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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Evaluating Construction Workforce Conditions and Their Effects on Productivity and Project Scheduling</title>
      <link>https://rip.trb.org/View/2712203</link>
      <description><![CDATA[Transportation construction projects often involve accelerated schedules, extended work hours, and work performed in challenging environmental and safety-sensitive conditions. These conditions can affect the physical and mental well-being of state department of transportation (DOT) and contractor staff and may negatively influence workforce productivity, safety, and project delivery outcomes.

Long work hours, demanding schedules, and changing environmental conditions have contributed to growing concerns regarding workforce stress, burnout, fatigue, and mental health challenges within the construction industry. Research and industry surveys have highlighted the need to better understand how workforce conditions influence productivity and project performance. However, there is limited guidance on incorporating workforce well-being considerations into project scheduling, phasing, and construction management practices.

The objective of this research is to develop a guide to assist state DOTs in evaluating environmental, physical, and mental conditions affecting the transportation construction workforce and their impact on productivity and scheduling expectations. The research is intended to support healthier, safer, and more sustainable project delivery practices.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:14:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712203</guid>
    </item>
    <item>
      <title>Innovative Approaches to Maintenance Funding for Active Transportation Infrastructure on State Highways Research</title>
      <link>https://rip.trb.org/View/2712169</link>
      <description><![CDATA[Currently, no substantial detailed advice or research exists in the leading national work on active transportation maintenance or active transportation policy on innovative maintenance funding strategies. More robust resources do exist for capital expenditures and new projects. Current funding maintenance recommendations generally do not go beyond mentioning that consistent funding is good and having a plan is good. Good public policy requires more detailed thought and specific guidance, which are especially critical during the planning and scoping phases to build maintainability in from project initiation.

Research is needed to address this gap and build from recently completed maintenance research. A comprehensive list, an analysis of funding sources and their constraints, and a cost-to-benefits comparison will provide the necessary groundwork for an informed assessment of innovative research strategies and enable good case studies to be identified. Focusing on ongoing maintenance needs, guidance for planning and scoping phases, and guidance on how active transportation maintenance fits into the larger transportation context would help ensure that these strategies work for current maintenance teams and result in better new projects coming into maintenance obligations.

The objective of this research is to identify innovative funding strategies for active transportation facility maintenance on state highways. Appropriately maintained facilities provide road safety, economic development, and land value benefits that could be factored into strategies. The research will address how these strategies align with ongoing maintenance needs for these facilities and provide guidance on maintenance in the planning and scoping of projects.]]></description>
      <pubDate>Tue, 09 Jun 2026 12:38:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712169</guid>
    </item>
    <item>
      <title>Nebraska Risk-Informed Construction Scheduling and Impact Analysis</title>
      <link>https://rip.trb.org/View/2689393</link>
      <description><![CDATA[Assigning a reasonable contract time is central to the Nebraska Department of Transportation (NDOT)’s project delivery process. The number of working days directly affects bid prices, contractor time-related overhead, public traffic impacts, and NDOT’s construction-engineering workload. However, many NDOT projects experience time extensions or schedule adjustments. These delays often arise from weather windows, utility coordination challenges, labor or material availability issues, and unforeseen field conditions. The presence of these uncertainties means that a single deterministic duration for each activity does not adequately represent the true likelihood of early or late project completion. NDOT currently relies on deterministic schedules and historical judgment when assigning contract time. These methods assume fixed activity durations and do not fully capture the uncertainties caused by weather, utilities, material supply, labor availability, or construction sequencing constraints. As a result, some projects may receive either more contract days than needed or face unexpected time extensions that increase cost and user delay. Recent research and best practices from other state DOTs and the Federal Highway Administration (FHWA) emphasize the need for probability-based scheduling that uses production-rate data, activity dependencies, and delay risk to estimate a realistic range of completion dates. The overarching goal of this project is to develop a data-driven, probability-based scheduling tool that enables NDOT to determine reasonable contract time and proactively assess construction delay risks throughout the project lifecycle.]]></description>
      <pubDate>Tue, 02 Jun 2026 12:25:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689393</guid>
    </item>
    <item>
      <title>Maintenance Decision Support System Refinement</title>
      <link>https://rip.trb.org/View/2705945</link>
      <description><![CDATA[The objectives of the Maintenance Decision Support System are to: (1) Assess current road and weather conditions using observations and reasonable inferences based upon observations and physical laws. (2) Provide time- and location-specific weather forecasts along transportation routes. (3) Predict how road conditions would change due to the combined effects of the forecast weather and the application of several candidate road maintenance treatments. (4) Notify state agencies of approaching adverse conditions and suggest optimal maintenance treatments that can be achieved with resources available to the transportation agencies. (5) Evaluate the reliability of predictions and the effectiveness of applied maintenance treatments for specific road and weather conditions so that the decision support logic can be improved. Continuing the efforts of the previous phases of work, the member agencies voted on the future direction and tasks of the Maintenance Decision Support System (MDSS) pooled fund study (PFS). Some of these tasks represent continuation of previous phases of work and others are new endeavors for the project. The primary research areas selected by members of the MDSS project panel include: (1) Investigate methods to improve the MDSS model for better support of frost, freezing rain and other weather conditions; (2) Assess recommendations based on user feedback in real-time with post-recommendation analysis to improve MDSS modeling; (3) Analyze the use of Level of Service in DOT operations and understand how this functionality can be improved within MDSS; (4) Focus on Liquids as a priority treatment recommendation and develop processes that facilitate specialty liquids within MDSS; (5) Conduct a discovery process on Performance Measurement methods that would be applicable for MDSS with the goal of implementing these methods to demonstrate MDSS value; and (6) Improve the Route Configuration Process by implementing automated functionality, clear guidance for users, and preparing for the future of MDSS.]]></description>
      <pubDate>Thu, 21 May 2026 22:52:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2705945</guid>
    </item>
    <item>
      <title>Assessment of Litter Hot Spot Areas for Targeted Reduction in Prince George's County</title>
      <link>https://rip.trb.org/View/2701237</link>
      <description><![CDATA[The frequency and volume of litter and illegal dumping on state and county roadways in Prince George’s County are increasing, despite efforts like scheduled litter blitzes, which have shown limited long-term success. Over the past five years, the Maryland Department of Transportation State Highway Administration (MDOT SHA) spent approximately $42 million removing litter and debris, with last year’s costs alone reaching $15 million—the equivalent of 45 new dump trucks or nearly 60 miles of resurfaced roads (Source WBAL News: https://www.msn.com/en-us/news/us/drivers-watch-out-for-operation-clean-sweep-maryland/ar-BB1jY1rr). These expenditures are unsustainable, especially given recent fiscal shortfalls. This joint research proposal, submitted by District 3 and Prince George’s County Department of Public Works and Transportation (DPW&T), aims to evaluate litter hot spots at the census tract level, as current efforts have not addressed the root causes of the issue. Prince George’s County, a well-resourced and educated area, presents unique challenges, suggesting the problem extends beyond awareness or resource deficits. ]]></description>
      <pubDate>Wed, 13 May 2026 09:15:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701237</guid>
    </item>
    <item>
      <title>Evaluate the Safety Effects of Multiple Vehicle Synchronized Warning Lights in ODOT Work Zones
</title>
      <link>https://rip.trb.org/View/2701274</link>
      <description><![CDATA[In 2024, 56 Ohio Department of Transportation (ODOT) crews were struck while working on the highway system. As of March 2025, 43 ODOT crews have been struck. With safety being of the upmost importance to ODOT's Executive Leadership, protecting road crews and individuals working on ODOT jobsites remains a common theme when investigating new technologies and techniques to help reduce and minimize these accidents. Currently ODOT has a variety of light-emitting diode (LED) warning light systems in use on its fleet of maintenance vehicles. When these vehicles are concentrated in a work zone, there has been concern that these lights, while flashing independently, can lead to confusion among the motoring public as they enter the work zone. Added to this, ODOT operates work zones during all times of the day and in all weather conditions further exacerbates the situation.  This can result in unsafe driving practices and increased accidents. 

There is a growing opinion among transportation professionals that synchronizing warning lights and/or customizing patterns to evolve situationally could alleviate, if not resolve, these dangerous work zone crashes. ODOT is looking to evaluate the effectiveness of a system that synchronizes the warning systems of all vehicles present in a work zone.   A system that could increase driver awareness and reduce safety related incidents would be useful not only to ODOT but to local public agencies, emergency responders, and other state departments of transportation (DOTs).

OBJECTIVES: The goal of this research is to identify the effectiveness of using synchronized warning light systems versus non-synchronized warning light systems on work zone vehicles.
             ]]></description>
      <pubDate>Tue, 12 May 2026 10:43:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701274</guid>
    </item>
    <item>
      <title>Advancing Rail Infrastructure Asset Management and Hazard Mitigation: Educational Tools and Practitioner Decision Support Systems</title>
      <link>https://rip.trb.org/View/2691664</link>
      <description><![CDATA[As rail infrastructure ages and faces intensifying system stressors (e.g., flooding, icing, and extreme heat), agencies need to identify pathways to enhance the durability and operational reliability of their physical assets. However, there is a significant gap in available training material regarding Rail Infrastructure Asset Management (IAM) for both university students and current practitioners. Building upon the researcher’s ongoing research into adaptive capacity and international rail best practices, this project will translate rigorous research findings into accessible educational and research tools and practical decision-support systems. The project focuses on three primary technology transfer and workforce development initiatives:


(1) Interactive Rail Asset Management Platform: The team will develop a web-based, interactive learning module (utilizing platforms such as Tigyog) targeting students and practitioners. This resource will cover the principles of IAM, condition assessment, and decision-making under uncertainty. It will feature "gamified" scenarios and narrative case studies drawn from the team's research, contrasting infrastructure failures (e.g., the East Palestine, Ohio derailment) with successful engineering adaptations (e.g., the Shinkansen automatic braking systems in Japan). Users will engage with a "build-your-own" asset management framework to apply these concepts in real-time.
(2) University Teaching Packets: To address the lack of specialized rail engineering curricula, the team will create comprehensive teaching modules for instructors. These packets will draw from the team's six-country comparative analysis (U.S., Australia, Spain, Japan, Ghana, Argentina), providing lecture slides, assignment materials, and case-study evaluations. Topics will focus on identifying key asset vulnerabilities, institutional barriers to maintenance, and successful infrastructure hardening strategies.
(3) Practitioner Decision Matrix: The team will develop a "Rail Hazard Mitigation Decision Matrix" for state agencies and rail operators.

This tool will synthesize data on geographic hazards, system ownership models, and cost-benefit ratios to help managers prioritize physical infrastructure improvements.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:25:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691664</guid>
    </item>
    <item>
      <title>Successful Strategies in Providing Training Programs for State and Local Equipment Technicians</title>
      <link>https://rip.trb.org/View/2681233</link>
      <description><![CDATA[Fleet managers across surface transportation agencies face ongoing challenges in identifying training needs for fleet maintenance technicians and delivering effective programs. Prior to 2020, many states relied heavily on vendor-led training to support technician development on new and existing equipment. Following the COVID-19 pandemic, a number of vendors reduced or discontinued these offerings, requiring agencies to pursue alternative approaches.

As fleet equipment continues to incorporate more advanced technologies, the need for consistent, high-quality, and up-to-date technical training has become increasingly critical to maintaining safe, reliable, and cost-effective operations.

OBJECTIVE: This scan will examine organizations that have successfully identified and implemented sustainable training programs for fleet maintenance technicians. The team will document how agencies structure and manage their programs, measure effectiveness, and ensure appropriate leadership support.]]></description>
      <pubDate>Tue, 17 Mar 2026 15:03:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681233</guid>
    </item>
    <item>
      <title>Road Network Restoration after Major Disruptions</title>
      <link>https://rip.trb.org/View/2447123</link>
      <description><![CDATA[This project develops practical optimization methods for selecting, sequencing, and scheduling restoration actions for disrupted road networks based on incomplete and gradually improving information. Road networks may be severely damaged by events such as hurricanes and earthquakes, and prompt restoration is often necessary for the resumption of emergency services, other essential services, and normal activities.

The proposed methods employ artificial intelligence heuristics such as genetic algorithms and particle swarm algorithms to optimize the schedules of restoration tasks. A hybrid optimization approach combines fast traffic assignment with microscopic simulation to refine solutions. The methods are designed to start with incomplete, uncertain information and adapt dynamically as additional data becomes available from weather forecasts, work crews, and the public. The project also develops methods for pre-planning purposes, including preparing effective restoration plans based on estimated probabilities of disruptions and their consequences.

The research team will collaborate with the Maryland State Highway Administration and other agencies to ensure the practical applicability of the methods. Technology transfer activities include journal papers, conference presentations, software with a user manual, a final technical report, and workshops for interested transportation organizations.]]></description>
      <pubDate>Wed, 11 Mar 2026 13:21:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447123</guid>
    </item>
    <item>
      <title>National Road Research Alliance (Phase-3)</title>
      <link>https://rip.trb.org/View/2678150</link>
      <description><![CDATA[This solicitation is for the continuation of the National Road Research Alliance (NRRA) for another 5 years and to continue to support Veda development to increase efficiency and effectiveness of both efforts. The NRRA exists to strategically implement cooperative pavement research. State agencies, industry, academia, consultants and associations work together to identify problems, complete research projects and implement results. The goal is to help agencies nationwide achieve consistent benefits from real world road research. It also seeks to provide members a forum to discuss issues and an outdoor, real-world laboratory (MnROAD) for evaluating cutting-edge pavement technologies.  The NRRA consists of five project teams: Flexible, Rigid, Geotechnical, Intelligent Construction Technologies, and Preventive Maintenance and is governed by an Executive Committee made up of two representatives from each government agency participating in the study.   Each team activities include prioritization of short and long-term research, development of long-term research test sections at MnROAD and providing input for technology transfer.  


]]></description>
      <pubDate>Fri, 06 Mar 2026 13:10:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2678150</guid>
    </item>
    <item>
      <title>Modernization and Web-Based Implementation of the Illinois Pavement Feedback System</title>
      <link>https://rip.trb.org/View/2677555</link>
      <description><![CDATA[This project will modernize the Illinois Department of Transportation’s (IDOT's) Illinois Pavement Feedback System, a pavement management system that contains detailed construction history, performance data and traffic data of the Illinois interstate system. Researchers will transition the database from a mainframe-based system into a secure, web-based pavement data management and analysis platform. Transitioning to a web-based platform will provide IDOT with an easy way to access the data, monitor interstate sections, and make informed maintenance and rehabilitation decisions. The system will also have a detailed dataset on Illinois’ interstate system available to researchers.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:22:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677555</guid>
    </item>
    <item>
      <title>Inventing an AI-Informed Risk Index to Prioritize Transportation Infrastructure Preservation</title>
      <link>https://rip.trb.org/View/2672766</link>
      <description><![CDATA[The proposed project seeks to transform national transportation asset datasets into actionable intelligence for preservation planning. Recognizing the fragmentation between roadway and bridge performance data within the Highway Performance Monitoring System (HPMS) and National Bridge Inventory (NBI), the project introduces an artificial intelligence (AI)-driven framework to systematically connect these datasets and develop a unified risk index. The study will first conduct comprehensive literature and data reviews to identify gaps in cross-asset analysis and assess data quality through spatial joins and validation of key attributes such as average daily traffic (ADT). Using descriptive, prescriptive, and predictive analytics, the research will examine relationships among international roughness index (IRI), bridge condition ratings, and traffic loading to uncover deterioration trends and key predictive features. The research will apply advanced machine learning models to forecast performance and support prioritization under budget constraints. The resulting risk index will provide transportation agencies with an objective method to rank preservation needs. This result will enhance Transportation Asset Management Plans (TAMPs) and ensure data-driven resource allocation. Expected outcomes include a validated analytic framework, cross-asset integration methods, predictive deterioration models, and interactive visualization dashboards to aid decision-making. The project directly supports USDOT's strategic goals of economic strength and global competitiveness by improving asset reliability, minimizing disruptions to freight and passenger mobility, and extending infrastructure service life. Educationally, it will train at least one doctoral student in advanced analytics and risk-based asset management. The research will also integrate the methods and results into graduate coursework and research. Technology transfer activities will disseminate results through academic publications, conference presentations, outreach products, and online tools. Stakeholder engagement will ensure practical adoption. Overall, this project aims to deliver a replicable, scalable decision-support tool to strengthen national transportation resilience and investment efficiency.]]></description>
      <pubDate>Sun, 22 Feb 2026 10:44:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2672766</guid>
    </item>
    <item>
      <title>Coastal pavement maintenance and rehabilitation decision making based on both surface and subsurface conditions</title>
      <link>https://rip.trb.org/View/2662938</link>
      <description><![CDATA[Texas has approximately 3,359 miles of coastline spanning five geographically distinct districts. Pavements in these regions are exposed to highly variable subgrade soils, diverse traffic loading levels, and unique climatic challenges, including hurricanes, storm surges, and recurrent flooding. Effective decision-making for pavement Maintenance and Rehabilitation (M&R) is therefore critical to ensuring resilient infrastructure, optimizing project selection, and allocating limited resources efficiently. Current M&R selection practices primarily rely on surface-level indicators—such as distress manifestations (cracking, rutting, etc.) and ride quality. While these measures are useful, they fail to provide a comprehensive understanding of the pavement’s structural health. To address this limitation, this study will propose an integrated framework that combines both surface and subsurface information for M&R decision-making. In particular, subsurface conditions derived from non-destructive testing will be emphasized as a means to bridge the existing knowledge gap, enabling a more holistic and data-driven approach to pavement management.]]></description>
      <pubDate>Thu, 29 Jan 2026 15:57:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2662938</guid>
    </item>
    <item>
      <title>Development of Data Driven Digital Twin for Enhancing Pavement Performance Prediction in South-Central United States</title>
      <link>https://rip.trb.org/View/2658057</link>
      <description><![CDATA[A comprehensive survey conducted by National Cooperative Highway Research Program (NCHRP) Synthesis 501 revealed that many state departments of transportation (DOTs) update their pavement performance models only every 2 to 5 years, with some agencies updating even less frequently. Such lengthy update cycles mean that the models often fail to reflect recent trends in traffic loading and material performance, leading to outdated forecasts that diminish the accuracy and usefulness of maintenance and rehabilitation planning. The primary objective of this project is to develop a data-driven Digital Twin (DT) framework based on pavement management system data that will regularly update Artificial Intelligence (AI)-based performance models for pavements in Louisiana. This framework aims to help state agencies make smarter, more accurate maintenance decisions while reducing costs over time. The proposed Digital Twin platform will focus on the interstate network in Louisiana, given its importance to the state and its wide implications on mobility and freight movement. The work will be divided into five tasks: (1) collect and preprocess pavement management system data for the interstate network, (2) development of digital twin framework, (3) forecast future pavement conditions in digital twin platform, (4) suggest potential maintenance strategies in the digital twin platform, and (5) prepare final report. The project will address the growing need for innovative approaches that can dynamically integrate diverse datasets, learn from both historical and emerging patterns, and provide transportation agencies with actionable, real-time insights. Digital twin technology offers this dynamic capability by enabling a shift from reactive maintenance toward predictive and proactive strategies.  ]]></description>
      <pubDate>Fri, 23 Jan 2026 13:50:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658057</guid>
    </item>
    <item>
      <title>An AI-Based Reasoning Framework for Proactive Infrastructure Monitoring and Preservation Using Connected Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2655750</link>
      <description><![CDATA[This research proposes the development of a Connected Autonomous Vehicles (CAV)-based Proactive Infrastructure Preserving (CAV-PIP) system to enhance the safety, resilience, and operational efficiency of transportation infrastructure. The system leverages the sensing and communication capabilities of CAVs to enable continuous, real-time detection and reporting of roadway anomalies, such as pavement distress and damaged traffic signage. By fusing multi-modal sensor data and incorporating a retrieval-augmented generation (RAG) framework with large language models (LLMs), the system constructs a dynamic prior knowledge base to reason about infrastructure conditions and recommend context-aware maintenance actions. The project aims to transform current reactive maintenance practices into a data-driven, proactive framework that improves decision-making for transportation agencies. The system will be validated through simulation in the CARLA (Car Learning to Act) environment and supported by curated real-world datasets. Expected outcomes include an integrated detection and reasoning framework, structured maintenance reporting tools, and publicly shareable datasets and software packages. The project's broader impact lies in advancing intelligent infrastructure monitoring technologies, reducing long-term maintenance costs, and contributing to safer and more sustainable transportation systems.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:01:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655750</guid>
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