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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzgyIiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+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>National Access Evaluation Pooled Fund - Phase III</title>
      <link>https://rip.trb.org/View/2777536</link>
      <description><![CDATA[The National Access Evaluation project has two main objectives. The first is to calculate national Census block-level Access datasets that can be used by partners in state and local transportation system evaluation, performance management, planning, and research efforts. The second is to add value to these datasets through research into questions of interest to Minnesota Department of Transportation (MnDOT) and the pooled fund Technical Advisory Panel (TAP), including research into the variability and change in access, as well as exploring access data in combination with other datasets. Access conditions will be described for 2025, 2026, 2027, 2028, and 2029.
Accessibility evaluation has applications in a variety of areas: Strengthening Cost-Benefit Analysis - Understanding the impacts of transportation investments requires quantification of benefits. These benefits include economic opportunities, such as increased access to jobs, healthcare facilities, recreational activities, commercial activity, or other ways to participate fully in the economy. Access measures these opportunities in a way that can be applied in the context of a given project, or used to prioritize among transportation investments based on the expected benefits to different groups. Transportation and Land Use Research - Access calculations can provide a valuable data source for transportation and land use research. Researchers have employed access in models of mode choice and other aspects of travel behavior, linked access to residential property values, and used access to explore the spatial relationship between jobs and worker locations. Study partners can share the datasets produced by this study with consultants and researchers as a component of contracted projects. Performance Management [AD1] - By tracking access over time, transportation agencies at all levels of government can better understand how well their transportation networks support the goal of providing opportunity. Access evaluation can be applied to federal, state, and local performance goals. The reports produced by this study will track access performance each year, and over time as the study progresses. Study partners can share the datasets produced by this study without restriction, including (for example) with municipal and county transportation departments.

]]></description>
      <pubDate>Fri, 11 Sep 2026 19:56:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2777536</guid>
    </item>
    <item>
      <title>A Crowd-sourcing Approach to Developing Visual Datasets for Safety Assets</title>
      <link>https://rip.trb.org/View/2775968</link>
      <description><![CDATA[Recent advances in data management and data-driven analytics offer substantial potential to improve transportation safety asset management by automating labor-intensive condition assessment and data interpretation tasks. However, despite the rapid growth of imagery and sensor data within UDOT, most existing datasets are not directly usable for data-driven applications because they lack standardized, high-quality condition labels. Existing annotation efforts are largely manual, inconsistent, and difficult to sustain at scale, limiting the development and deployment of reliable computer vision and artificial intelligence (AI) tools.

This project addresses these limitations by developing a collaborative, web-based crowd-sourcing platform that enables UDOT personnel and approved stakeholders to efficiently annotate transportation safety asset imagery. The platform will incorporate standardized labeling workflows, quality assurance procedures, and automated metadata generation to create reusable, high-quality datasets that support both operational asset management and future AI applications. The resulting framework will establish a scalable process for transforming raw imagery into curated datasets suitable for inspection, maintenance prioritization, performance monitoring, and future research.]]></description>
      <pubDate>Thu, 10 Sep 2026 12:44:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775968</guid>
    </item>
    <item>
      <title>Completion of Hydrologic Design and Platform Data (TR-792 Phase II)</title>
      <link>https://rip.trb.org/View/2775961</link>
      <description><![CDATA[Flooding is Iowa's most persistent and costly natural hazard, responsible for approximately 80 percent of all federally declared disasters in the state since 1989. The Iowa Highway Research Board (IHRB) Project TR-792 demonstrated that On-Road Structures (ORS) are a practical tool for reducing peak flows and protecting downstream communities and infrastructure. TR-792 identified approximately 250,000 potential ORS sites statewide, developed an automated hydrologic design methodology, and deployed results through the Iowa DOT On-Road Structures (IDOT-ORS) web platform. Watershed-scale modeling in six representative HUC12 watersheds showed that strategically deployed ORS systems can reduce 50-year storm peak flows by approximately 18 percent at watershed outlets.

This Phase II proposal will build upon and expand the work completed by TR-792 by working on three interlinked objectives. Objective 1 extends the automated planning design methodology to all feasible ORS sites across Iowa, including scenario analyses that evaluate the effect of alternative roadway elevation assumptions (e.g., raising the road by one or three feet). Objective 2 develops a surrogate machine-learning model, trained on process-based hydrologic simulations, to enable rapid, large-scale estimation of watershed-scale flood reduction benefits. Objective 3 modernizes the IDOT-ORS platform, integrates all new datasets and scenario modeling capabilities, and deploys a targeted communication strategy, including educational videos, training materials, and digital brochures to translate technical outputs into actionable guidance for county engineers, watershed managers, and emergency management professionals. Similar to the approach followed for TR-792, the proposed research will be performed by a team of researchers from Iowa State University and The University of Iowa.]]></description>
      <pubDate>Thu, 10 Sep 2026 11:49:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775961</guid>
    </item>
    <item>
      <title>County Pavement Preservation Data Collection</title>
      <link>https://rip.trb.org/View/2775960</link>
      <description><![CDATA[Pavement preservation is a cost-effective, proactive approach for maintaining roadway
infrastructure in a state of good repair. However, pavement preservation practices vary substantially across Iowa’s counties, cities, and agencies like the Iowa Department of Transportation due to differences in available resources, technical expertise, staffing levels, and operational constraints. Counties in particular often face significant challenges in identifying the most appropriate preservation treatments for specific pavement conditions. While generally open to adopting new materials, technologies, and preservation strategies, the performance outcomes of county-implemented treatments have not been systematically documented, standardized, or shared statewide. As a result, Iowa counties often lack access to historical performance data and lessons learned that could inform more confident, cost-effective decision-making. To address this gap, researchers at Iowa State University, in partnership with the Iowa County Engineers Association Service Bureau, have developed and are updating the Iowa Pavement Analysis Techniques (IPAT) tool with support from the Iowa Highway Research Board. Although the IPAT tool provides valuable decision-support capabilities, its effectiveness is fundamentally dependent on the availability, consistency, and quality of input data. The primary objective of this proposed study is to collect, standardize, and process pavement preservation and related data from all 99 Iowa counties to support and enhance preservation recommendations. The study will focus on a statewide collection of historic roadway physical and structural data, along with pavement condition, traffic, and pavement preservation treatment data for Iowa’s secondary paved road network. A web-based County Pavement Preservation Database Platform will be developed to store, manage, and visualize these datasets. The resulting statewide core dataset will enable the IPAT tool to provide county engineers with preservation options informed by both current conditions and historical county-level data—an integrated approach that has not previously been available in Iowa counties.]]></description>
      <pubDate>Thu, 10 Sep 2026 11:43:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775960</guid>
    </item>
    <item>
      <title>Ongoing Evaluation of Full-Scale Unpaved Roads Research Test Sites</title>
      <link>https://rip.trb.org/View/2775959</link>
      <description><![CDATA[The goal of the proposed project is to evaluate past Iowa Highway Research Board (IHRB) unpaved granular-surfaced (gravel) road test sites to identify ones that could provide useful long-term performance data from additional field tests, and to develop a list of proposed sites, test methods, and analysis methods for a Phase II experimental project. Deliverables include a Data Collection Template, a Site Condition Summary, and a final report that serves as a scope recommendation and roadmap for a Phase II project.]]></description>
      <pubDate>Thu, 10 Sep 2026 11:19:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775959</guid>
    </item>
    <item>
      <title>Human-AI Collaborative Bridge Inspection for Advanced Damage Detection
and Defect Quantification</title>
      <link>https://rip.trb.org/View/2689757</link>
      <description><![CDATA[The aging concrete bridge infrastructure across the U.S. poses increasing risks to safety and reliability, amplifying the urgency to modernize inspection processes. Traditional manual inspection methods are labor-intensive, subjective, and inconsistent, creating barriers to effective infrastructure management. This research proposes an innovative human-AI collaborative framework designed to enhance the durability, resiliency, and technological capabilities of bridge inspections. The project integrates advanced artificial intelligence (AI) and computer vision technologies to automate damage detection and defect quantification. To ensure reliability and trustworthiness, the AI system incorporates rigorous uncertainty quantification methods, increasing transparency and inspector confidence. An active learning component strategically facilitates human-AI collaboration, enabling inspectors to focus on the most critical inspection cases identified by the AI, significantly reducing manual workloads. Additionally, engineering domain expertise, particularly in structural reliability and risk assessment, will inform and prioritize inspection processes within the AI framework. The outcomes of this project will substantially improve the accuracy, efficiency, and reliability of bridge inspections, promoting safer, longer-lasting, and more resilient transportation infrastructure. Anticipated benefits include reduced inspection costs, enhanced public safety, and a proactive approach to infrastructure management that significantly extends service life and supports sustainability. ]]></description>
      <pubDate>Thu, 10 Sep 2026 09:04:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689757</guid>
    </item>
    <item>
      <title>Near Real-time Construction Quality Monitoring and Inspection Protocols
Using Uncrewed Aerial Vehicles</title>
      <link>https://rip.trb.org/View/2691499</link>
      <description><![CDATA[Innovative quality control technologies are used for asphalt materials to ensure smooth and long-lasting pavements. Paver Mounted Thermal Profilers (PMTPs) and Intelligent Compactors (ICs) are among the technologies used for freshly paved mat quality monitoring. However, these technologies are expensive and have some limitations in retrofitting the existing pavers and rollers from different equipment manufacturers. In the first phase of our research, aerial thermal images captured from an Uncrewed Aerial Vehicle (UAV) were used to develop a framework for hot-mix asphalt (HMA) construction quality monitoring. Thermal segregation risk was quantified using three different metrics. An object detection machine learning algorithm was implemented to develop compaction efficiency metrics that includes roller pass counts and roller speed. Various construction sites were visited in 2023 and 2024 to develop models and framework. The main goal of the second phase of the study is to extend capabilities of the models and algorithms of the aerial pavement quality monitoring protocol and improve the readiness level for implementation in real-life scenarios. The focus of the proposed work in the second phase is to develop the probabilistic density models and extend the limits of the surveyed mat. The ultimate vision is to commercialize the proposed technology as a contractor tool that can provide near real-time quality data from the ongoing construction with actionable feedback. When such data is made available to the paving crew, likelihood of achieving uniform and target in-place densities will increase and overall pavement quality will be improved. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:58:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691499</guid>
    </item>
    <item>
      <title>Conventional and Ultra-High Performance Concretes for Transportation Infrastructure using Limestone-Calcined Clay Cements (LC3)</title>
      <link>https://rip.trb.org/View/2692308</link>
      <description><![CDATA[This project evaluates the use of limestone calcined clay cement (LC3), a new type of hybrid blended cement, with potential energy, environmental, and economic benefits in addition to performance enhancement, in transportation infrastructure applications (highway concrete and bridge decks/connections). Specifically, the use of LC3 with 35-50% lower clinker content, is explored as a drop-in replacement for conventional Type I/II ordinary portland cement (OPC) or Portland limestone cement (PLC, Type IL) for conventional (compressive strengths in the 4000-8000 psi range) and ultra-high-performance concretes (UHPC; compressive strengths of ~25,000 psi).  LC3 uses calcined clays (even low purity clays, which are abundant in many parts of the world), which react pozzolanically in a cementitious system to form secondary hydration products, thereby resulting in cement replacement. In this project, the influence of calcined clays (synthesized by Ash Grove Cement, and used in the formulation of their LC3 cements) will be evaluated with respect to reactivity in cement-based systems, and the development of hydration products and pore structure. Conventional concretes and UHPCs will be proportioned using LC3 cements. The influence of 35-50% clinker replacement on the mechanical and durability properties of conventional concretes and UHPCs will be evaluated. A few concrete mixtures will be chosen to be recommended to transportation agencies based on performance and a detailed life cycle cost analysis will be carried out. It is anticipated that this work will lead to a better understanding of economical and high-performance low clinker content concretes, which are critical towards improving the durability and extending the life of transportation infrastructure.  ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:49:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692308</guid>
    </item>
    <item>
      <title>Development of A Testbed for Automated Emergency Braking (AEB)
Technology for Buses</title>
      <link>https://rip.trb.org/View/2692309</link>
      <description><![CDATA[The U.S. Department of Transportation’s proposed mandate for Automated Emergency Braking (AEB) systems on heavy vehicles highlights a critical need for research to support safe and effective implementation—particularly for transit buses operating in dense urban environments. While AEB technologies are increasingly available, there is limited research infrastructure to support the evaluation of their real-world performance, especially under the complex and dynamic conditions faced by buses. 

This project proposes the design and planning of an AEB testbed at Rutgers University to fill this gap. The testbed will simulate key operational scenarios such as pedestrian crossings, mixed-traffic conditions, and varying road and weather environments. The platform will support both OEM-installed and retrofitted AEB systems, enabling systematic evaluation of braking performance, sensor reliability, and system limitations. This research provides an important step toward the development of critical infrastructure to support the research and implementation of AEB technology for improved transit safety.   ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:41:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692309</guid>
    </item>
    <item>
      <title>SHM-based Bridge Element Deterioration Modeling and Prediction</title>
      <link>https://rip.trb.org/View/2692311</link>
      <description><![CDATA[The transportation legislation MAP-21 and the FAST Act have advanced the integration of performance-based asset management into transportation decision-making across federal, state, and local levels. Through Transportation Asset Management (TAM), agencies are establishing systematic processes to operate, maintain, and preserve infrastructure assets throughout their lifecycle. The objective of the project described herein is to support these goals by developing a methodology for predicting bridge element deterioration using long-term Structural Health Monitoring (SHM) data. Current approaches rely on periodic inspections and NDT/NDE methods, which miss real-time changes and pose safety risks. By combining SHM data with traditional models (Markov chains, Weibull distributions) and advanced algorithms, this research aims to produce accurate, time-based deterioration curves for bridge elements. Using high-resolution data from the BEAST lab at Rutgers University, the models will be calibrated and validated to reflect real-world deterioration mechanisms. This work supports NCIT’s mission to improve durability and extend the life of transportation infrastructure. It directly addresses the topical pillars of Infrastructure Durability, Resiliency, and Technology, offering tools for predictive maintenance, life-cycle cost management, and improved safety. The outcome will aid bridge owners in making data-driven decisions that enhance long-term performance and resilience. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:40:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692311</guid>
    </item>
    <item>
      <title>Infrastructure Asset Management Academy (IAMA) for Engineers and Planners</title>
      <link>https://rip.trb.org/View/2692314</link>
      <description><![CDATA[Spanning millions of miles, the United States is home to a vast public roadway network that is relied on every day to connect a growing population with jobs, communities, essential services, and goods. Over time this critical transportation network has aged, and now a significant portion of U.S. roads are in poor or mediocre condition. Previous federal legislation has recognized the need for investment in American roads and bridges, including mandating that state agencies develop and implement Transportation Asset Management Plans. During the last decade, significant progress has been made. However, the complexity and scale of managing modern transportation networks require specialized knowledge that expands into disciplines beyond traditional engineering. Today’s asset owners must also manage and analyze large amounts of data, consider lifecycle and economic costs, develop risk management programs, and strategically deploy maintenance activities and capital investments to ensure long-term performance. The goal of this project is to train today’s asset managers in these key areas and provide them with the knowledge and tools needed to make informed decisions that can extend the service life, improve durability, and minimize the risk to their assets. CAIT’s Infrastructure Asset Management Academy is a 4-course program that brings expert speakers together to enable state DOTs and their staff to utilize emerging asset management technologies; better collect, manage, and integrate large amounts of data; break down organizational silos; and ultimately implement a risk-based, data-driven asset management program that can extend the service life of their infrastructure.   ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:40:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692314</guid>
    </item>
    <item>
      <title>Smart Transportation Workshop for Workforce Development Empowering AI
and IoT Technologies</title>
      <link>https://rip.trb.org/View/2696931</link>
      <description><![CDATA[This project enhances the integration of emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) into the modern transportation sector and the education sector. It represents a fusion of research, education, and workforce development, focusing on how advanced technologies can address real world challenges in transportation systems. From the research side, this project will guide students to conduct research on applying Artificial Intelligence (AI) to solve practical transportation problems. Such as developing lightweight predictive real-time AI framework for smart city developing that utilizes low resolution IoT sensor data from drones and self-driving cars. This framework can be applied to construction and maintenance planning, infrastructure health monitoring, and traffic pattern optimization. Another research topic focus is enhancing AI robustness with low quality and uncertain IoT sensor data. This part of the research will develop methods to improve the resilience of AI frameworks against unreliable or noisy sensor inputs and will improve model robustness in autonomous systems (such as self-driving cars, AI-powered monitor systems) under adverse conditions such as heavy rain or when sensors are faulty or inaccurate. From the education and workforce development (EWD) side, this project will also hold annual workshops and seminars that provide hands on training and experiences for students to apply emerging technology in the transportation sector. It will also develop new interdisciplinary education models that integrate computer science and civil engineering with emerging technologies and transportation applications, addressing a gap that currently exists in undergraduate education. Overall, the project will produce state of the art research contributions in AI and IoT enabled transportation. It will provide practical training through workshops, seminars, and hands on projects. It will
Smart Transportation Workshop for Workforce Development Empowering AI
and IoT Technologies create interdisciplinary education models that bridge emerging technologies and transportation engineering. It will strengthen the workforce pipeline prepared to apply AI and IoT in transportation innovation.]]></description>
      <pubDate>Thu, 10 Sep 2026 08:33:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696931</guid>
    </item>
    <item>
      <title>Developing a Multi-Resolution Remote Monitoring System for Bridge Integrity
Assessment (BIA) Using IoT and AI Technologies</title>
      <link>https://rip.trb.org/View/2696932</link>
      <description><![CDATA[Bridges are an integral part of U.S. transportation networks, improving highway connectivity and accessibility. For instance, there are over 55,000 bridges in Texas, with an average age of 43 years. The maintenance of this bridge network costs the TxDOT more than $300 million annually. The traditional Bridge Integrity Assessment (BIA) monitoring methods include periodic manual inspections to observe visible cracks, wearing surface, drainage features, signs of scouring or erosion at the piers or abutment, and overall structural integrity, and other sensors to monitor stress, vibrations, and movements continuously. With the advancement of AI and IoT, it is more possible than ever to develop an innovative BIA system that can address the challenges of traditional BIA methods and support bridge structure safety and integrity. The proposed research has three objectives: (1) Design an AI-enabled secure remote monitoring architecture for supporting BIA and maintenance. (2) Build an end-to-end simulation system to evaluate and validate the developed architecture to detect threats that pose a risk to bridge structure safety. (3) Implement a prototype of the proposed multi-resolution remote monitoring system using various remote sensing technologies and conduct real-world field experiments using the developed system (i.e., UAV and IoT sensors). Additionally, the Blinn College District (BCD) will organize an educational workshop for Prairie View A&M University (PVAMU) and BCD students, during which unmanned aerial vehicle (UAV) technologies will be demonstrated. The project outcomes will assist the DOTs (Department of Transportation) in monitoring the bridge infrastructure condition and conducting preventive maintenance more efficiently. This project is directly related to NCIT’s focus area of “Improving the Durability and Extending the Life of Transportation Infrastructure” and the NCIT’s topical pillar, “Technology.”]]></description>
      <pubDate>Thu, 10 Sep 2026 08:32:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696932</guid>
    </item>
    <item>
      <title>An Integrated AI-Powered Approach for Road Infrastructure Health
Monitoring Using UAV and ERI Technologies</title>
      <link>https://rip.trb.org/View/2696933</link>
      <description><![CDATA[The stability of transportation infrastructure, particularly slopes adjacent to roadways, is critical for ensuring safety and longevity. However, traditional monitoring methods (e.g., inclinometers, extensometers, piezometers, sensors, etc.) often fail to provide comprehensive subsurface insights for early slope failure detection. While significant advancements have been achieved by unmanned aerial vehicle (UAV)-based imaging techniques, such as red, green, blue and light detection and ranging, for surface monitoring, several challenges and research gaps remain, such as deeper subsurface data acquisition and automation in surface and subsurface data fusion. This research proposes a data fusion framework integrating UAV-based sensing of slope surface and Electrical Resistivity Imaging (ERI)-based sub-surface characterization to enhance slope stability assessments. Integrating advanced data fusion methodologies using Machine Learning (ML) and Artificial Intelligence (AI), this research aims to develop an infrastructure health monitoring (IHM) framework. This research directly contributes to transportation infrastructure's durability and extended service life by providing a holistic view, advancing early‐warning capabilities, and optimizing maintenance strategies. The integrated monitoring system developed from this study will enable preventative interventions and deliver the precise information required for maintenance. In addition, advanced data fusion using ML and AI enhances infrastructure management technology. By integrating UAV and Electrical Resistivity Imaging (ERI) technologies, this study addresses the limitations of conventional IHM and offers a novel, scalable solution for transportation agencies. The developed framework will serve as a model for data-driven, technology-enhanced geohazard assessments, ensuring safer and sustainable transportation networks.]]></description>
      <pubDate>Thu, 10 Sep 2026 08:32:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696933</guid>
    </item>
    <item>
      <title>AI-driven Tax Increment Financing: Revolutionizing Transportation
Infrastructure Funding</title>
      <link>https://rip.trb.org/View/2696939</link>
      <description><![CDATA[Local governments across the U.S. are facing significant challenges in funding critical transportation infrastructure improvements. While federal funding opportunities exist, communities often struggle with providing required matching funds for transportation projects. This requirement heightened the need for creative financing strategies, particularly value capture tools like Tax Increment Financing (TIF). TIF is a geographically targeted economic development tool that captures a portion of the increased property tax revenue generated by development spurred by public investments in infrastructure or other public assets and reallocates it to help finance those investments. This project introduces an innovative machine learning approach that combines advanced time series analysis with multi-factor economic modeling to generate 30-year property tax revenue forecasts. It will employ a hybrid model that integrates multiple data streams including historical land value trends, macroeconomic indicators, and planned development data. Building on TTI’s decade of expertise in TIF and value capture methodologies, this data-driven advanced AI model will enable local governments to make more informed decisions about infrastructure investments and expand the application of value capture strategies. The research aligns both Policy and Technology pillars of NCIT, leveraging artificial intelligence to enhance financial resource allocation decisions. By providing more reliable revenue predictions, this research will help reduce financial risks in infrastructure investments while promoting the broader adoption of value capture strategies. The project’s goal is to catalyze transportation infrastructure development, fostering improved mobility and economic growth across communities nationwide.]]></description>
      <pubDate>Thu, 10 Sep 2026 08:17:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696939</guid>
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