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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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      <title>Applications of data science and big data analytics in underground transportation infrastructure (UTI-UTC 02)
</title>
      <link>https://rip.trb.org/View/2543307</link>
      <description><![CDATA[This project focuses on harnessing the power of data science, machine learning (ML), and big data analytics to enhance the construction, operation, and maintenance of underground transportation infrastructure (UTI). By collecting and processing large-scale datasets from tunneling projects—such as TBM performance data, geotechnical records, and operational logs—the research develops predictive models to assess ground conditions, detect anomalies, and forecast potential structural failures. Key objectives include refining data-driven methods for real-time TBM state prediction, designing algorithms to detect defects like cracks or rock incursions, and creating interactive visualization tools to support decision-making. The project emphasizes scalable ML architectures (e.g., deep learning, recurrent neural networks) to improve the resilience, safety, and cost-efficiency of UTI systems. Its outcome serves as a foundation for intelligent tunneling and infrastructure health monitoring frameworks in modern urban environments.
]]></description>
      <pubDate>Wed, 07 May 2025 19:00:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543307</guid>
    </item>
    <item>
      <title>Data-Supported Quantification of Bridge Deck Degradation Using GDOT’s Road Maintenance Data and Other Data Available </title>
      <link>https://rip.trb.org/View/2508900</link>
      <description><![CDATA[The primary aim of this project is to leverage the Georgia Department of Transporation (GDOT)'s extensive data resources to accurately quantify damage or degradation in bridge deck slabs, with the overarching goal of improving safety and mobility. This objective encompasses three main goals: (1) Implementing a geospatial data visualization approach to monitor road surface maintenance activities specifically on bridge decks, (2) Developing methods to quantify bridge deck degradation effectively, and (3) Investigating the impact of changes in traffic patterns on bridge maintenance and condition data.]]></description>
      <pubDate>Tue, 11 Feb 2025 14:59:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2508900</guid>
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    <item>
      <title>Virtual Reality as a Tool to Enhance Public Involvement Process</title>
      <link>https://rip.trb.org/View/2447165</link>
      <description><![CDATA[This project explores the use of Virtual Reality (VR) as a public involvement tool in transportation planning, offering an immersive experience to engage communities in proposed project designs. By comparing traditional visualizations (e.g., maps, 2D renderings), 3D videos, and VR immersion, the study aims to assess the effectiveness of VR in communicating complex design elements to the public. The research involves developing a VR simulation of a double roundabout in Bristol, Virginia, enabling participants to experience and provide feedback on the design from the perspectives of drivers, pedestrians, and cyclists. Ultimately, the project will produce a guidance document to aid the Virginia Department of Transportation (VDOT) in implementing VR for future public involvement efforts.]]></description>
      <pubDate>Wed, 30 Oct 2024 15:36:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447165</guid>
    </item>
    <item>
      <title>Use of Enhanced Visualization Technology to Assess the States of Coastal Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2427597</link>
      <description><![CDATA[The main goal of this project is to devise a mechanism that incorporates the needs and preferences of coastal communities in the development of a decision-making support tool that assesses risks for transportation corridors based on performance and that improves infrastructure and services in support of the blue economy. This project looks to enhance the transportation infrastructure of coastal communities through a methodology that assesses accessibility based on three states: perceived, actual, and designed, using mixed reality visualizations as educational tool. Integrating public and community participation in decision-making processes remains a challenging task for transportation planning, as there is often a gap between what is achievable and what is implemented. Incorporating visualization tools has high potential to facilitate communication with the community, both to convey information and to gather clear opinions and ideas from residents.]]></description>
      <pubDate>Wed, 11 Sep 2024 12:49:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2427597</guid>
    </item>
    <item>
      <title>SPR-4918:  Enhancing Pavement Instrumentation and Monitoring: A Novel Edge-First, Network Level Solution for Pavement and Roadside Sensors and Live Data Visualization</title>
      <link>https://rip.trb.org/View/2422896</link>
      <description><![CDATA[This project will develop an Avena-powered hardware system to sample in-pavement sensors, host a camera package and analysis software, and integrate remote data storage and visualizations. The initial prototypes will be installed at the Accelerated Pavement Testing (APT) facility and I-65 near Lebanon, improving the utility of embedded pavement sensors for comprehensive road health monitoring.]]></description>
      <pubDate>Thu, 29 Aug 2024 08:48:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422896</guid>
    </item>
    <item>
      <title>Digital Twins as a Catalyst for Sustainable and Smart Cities</title>
      <link>https://rip.trb.org/View/2350747</link>
      <description><![CDATA[This project aims to develop an urban digital twin for the city of Austin that assists city planners and managers to build a sustainable and smart city. The proposed urban digital twin will incorporate a data management and visualization platform, a real-time city monitoring system, an integration of predicting models, and a dynamic urban simulation environment to achieve effective city management, better resource allocation, more efficient transportation operation, and more proactive responses to risks. The data management and visualization platform will store and publish static and real-time urban data. The platform will enable API access and data download to facilitate third-party use. The real-time city monitoring system will access and process multiple data sources, including public real-time dataset, camera, and road sensors, for traffic monitoring and accident detecting. The digital twin will incorporate a traffic predicting model and a risk predicting model using graph-based deep learning methods. The project team will also build a dynamic urban simulation environment for the city of Austin, including a 3D city model, a road network model, and a traffic simulator. These digital twin modules will operate cooperatively by interacting with each other to synchronize real-world and virtual information. The expected outputs of this project include online platforms, software, technical reports, and research papers.]]></description>
      <pubDate>Tue, 12 Mar 2024 10:57:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2350747</guid>
    </item>
    <item>
      <title>Guide to Using 3D Models for Construction Inspection</title>
      <link>https://rip.trb.org/View/2348475</link>
      <description><![CDATA[Many state departments of transportation (DOTs) have implemented three-dimensional (3D) models for project planning, design, and construction; however, field inspection staff need to develop the requisite skills for using digital inspection procedures. Some state DOT staff have limited exposure to or proficiency in using 3D models for uniform inspection processes and procedures. Three-dimensional models provide detailed information, efficiency, and visualization capabilities. These models can replace the current standard two-dimensional (2D) plan sets and offer effective tools and streamlined practices. Construction inspectors are expected to use data from 3D models to measure, verify, and accept construction materials and payment quantities. Many inspectors access this information through PDF or paper plans. Digital 3D models eliminate the need for some portions of 2D plans since details and visualizations are in the models. Identifying what construction inspectors need for optimal use of data from 3D models is an area of opportunity for state DOTs who can potentially develop digital solutions for construction contract documents. Some states are piloting the use of 3D design models as the primary contract document. Other states adopting 3D models plan to continue to make 2D plans available for a limited time. There is a need to prepare staff for the migration to 3D models and to develop a 3D model use guide and training for construction inspectors.

OBJECTIVE: The objective of this study is to develop a guide for construction inspectors that (1) describes what information construction inspectors need to utilize data from 3D models and how to best document construction activities, and (2) identifies and catalogs the business needs and core competencies of construction inspectors using data from 3D digital models.]]></description>
      <pubDate>Mon, 04 Mar 2024 19:58:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2348475</guid>
    </item>
    <item>
      <title>Reinforcement Learning-Assisted Virtualized Security Framework for CAVs</title>
      <link>https://rip.trb.org/View/2335052</link>
      <description><![CDATA[Connected and autonomous vehicle (CAV) technology has brought a major transformation in the transportation sector by significantly improving the mobility of people and goods through advanced communication, sensing, and computing capabilities. However, CAVs can be hacked due to vulnerabilities in the in-vehicle software, resulting in physical damage and jeopardizing the safety of drivers and passengers. By exploiting the vulnerabilities, hackers can perform malicious actions ranging from draining batteries and taking control of the steering wheel to disabling the alarm system. The existing security solutions implemented in CAVs are static and cannot withstand evolving security threats such as Advanced persistent threats (APT) and ransomware attacks. Moreover, costly update procedures leave the CAV software unpatched for a long time, making the CAVs vulnerable to new exploits.
This project aims to develop a virtualized security framework to improve the resiliency of CAV software. The framework will allow the execution of different code variants of CAV software to introduce uncertainty in the attack surface. The proposed framework will integrate the Network Functions Virtualization paradigm to implement the code variants of CAV software as virtual network functions. The proposed framework will offer the ability to optimally deploy the appropriate virtual network functions using a reinforcement learning agent. The reinforcement learning agent perceives the threat environment of CAVs and provides the optimal code variant that maximizes the resiliency of CAV software while ensuring their Quality of Service (QoS) requirements. This project aims to accomplish the following goals: (1) develop a virtualized security framework that allows fast and dynamic provisioning of different code variants of CAV software, (2) design novel and efficient algorithms designed based on game theory and Artificial Intelligence (AI) techniques including Deep Learning and Generative Adversarial Networks (GANs) to determine the optimal code variant, (3) evaluate the performance of reinforcement learning algorithm using simulations, and (4) build a proof-of-concept of the proposed security framework and evaluate its performance using real-world experiments.
]]></description>
      <pubDate>Fri, 09 Feb 2024 19:36:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2335052</guid>
    </item>
    <item>
      <title>Web-Based Tool to Advance Geotechnical Data Interchange and Reliability-Based Site Characterization</title>
      <link>https://rip.trb.org/View/2296662</link>
      <description><![CDATA[Web-Based Visualization
A web-based platform has been implemented by the DOTD for several large highway and bridge design and construction projects. These larger projects focused on the ability to interactively visualize and interpret data from soil borings, CPTs, geophysical data, and conventional field survey data. This project will standardize this visualization process for all projects.

GEC-5: 
This project will also demonstrate how geotechnical engineers and consultants can efficiently quantify the uncertainty of site conditions and develop soil design models based on statistical analysis of soil boring data. The uncertainty model can be interactively evaluated based on the methods presented in FHWA's Geotechnical Engineering Circular (GEC) No. 5 - Geotechnical Site Characterization.

DIGGS: 
Additionally, the project will demonstrate how DIGGS can be implemented into the web-based platform for standardized geotechnical data exchange. The implementation of DIGGS to efficiently increase the quality of geotechnical data deliverables as a digital asset. Other benefits of implementing DIGGS in geotechnical data management, visualization, and geotechnical design process will also be researched.
]]></description>
      <pubDate>Tue, 28 Nov 2023 10:41:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2296662</guid>
    </item>
    <item>
      <title>Extended Reality Possibilities in the Airport Environment</title>
      <link>https://rip.trb.org/View/2226008</link>
      <description><![CDATA[Extended reality (XR) is the umbrella term for immersive technologies such as augmented reality (AR), virtual reality (VR), and mixed reality (MR). AR blends the real world with a digital world, virtual reality immerses the user in a digital world, and MR is a mix of both. XR is a great way to put written concepts into some type of visualization. The military has been using XR in certain applications to improve efficiency, safety, and productivity. Airlines are using XR to help train flight attendants and aviation mechanics and as an entertainment option for their passengers. The number of applications and industries where XR can provide benefits is growing.

XR used in other industries may also apply in the airport environment for airport operators. The benefits can be internal (e.g., used by employees in their day-to-day work or in training) or external (e.g., used by passengers and other stakeholders). Further research is needed to identify what immersive technologies could be most useful to airport operators. 

The objective of this project is to develop a report on the potential applications of XR at airports for airport operators.]]></description>
      <pubDate>Tue, 08 Aug 2023 06:59:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2226008</guid>
    </item>
    <item>
      <title>A Deep Learning-based Network-wide Traffic Prediction Model for Integrated Corridor Management Systems</title>
      <link>https://rip.trb.org/View/2194299</link>
      <description><![CDATA[The objectives for this study are as follows: 
(1) Develop a deep learning-based modeling framework for high-fidelity traffic prediction utilizing traffic sensors, link capacity, socio-economic, and land use data; 
(2) Develop a predictive strategy evaluator to assess the impact of potential traffic management strategy given an incident and predicted traffic; 
(3) Develop a data pipeline that can feed a range of datasets and deliver prediction outputs to a visualization application; and 
(4) Create a data visualization dashboard providing traffic flow information (such as volume and travel time by link) to show future traffic forecast.]]></description>
      <pubDate>Wed, 07 Jun 2023 07:33:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2194299</guid>
    </item>
    <item>
      <title>Evaluation, Maintenance, and Evolution of the Traffic Polling &amp; Analysis System</title>
      <link>https://rip.trb.org/View/2120729</link>
      <description><![CDATA[The main goal of this project is to evaluate the traffic polling and analysis system in terms of performance, features, and visualization tools as well as efficiencies gained by using the system. Consistent with this goal, the objectives of this project are to evaluate the traffic polling and analysis system’s performance, features, and visualization tools; to evaluate efficiencies gained by the use of the system; and to evaluate practicality and cost-effectiveness of implementing further enhancements to the system.]]></description>
      <pubDate>Mon, 20 Feb 2023 15:00:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2120729</guid>
    </item>
    <item>
      <title>Visualization and demonstration of risk identification and evaluation for extreme events</title>
      <link>https://rip.trb.org/View/2096589</link>
      <description><![CDATA[This task addresses one of the greatest difficulties in explaining the techniques in evaluation and management of risk from extreme events to audience of various backgrounds. The application of risk management on bridges and tunnels needs to be properly communicated to stakeholders and other audience that may use these concepts. Conceptual visualization backed by sound principles and realistic parameters will be used to ensure the ease of comprehension and accurate understanding. The product will assist in better compliance with the statutory requirement in risk management.]]></description>
      <pubDate>Fri, 13 Jan 2023 14:49:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2096589</guid>
    </item>
    <item>
      <title>Innovative Visualization Methods</title>
      <link>https://rip.trb.org/View/2085735</link>
      <description><![CDATA[This research will identify innovative visualization methods to effectively analyze, map, display, and report a wide range of information to support planning functions.]]></description>
      <pubDate>Fri, 16 Dec 2022 14:15:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2085735</guid>
    </item>
    <item>
      <title>Maintaining and Operating the Data Visualization Center (DVC) 2.0</title>
      <link>https://rip.trb.org/View/2071558</link>
      <description><![CDATA[The Data Visualization Center (DVC) provides effective data and information visualization service including the development of various visually pleasing and information-rich charts, maps, and figures for the entire FHWA and others.]]></description>
      <pubDate>Mon, 28 Nov 2022 14:20:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071558</guid>
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