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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>Intelligent Speed Assistance Guide for State Highway Safety Offices</title>
      <link>https://rip.trb.org/View/2720300</link>
      <description><![CDATA[Speeding remains one of the most persistent and deadly threats on U.S. roadways, accounting for more than 11,000 deaths in 2024, and 125,000 fatalities over the last decade. One promising countermeasure to help address speeding behavior is Intelligent Speed Assistance (ISA) technology, which uses real-time Global Positioning System (GPS) data to detect the speed limit and proactively alert (or limit) the driver if they are speeding, can reduce speeding and help promote long-term safe driving behaviors.
The Governors Highway Safety Association (GHSA) recently documented a growing number of examples of ISA’s effectiveness at the local level. In New York City, a pilot program involving 500 fleet vehicles saw a 64% reduction in speeds substantially above speed limits. A District of Columbia school bus pilot logged 10,000 miles with zero speeding events.
European research has shown that ISA can reduce crash risk and lessen the severity of injuries, particularly in areas with changing speed limits or heavy pedestrian activity. A 2019 policy report from the European Transport Safety Council estimated that ISA could cut road deaths across Europe by approximately 20%. Another study projected that equipping all vehicles with mandatory active ISA could reduce injury and fatal crashes by 20% and 37%, respectively.
Given the potential for wide adoption of ISA to substantially reduce speeding-related fatalities and serious injuries, research is needed to identify ways for state highway safety offices (SHSOs) to advance the use of this technology.

OBJECTIVE: The objective of this research is to develop a guide that supports efforts by SHSOs to: 1) Conduct comprehensive stakeholder assessment to identify which groups have the greatest need for education and which hold the most influence over its adoption. 2) Develop a core set of educational active ISA materials or leveraging materials available from other sources. 3) Ensure that SHSO staff have a strong foundational understanding of active ISA. Staff training should cover how active ISA works, its effectiveness as demonstrated in peer-reviewed research, relevant policy considerations and communication strategies tailored to different audiences. 4) Establish clear metrics and evaluation processes allows SHSOs to measure the effectiveness of educational initiatives and outreach efforts. 5) Engage with key stakeholders to advance pilot programs. 6.) Develop guidelines and implementation frameworks for pilot projects that help SHSOs evaluate and demonstrate effective strategies for modifying speeding behavior through ISA technologies, including  recommendations on target driver populations, stakeholder coordination, public communication, data collection, performance measures, privacy considerations, and evaluation methodologies to support broad adoption.
]]></description>
      <pubDate>Thu, 02 Jul 2026 20:16:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2720300</guid>
    </item>
    <item>
      <title>Developing a Standardized Framework for Real-Time Freight-Specific Traveler Information and Route Restrictions for Commercial Motor Vehicle Operators; Truck Parking Data Exchange Standards</title>
      <link>https://rip.trb.org/View/2709247</link>
      <description><![CDATA[Commercial motor vehicle (CMV) operations increasingly rely on maps and navigation systems that were not designed to address the unique needs of freight operations. This mismatch contributes to increased safety risks, including unplanned diversions, bridge strikes, congestion in freight corridors, lane geometry constraints, and other routing errors.

Today, the lack of a standard, consistent data structure or framework for sharing real-time freight-specific information remains a foundational challenge for public agencies and for the economy that depends heavily on the national roadway network. Public agencies currently lack a widely accepted standard or shared framework for communicating restrictions, alerts, and disruptions to CMV operators. Existing standards such as the Traffic Management Data Dictionary (TMDD) and SAE J2354 (Advanced Traveler Information Systems) support general traveler messaging but do not include freight-specific data elements.

In addition, the growing need for timely and reliable truck parking information, coupled with the rapid expansion of truck parking information systems, demonstrates the need for standardized methods to collect and disseminate truck parking data. As technologies used in these systems become increasingly ubiquitous, and as industry expectations and preferences continue to evolve, standardization of both information and dissemination tools becomes a critical next step.

OBJECTIVES: The objectives of this research are: (1) to develop a unified data framework for delivering time-sensitive, relevant, and actionable freight-specific traveler information messaging to CMV operators; and (2) to develop proposed data standards for real-time, public and private truck parking availability and attributes (including the number of spaces, size, hours of availability, and available amenities).

]]></description>
      <pubDate>Tue, 02 Jun 2026 14:33:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709247</guid>
    </item>
    <item>
      <title>Developing a Nebraska-Specific Evaluation Framework for Superheavy Load Movements on Pavements</title>
      <link>https://rip.trb.org/View/2689391</link>
      <description><![CDATA[Nebraska continues to receive superload permit requests whose non-standard axle/tire layouts, slow-roll operations, and edge-proximity produce subsurface stress patterns that are not consistently captured by current screening practices. Although the federal framework and SuperPACK provide a sound mechanistic basis, they have not yet been exercised in a Nebraska-focused parametric fashion to reveal which combinations of pavement structure, material properties, axle weights, and spacings govern ultimate capacity and service-limit responses on state routes. Without that evidence, route approvals remain slower and less consistent, and operating conditions or mitigations are difficult to specify with confidence. This study will provide the Nebraska Department of Transportation (NDOT) with a focused, evidence-based screen for superload movements on Nebraska highways. By exercising SuperPACK in a structured parametric study and benchmarking marginal cases with an advanced finite element (FE) tool, the work will yield preliminary acceptance bounds that can be applied directly to permit reviews. The project will also clarify when a simple mechanics-based screen is sufficient and when an advanced check is warranted, improving the efficiency of reviewer time and aligning engineering findings with transparent cost-recovery logic. Because the analysis uses Nebraska-representative sections, inputs, and axle/tire nuclei taken from real permits, the results will be credible to internal reviewers and external stakeholders and will establish a practical pathway to extend the approach to rigid and composite pavements in subsequent phases.]]></description>
      <pubDate>Tue, 02 Jun 2026 12:24:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689391</guid>
    </item>
    <item>
      <title>New Models and Solutions to Vehicle Routing with Cardinality and Distance Constraints</title>
      <link>https://rip.trb.org/View/2703788</link>
      <description><![CDATA[Many emerging transportation and logistics operations are constrained by both the maximum distance a vehicle can travel and the number of customers it can serve before requiring replenishment, recharging, or maintenance. These operational realities motivate the need for new routing optimization models that explicitly integrate distance and cardinality constraints. This project proposes the first comprehensive study of a novel Black-and-White Vehicle Routing Problem (BWVRP), where customer nodes and replenishment nodes are jointly routed across a fleet of vehicles, with replenishment nodes allowed to be visited multiple times. The project will develop new mixed-integer linear programming models and exact branch-and-cut methods to obtain optimal solutions for small and medium-sized instances. To address large-scale instances, efficient heuristic and metaheuristic algorithms will be designed and implemented. In addition to methodological advances, the project will develop a data-driven optimization decision-support tool integrating models, algorithms, and user-friendly interface. 
]]></description>
      <pubDate>Sat, 16 May 2026 11:45:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703788</guid>
    </item>
    <item>
      <title>An Efficient Algorithm for Solving Collaborative Truck-Drone Parcel Delivery System Considering En-Route Launching and Recovery Points</title>
      <link>https://rip.trb.org/View/2692315</link>
      <description><![CDATA[The logistics industry faces significant challenges in keeping up with evolving demand and supply conditions, especially in urban areas. Traffic congestion during peak hours makes on-time delivery hard. Moreover, time-sensitive products, such as emergency blood and medicine, must be delivered to the customer at the desired time. Drones are a viable solution to urban logistics problems, as they offer several benefits for package delivery. Drones are resilient to traffic delays since they function independently of road infrastructure, unlike conventional vehicles. However, drones have capacity and other constraints; therefore, collaborating with a drone and a truck can make the delivery system more efficient. Although there has been significant research interest in developing truck-drone routing algorithms, a gap remains in developing models that allow for en-route drone launching points and recovery points. The prior research on truck-drone routing assumes that the truck can only reconnect with a drone at a customer location. This project will expand on the prior work to develop optimization models and algorithms to allow with en-route meet points. This added dimension has the potential to reduce truck vehicle miles and subsequently congestion. The solution framework will employ a dynamic programming-based algorithm for the initial solution and a synchronized drone dispatch algorithm to determine the launching and recovery points along the truck route. The proposed algorithm will be able to provide solutions for real-world large instances.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:15:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692315</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicated Lanes</title>
      <link>https://rip.trb.org/View/2676007</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they have to meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic.


Under this project the research team plans to analyze and evaluate the use of automated trucks that will operate on the surface network at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles which may be using the network, thereby addressing the issue of safety. This project will address the potential benefits of automated trucks on dedicated lanes operating at low volume traffic hours. In addition, it will extend the approach to automated truck platoons where automation will also lead to significant fuel savings (up to 20%) due to reduction in aerodynamic drag, bringing the potential to lower costs. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs. The team plans to use as an example a network that includes Interstate 710 (I-710) and the Ports of Los Angeles/Long Beach, a route that generates considerable truck traffic. The team will identify the lanes that can be dynamically dedicated to automated trucks at certain hours and estimate the impact on congestion and fuel savings. The team will use real truck and traffic data to validate their traffic simulators which they will then use to run different scenarios.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:31:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676007</guid>
    </item>
    <item>
      <title>Potential Impact of Autonomous Vehicles on Reducing Congestion</title>
      <link>https://rip.trb.org/View/2676004</link>
      <description><![CDATA[Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total. The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion.

The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium (UE), leading to travel times that can be significantly higher than travel times from the System Optimal (SO), particularly in congested urban networks where the effects of individual decisions cascade throughout the system. With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal. Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas. The centralized routing capabilities of these autonomous services have the potential to reduce congestion. This first phase of this research project will develop centralized optimization models to quantify the impact of using autonomous vehicles on ride-hailing platforms in reducing congestion.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:17:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676004</guid>
    </item>
    <item>
      <title>Real-time Pedestrian Safety and Risk Exposure using Real-time Vehicle Activity and Fleet Composition</title>
      <link>https://rip.trb.org/View/2669652</link>
      <description><![CDATA[Pedestrians face significant a risk crossing roadways as they interact with vehicle traffic (more than 7,000 pedestrians were killed in traffic crashes in 2023).  New tools that assess risk exposure can improve the safety of routes delivered by navigation apps.  In 2025, the research team generated a complete-paths pedestrian network for Downtown Atlanta and inspected the condition of all sidewalk surfaces.  In 2024, Georgia Tech researchers also began collecting very consistent vehicle images using portable high-resolution video cameras positioned on Interstate overpasses (more than one-million vehicle images) for the State Road and Tollway Authority.  A large subset of vehicle images were coded by vehicle make-and-model and used in a prior research project to develop machine-vision artificial intelligence (AI) models to generate fleet composition profiles, for use in energy and safety research.  

In this new project, the researchers will integrate traffic operations data and assess pedestrian exposure to high traffic volumes, high vehicle speeds, and turn movements that cross pedestrian paths.  The team will enhance SidewalkSim and G-MAP (www.its.dot.gov/research-areas/ITS4US/), models that find the “shortest path” (i.e., lowest impedance path) for pedestrian trips between any origin-destination, by integrating traffic exposure into routing impedance factors.  This will allow the apps to route pedestrians around high-risk-exposure crossings.  The team will also map and integrate pedestrian safety countermeasures (bollards, barriers, pedestrian fences, extended crossing times, leading pedestrian intervals, no-crossing zones, no-right-turn-on-red, etc.) in the study area, so that these countermeasures can also be used in impedance-based pedestrian routing along safer paths.  The project culminates by integrating traffic conditions, fleet composition, and risk exposure into SidewalkSim and G-MAP pedestrian routing app and demonstrating the system in downtown Atlanta.  


Another finding from past research was that the resulting machine vision models are so fast, they can run in real-time.  In this new project, the research team will further refine the AI models so that they can be used in edge-computing, processing vehicle fleet composition in the field, without transmitting video data to a data center.  In this project, the team aims to design and package an efficient portable computing system with a high-end graphics card that can operate under year-round Atlanta outdoor temperature and humidity conditions, balancing system performance with power-draw and heating/cooling requirements.  This equipment research (downsizing, enclosure design, heat dissipation, power consideration, etc.) and machine vision model implementation may lead to patentable inventions or licensable software.  If successful equipment deployments are afforded patent protection, the team will work with Georgia Tech’s commercialization office (commercialization.gatech.edu) to develop license agreements for the manufacture of equipment and deployment of portable edge-computing systems and/or will create a GT Create-X business startup.  If the USPTO rejects the patent claims, the team will release equipment specifications, software code, and technology transfer reports under open-source licensing that will allow state DOTs and their consultants to implement the systems.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:27:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669652</guid>
    </item>
    <item>
      <title>Emergency Response and Access Mapping for Rural Navajo Communities </title>
      <link>https://rip.trb.org/View/2658056</link>
      <description><![CDATA[In the Navajo Nation Area, poorly maintained, unpaved, and seasonally hazardous road conditions in rural areas hinder timely response of emergency services. For example, in Crownpoint, heavy snowfall can make it difficult for ambulance services and firefighting vehicles to reach homes, as they must travel through unpaved or unmaintained roads to reach their destinations. Although current routing tools are able to locate the best route between two points, they do not contain pavement condition data or hazard data that would allow for the accurate determination of safe passage for emergency vehicles. Satellite images are also unable to show potholes, ruts, washouts, etc.; therefore, responders are forced to guess which is the best route based on their experience or try different routes until they find one that works. In many cases, this results in substantial delays, especially during severe weather when traditional navigation systems provide little guidance on actual road accessibility. A new platform is needed that has reliable and accessible data to help direct emergency responders to the safest route to the point of origin. Such a system would not only improve response time but also provide agencies with a standardized way to assess roadway risk during rapidly changing environmental conditions. This project will create a reliable, data driven, artificial intelligence (AI)-assisted Road Accessibility Index (RAI), and a geographic information services (GIS)-based routing dashboard utilizing Vialytics' smartphone-based road assessment capabilities, along with data on transportation, crashes, maintenance, and climate to provide real time accessibility ratings for each ten meter section of road within the Crownpoint area (150 miles total) and direct Emergency Medical Services, Fire and Law Enforcement departments towards the safest routes to travel to emergency locations. 
 ]]></description>
      <pubDate>Wed, 04 Feb 2026 19:20:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658056</guid>
    </item>
    <item>
      <title>Assessing Transportation Infrastructure Exposure to Flooding Using Next-Generation Flood Maps in Eastern Oklahoma </title>
      <link>https://rip.trb.org/View/2646941</link>
      <description><![CDATA[Federal Emergency Management Agency (FEMA) flood maps are widely used as a primary reference for the planning, design, and risk assessment of transportation infrastructure, particularly for evaluating flood exposure to roads, bridges, and overall network performance during extreme weather events. While FEMA maps are routinely used by stakeholders, they have come under increasing scrutiny due to a key limitation: FEMA’s 100-year flood maps assume that the “100-year flood” is produced by a single design storm, commonly referred to as the “100-year storm.” As a result, these maps are deterministic, indicating only whether an area is flooded or not under the 100-year storm event. This approach fails to represent the full range of meteorological and hydrologic variability. To address this limitation, FEMA, in collaboration with U.S. Army Corps of Engineers (USACE), National Oceanic and Atmospheric Administration (NOAA), and U.S. Geological Survey (USGS), launched the Future of Flood Risk Data Initiative (FFRDI) project. This initiative represents a paradigm shift: moving from deterministic to probabilistic flood hazard maps. Yet, there remains a critical question: how will these next-generation probabilistic flood maps impact transportation infrastructure risk analyses compared to the traditional, deterministic FEMA products? Addressing this question is urgent. Understanding how the new probabilistic maps alter flood exposure assessments is essential for transportation agencies to update resilience strategies, design standards, and emergency management plans. Without proactive evaluation, agencies risk facing misalignments between outdated flood data assumptions and modern hazard realities. This project aims to lead the first assessment of transportation infrastructure flood hazard exposure using probabilistic flood maps by using the FEMA’s FFRDI framework. The study will focus on the Illinois River watershed in eastern Oklahoma, covering approximately 800 km² from the urban center of Tahlequah to the Arkansas state line. This area includes critical transportation corridors such as State Highways 10 and 82, U.S. Highway 59 and 412. The domain was strategically selected based on the availability of pre-calibrated and validated hydrologic and hydraulic models provided by the USACE Tulsa District, ensuring realistic implementation within the project timeline. 
Using the Illinois River Basin in eastern Oklahoma as a case study, this project has three primary objectives: (1) generate high-resolution probabilistic flood hazard maps following the FFRDI methodology; (2) assess transportation infrastructure flood exposure by intersecting these maps with road and bridge datasets; and (3) quantify differences between traditional and probabilistic flood maps, with a focus on transportation-related impacts. The project will be carried out through five main tasks: Task 1 involves generating synthetic storm events using stochastic storm transposition methods. Task 2 includes hydrologic and hydraulic simulations using HEC-HMS and HEC-RAS models. Task 3 focuses on developing probabilistic flood maps. Task 4 evaluates infrastructure exposure under both traditional and probabilistic mapping approaches. Task 5 tracks progress across all tasks and compiles key deliverables through mid-year and final reporting. Expected outcomes include a publicly available dataset of probabilistic flood hazard maps and a catalog of flood-exposed transportation assets within the study area. Ultimately, this work will demonstrate the added value of probabilistic flood products for improving hazard characterization and will offer practical guidance for DOTs and planners seeking to integrate next-generation flood data into transportation resilience planning. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:01:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646941</guid>
    </item>
    <item>
      <title>Enhancing Rural Freight Resilience in the Southeastern U.S.: Data-Driven Modeling and Decision Support for Supply Chain Efficiency.

</title>
      <link>https://rip.trb.org/View/2643108</link>
      <description><![CDATA[This research aims to address the issue of limited alternative routes in rural freight systems by modeling rural freight networks to identify critical vulnerabilities and evaluate potential recovery strategies. The study also proposes new methods for addressing truck parking shortages using models such as reservation and automated allocation for predicting demand and optimizing supply. The project leverages network science, emerging data sources, and simulation tools to develop methodologies for assessing the resilience of rural freight networks. Additionally, the study will explore the potential of connected and autonomous vehicles (CAVs) for improving operational efficiency and reducing parking demand, particularly for middle-mile delivery and short-range freight operations. This research directly addresses these issues by (1) Developing network-based modeling techniques to analyze rural freight resilience, (2) Identifying critical corridors and evaluating alternative routing strategies, and (3) Proposing innovative truck parking solutions to improve operational efficiency. This includes broader operational strategies such as parking reservations, staging areas near hubs or ports, route reservations, and quicker incident resolution for truckers.  ]]></description>
      <pubDate>Sat, 20 Dec 2025 17:04:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643108</guid>
    </item>
    <item>
      <title>Develop Multi-Modal Maritime-Rail-Roadway Transportation Model for the Texas Inland and Intercoastal Waterways</title>
      <link>https://rip.trb.org/View/2614515</link>
      <description><![CDATA[Texas plays a key role in freight transport nationally and internationally. Freight systems are highly multi-modal (maritime, rail, roadway) and complex, with infrastructure serving different cargo types (bulk goods, containers, hazardous material, etc.). The research team will develop a simulator and decision-support tool for routing and scheduling freight in this system. This work builds on and enhances simulation models and tools the research team has designed, developed, and deployed to successfully represent multi-modal freight operations in the Port of Houston, Houston Ship Channel, and Texas road and rail networks for past projects. This prior experience has shown that the greatest challenges involve data availability, computational speed for statewide modeling, and reflecting the complexities of real-world logistics in routing and scheduling algorithms. The work plan is specifically designed to address these challenges: in terms of data, the research team will use both publicly-available datasets (including the ones used to calibrate its past models) and its existing relationships with port and rail operators and other stakeholders; in terms of computation, the research team will explore a hybrid discrete-event simulator architecture; and in terms of realism, the research team will incorporate uncertainty and reliability in the decision-support tool.]]></description>
      <pubDate>Tue, 28 Oct 2025 11:09:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2614515</guid>
    </item>
    <item>
      <title>Evaluating isolated areas, alternative routing, and economic impact for resilient transportation in North Carolina</title>
      <link>https://rip.trb.org/View/2604572</link>
      <description><![CDATA[Natural disasters, such as flooding, landslides, storm surge, and wildfire can cause severe impacts to the social, environmental, economic, and transportation systems of North Carolina. At the same time, transportation infrastructure plays a critical role in natural disaster response and recovery efforts during these natural disaster events. Unfortunately, extreme hazard events such as these are occurring with greater frequency and intensity. These events can negatively impact road functionality and lead to the loss of essential services. According to the National Oceanic and Atmospheric Administration (NOAA), weather-related disasters have cost over $1.875 trillion since 1980. The built environment isn’t designed to handle many of the impacts that are happening due to extreme hazard events. For example, stormwater systems, culverts, and tidal pumps were all designed for past events— not current and future conditions. The failure of these systems will impact  communities to a level where they may not be able to return to normal for months or years.

Transportation planners and engineers from North Carolina Department of Transportation (NCDOT), as well as other federal, state, and local agencies across the state, and in close collaboration with emergency managers, are increasingly looking for better ways to address these issues and become more resilient, while simultaneously planning for a more reliable transportation network. Planning for extreme events is about finding ways for systems to bounce back to normal as quickly as possible after the negative impacts of an event. One particular issue that NCDOT faces is the rerouting of traffic during and immediately after natural disaster events. Typical considerations include traffic volumes, current conditions, roadway capacities, and overall safety. However, there are other considerations such as the overall economic impact, including issues like commerce, commute times for individuals traveling between work and home, access to essential services, and disruption to local businesses, that should also be taken into account. These impacts can be further compounded in areas where entire networks of roads, such as a neighborhood or community, become cut-off due and thus isolated. This isolation can be due to such factors as a damaged bridge or road washout. Worse yet, these impacts can often last for days or even months. By identifying these areas ahead of time, and better understanding the potential economic impacts, NCDOT and other agencies can be better equipped when planning for a more resilient and sustainable transportation infrastructure system.

The joint proposal team, consisting of researchers from the University of North Carolina at Asheville’s National Environmental Mapping and Applications Center (NEMAC) and the University of North Carolina at Charlotte, proposes a comprehensive and innovative approach to helping NCDOT better understand the forces behind transportation route and commute pattern disruptions, and their effects on local economies, in the face of an increase in extreme hazard events. Through comprehensive user research and discovery, data analysis, and the development of decision-making workflows, the project team seeks to provide NCDOT with actionable insights to better plan and respond to disruptions related to extreme hazard events, ultimately improving infrastructure reliability and community access.]]></description>
      <pubDate>Tue, 30 Sep 2025 11:13:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604572</guid>
    </item>
    <item>
      <title>SPR-5019: Vehicle Moving Pattern Analysis of Road Merge and Split Weaving Areas Involving Highway and Local Streets</title>
      <link>https://rip.trb.org/View/2576684</link>
      <description><![CDATA[Due to the unique capabilities and efficiency improvements of the TASI developed weaving software in project SPR-4930, INDOT aims to extend this tool to analyze weaving and vehicle routing in areas with highway entries (or exits) and multiple local streets. TASI proposes to expand and enhance the software developed in SPR-4930 by generalizing the number of cameras and using collected weaving information at various road segments, including highways, ramps, and local streets, over a specified period. 
]]></description>
      <pubDate>Tue, 15 Jul 2025 15:46:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2576684</guid>
    </item>
    <item>
      <title>Autonomous Electric Ferries (Autoferry) - Low Carbon Connectors - II</title>
      <link>https://rip.trb.org/View/2548807</link>
      <description><![CDATA[Island and peninsula communities in the Puget Sound are often isolated from regional cities like Seattle and Tacoma due to large transit time by road. Regional ferry systems are large and expensive to operate, limiting the number of service times and access points. Most of the ferries only operate between larger regional towns and major cities, isolating smaller communities that often lack bus services as well. Autonomous electric ferries offer a unique low-carbon option to better connect rural communities in the region. In recent years, the Washington state ferry system has struggled with staffing and maintenance of older diesel ferry systems. For example, the residents of Anderson Island and Ketron Island in the south Puget Sound region are served by one ferry that connects them to the mainland. For Ketron Island, the ferry runs only 4 times per day and was out of service completely for several days recently while the ferry was being repaired. This project will focus on addressing key technical objectives with the autonomous ferries, including (1) autonomous docking procedures, (2) optimization of possible routes for weather and tidal events, and (3) building deeper partnerships with commercialization partners. Our methods will include literature review, weather and tidal data collection, design and prototyping, and testing.]]></description>
      <pubDate>Thu, 01 May 2025 15:30:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548807</guid>
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