr. Christoph Mertz) by including technology transfer in photographical images processing to build conceptual models and identify slope failures.   

Dr. Zhuping Sheng has experience in geohazards assessment and mitigation, geotechnical and water resources engineering. As PI Dr. Sheng will coordinate the efforts in collaboration with MDOT/SHA and advise other faculty and postdoctoral research associates and graduate students to carry out the project. The team includes Co-PIs, Dr. Oludare Owolabi with experience in transportation engineering and resilient infrastructure and Dr. Yi Liu with experience in geohazards, land subsidence and landslides and geotechnical engineering. They are currently conducting research supported by MDOT SHA, which provides a strong foundation for future collaboration with the partner MDOT SHA and others for technical transfer. 

This program includes a summer internship program with two students and one graduate team for development of future workforce in transportation safety led by Dr. Owolabi in cooperation with MSU AI/ML program through National Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) led by Dr. Kofi Nyarko. Students have participated in and will continue to participate in exchange programs and deployment partner symposium and other activities. Through this project the MSU team will continue to expand collaboration with CMU and other partner institutes via faculty meetings, seminars, national summit, and other venues, which provides great opportunity for professional development.
]]></description>
      <pubDate>Sat, 12 Oct 2024 12:09:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440012</guid>
    </item>
    <item>
      <title>Landslide Hazard Identification and Monitoring</title>
      <link>https://rip.trb.org/View/2440046</link>
      <description><![CDATA[Debris flows, sometimes referred to as mudslides, mudflows, or debris avalanche, are fast-moving landslides that can carry large items such as boulders, trees, and cars and can destroy objects in their paths. Typically, debris flows occur during periods of intense rainfall or rapid snowmelt, and usually start on hillsides or mountains. Considerable roadway damage occurred in July 2023 VT rains causing from uphill landslide and debris flows spilling onto roadways. This was a new phenomenon at this magnitude for Vermont Agency of Transportation (VTrans) which required them to engage outside expertise to understand long- and short-term risk, develop standard details for rapid repairs, and in some cases, case specific recommendations.

There is safety and cost-saving value in identifying the future hazard potential and to develop strategies for preparing for, warning of, and responding to the debris flow hazard. Identifying higher risk areas and monitoring of these areas will facilitate asset management, improve roadway safety, reduce the need for emergency road closures, and reduce long- term costs associated with repeated emergency responses.
]]></description>
      <pubDate>Thu, 10 Oct 2024 14:13:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440046</guid>
    </item>
    <item>
      <title>Delivery Deserts: Mapping, Understanding, and Overcoming Service Challenges</title>
      <link>https://rip.trb.org/View/2422984</link>
      <description><![CDATA[Delivery services play an integral part in our economy and daily lives. They are the backbone of e-commerce, connecting businesses with consumers across large distances. Moreover, they facilitate access to essential goods such as groceries and medicines, especially for populations who may have difficulty traveling long distances or leaving their homes. This research aims to address reasons that delivery services create “delivery deserts”—regions where the efficiency of delivery services is severely limited or non-existent. These desert regions impact national economic growth by limiting business opportunities for rural entrepreneurs and increasing costs for rural consumers. They also underscore broader societal disparities between urban and rural communities. This project employs a detailed, two-phase approach to tackle delivery deserts in America: 1) identifying and understanding the problem by quantifying and mapping these deserts and 2) identifying the specific barriers to delivery access. By identifying these poorly serviced areas, the study team not only sheds light on existing disparities but also pave the way for a better future. This information can guide delivery service providers and policy makers about strategic planning opportunities, thereby fostering economic growth and improving quality of life for individuals residing in these delivery desert zones.]]></description>
      <pubDate>Thu, 29 Aug 2024 16:37:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422984</guid>
    </item>
    <item>
      <title>Cemetery Mapping for Indigenous and Enslaved People's Remains</title>
      <link>https://rip.trb.org/View/2422892</link>
      <description><![CDATA[This research is vital to acknowledging, protecting and preserving the burial sites of indigenous and enslaved peoples as well as other cultural and historically significant communities (such as historic African American communities). Understanding the location and mapping these sites will allow the North Carolina Department of Transportation (NCDOT) to plan and design projects that mitigate or eliminate impacts to these important cultural resources. This will allow NCDOT to engage with local communities associated with burial sites in advance and develop projects that avoid burial site impacts and strengthen community relations. A reduction of unanticipated burial sites not only serves to protect these sites, it also saves the NCDOT from lengthy and costly delays associated with discovering a burial site during construction.

This project has a high level of urgency, as local communities and archaeology experts are concerned that rapid development and increased storm events will impact these sites if they are not documented soon. Having a comprehensive geospatial data set that includes site locations, cultural significance, and allows for sites to be easily added to the dataset is vital to ensuring the burial sites of indigenous and enslaved peoples are acknowledged and protected. This project will expand upon existing NCDOT and Office of the State Archaeologist (OSA) mapping and datasets by providing a methodology to capture the cultural and historical significance of burial sites, use-community driven approaches to identifying new sites, employ a field verification process, and highlight opportunities to embed these approaches into existing NCDOT project planning and development processes.​

Beyond expanding a mapping dataset for cemeteries and burial sites, the project team will develop a community engagement methodology to allow community members to participate in the identification of unmapped burial sites and inform the historical and cultural significance of sites. Using a county-level project study area (to be established through conversations with the NCDOT steering committee and research team) this project will yield a proof of concept and a community participation roadmap for engaging communities around the state in an effort to map previously unmapped cemetery and burial sites, with a focus on indigenous and enslaved peoples and culturally historic community cemeteries.]]></description>
      <pubDate>Thu, 29 Aug 2024 07:50:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422892</guid>
    </item>
    <item>
      <title>Pedestrian Level of Traffic Stress (PLTS) Application and Validation</title>
      <link>https://rip.trb.org/View/2401757</link>
      <description><![CDATA[Many of the existing methods to evaluate pedestrian and bicyclist suitability require a large number of inputs, some of which are not available in typical roadway inventory data (e.g., pavement condition, on-street parking coverage, heavy vehicle proportion), making them impractical for most agencies to apply. Some of these methods also require statistical modeling expertise or specialized software to run, further putting them out of reach for many agencies. Occasionally, their outputs do not make intuitive sense. The Year 1 
Center for Pedestrian and Bicyclist Safety (CPBS) project created a well-researched, standardized version of a table-based, Pedestrian Level of Traffic Stress (PLTS) tool. It incorporates many of the most important and easy-to-collect roadway factors associated with pedestrian suitability from a) existing pedestrian suitability methods and b) the pedestrian safety literature. This Year 2 project will build on the previous effort to apply the method in at least two case study communities (including the City of Milwaukee, Wisconsin) and validate the PLTS categories in a sample of locations against real pedestrian stress ratings from public surveys and police-reported pedestrian crash data. The goal is to establish a validated, practical PLTS method that agencies across the country can use to estimate suitability and stress for pedestrians in various contexts, ultimately leading to safer and more enjoyable walking and rolling conditions.  

As done for the BLTS in 2012, the research team will produce a final technical report that includes a description of the PLTS method. This report will include PLTS tables and example PLTS maps from communities where the method has been tested. The final report will discuss how well the PLTS method works for practitioners and matches with public perceptions of pedestrian stress and pedestrian crash locations.  As done for the BLTS in 2012, the research team will produce a final technical report that includes a description of the PLTS method. This report will include PLTS tables and example PLTS maps from communities where the method has been tested. The final report will discuss how well the PLTS method works for practitioners and matches with public perceptions of pedestrian stress and pedestrian crash locations.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401757</guid>
    </item>
    <item>
      <title>Elevating Traffic Safety in Native American Communities: A Comprehensive Approach with Online Mapping and Crowdsourcing Solutions</title>
      <link>https://rip.trb.org/View/2401748</link>
      <description><![CDATA[This proposed project will focus on investigating the effectiveness of modern web-based tools and technologies in improving traffic safety education and decision-making within Native American communities. Technologies that will be implemented and investigated include, but are not limited to, spatial data visualization, spatial data management, spatial analysis, spatial education, and internet mapping. Leveraging free and open-source software programs, modules, and libraries, the research team aims to implement a tailored online mapping and analysis portal, along with a crowdsourcing tool. This approach ensures a cost-effective solution for the Native American communities we are committed to assisting and serving. The proposed project will begin by organizing comprehensive training workshops in collaboration with the New Mexico Local Technical Assistance Program (LTAP). These workshops will focus on the practical applications and benefits of an existing online traffic crash mapping and analysis portal developed by the University of New Mexico’s Center for Pedestrian and Bicyclist Safety (CPBS). The proposed project will also implement a crowdsourcing web application based on Volunteered Geographic Information (VGI), and at the same time incorporate gamification elements to encourage crowdsourcing of traffic crash data and addressing issues related to insufficient traffic data.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401748</guid>
    </item>
    <item>
      <title>Administration of Highway and Transportation Agencies. A Framework for Data Exchanges Between Transportation Agencies and Third-Party Mapping Organizations</title>
      <link>https://rip.trb.org/View/2325858</link>
      <description><![CDATA[The objective of this research is to develop a framework for a comprehensive data specification that allows for a two-way exchange of information between transportation agencies and third-party mapping organizations so each can ingest data with uniform attributes and metadata.]]></description>
      <pubDate>Wed, 24 Jan 2024 15:38:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325858</guid>
    </item>
    <item>
      <title>Improve highway safety by reducing the risks of landslides</title>
      <link>https://rip.trb.org/View/2292645</link>
      <description><![CDATA[Geologic hazards including slope failures, landslides, mudflows, debris flows, etc. and hydrological hazards related to floods and stormwater surge can be destructive to transportation infrastructure and threaten property and human life along the highway and roads. Landslides alone cause thousands of deaths and many billions of dollars in damage every year. Morgan State University (MSU) team proposes a multi-phase (multi-year) project focusing on safety of transportation infrastructure systems by preventing geohazard, specifically slope failure and landslides and minimizing impacts of geohazard. This project will employ an integrated approach of geotechnical and Artificial Intelligence (AI)/Machine Learning (ML) methods for assessing conditions of geotechnical assets, such as cut slopes and embankment of the Maryland Department of Transportation State Highway Administration (MDOT SHA) and delineating landslides and high-risk areas. The objectives (tasks) of the proposal include: (1) with AI/Machine Learning approaches assess the risks of landslides based on soil/rock types, weather conditions, mechanical properties of slope materials, and the status of existing retaining structures along the selected highway sections, using Maryland as case studies, (2) identify and map the high-risk areas based on controlling factors such as geometry and mechanical properties of soil or rock, and triggering factors, including gravitational and hydraulic forces, using available survey data, remote sensing and LIDAR data and and other factors like transportation modes, (3) design and test protocols for real time monitoring at selected sites in consultation with MDOT SHA staff, and (4) recommend strategies for reducing the risks of landslides with real-time monitoring for the high-risk areas, and improving the safety of the transportation infrastructure. All the methods and strategies can be transferred to other states or regions with similar geological conditions and engineering configurations. Phase 1 of this project will primarily cover task 1 and part of task 2. This project will primarily complement ongoing projects sponsored by the MDOT SHA (see more information in TRID) led by Zhuping Sheng. Dr. Jiang Li has experience in both transportation research and environmental hazards. The former focuses on the mechanical behavior of road subgrades and the latter addresses the geological or hydrological hazards that may adversely affect the regional transportation infrastructure and traffic safety.  As principal investigator (PI) Dr. Li will coordinate the efforts in collaboration with MDOT SHA and advise other faculty and postdoctoral research associates and graduate students to carry out the project. The team includes Co-PIs, Zhuping Sheng, Oludare Owolabi and Yi Liu who are currently conducting research supported by MDOT SHA, which provides a strong foundation for future collaboration with the partner MDOT SHA and others for technical transfer. This program includes a summer internship program with two students and one graduate team for development of future workforce in transportation safety in cooperation with MSU AI/ML program led by Dr. Owolabi. Students will also participate in exchange programs and deployment partners symposium and other activities. Through this project the MSU team is expected to expand collaboration with Carnegie Mellon University (CMU) and other partner institutes via faculty meetings, seminars, national summit, and other venues, which provides great opportunity for professional development.]]></description>
      <pubDate>Mon, 20 Nov 2023 20:00:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292645</guid>
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