<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    </image>
    <item>
      <title>Evaluating Autonomous Vehicles’ Pedestrian Safety Benefits in Road Networks</title>
      <link>https://rip.trb.org/View/2778253</link>
      <description><![CDATA[Pennsylvania experienced more than 3000 pedestrian-related injuries in 2022, according to PennDOT, and the Governor’s Highway Safety Association estimates that Pennsylvania ranked 10th among all 50 states in 2022 for the most pedestrian fatalities. This project will explore and evaluate the potential for connected autonomous vehicles (CAVs) to reduce these pedestrian-involved incidents. While many past works have explored using CAV capabilities to detect and avoid pedestrians, the research team will specifically focus on ensuring that these benefits are felt where and when they are most needed.

Recent studies show that pedestrian fatalities in the United States are especially common at night and on roads that do not have infrastructure designed to support pedestrians. By increasing the concentration of CAVs, which may be safer for pedestrians, on such dangerous roadways, the team may be able to increase overall pedestrian safety. Many pedestrian-related incidents, for example, involve human driver distraction, and CAVs can make use of non-visual (e.g., radar) environmental data to detect pedestrians, especially at night. However, CAVs may also be more dangerous for pedestrians if their detection systems fail. Moreover, intentionally diverting CAVs to areas that are dangerous for pedestrians may have implications for safety and traffic flow through the road network as a whole. For example, proactively routing CAVs to these areas may create more traffic congestion and potentially increase accidents involving vehicles, leading human-driven vehicles to avoid this area. Indeed, CAVs will likely be deployed initially in mixed-autonomy settings with human-driven vehicles, which adds an additional layer of complexity in analyzing how pedestrians will affect safety or traffic flow.

The team plans to build on their 2023-4 Safety21 project investigating CAVs’ safety effects in road networks. In particular, the team will make use of their mixed autonomy simulator from the previous project, which includes models of vehicle-involved accidents but does not explicitly seek to improve pedestrian safety. The work will comprise four parts: (i) integrating pedestrian fatality and injury statistics into the mixed autonomy simulator, (ii) developing analytical models for traffic flow and pedestrian injuries/fatalities given CAV routing, (iii) designing algorithms for CAVs to minimize pedestrian fatalities and injuries, and (iv) using the simulator and the analytical models to evaluate whether CAVs can effectively reduce pedestrian injuries and fatalities, as well as any tradeoffs with increasing traffic congestion.]]></description>
      <pubDate>Thu, 17 Sep 2026 17:12:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778253</guid>
    </item>
    <item>
      <title>Potential Impact of Autonomous Vehicles on Reducing Congestion - Phase 2</title>
      <link>https://rip.trb.org/View/2733194</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, leading to travel times that can be significantly higher than travel times from the System Optimal, 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.

In Phase 1, the research team developed the research foundation for this problem. This work includes the literature review and the development of an online dispatch-and-relocation framework for a centrally controlled autonomous vehicle fleet. The Phase 1 framework matches requests to vehicles while accounting for pickup deadlines, near-term vehicle availability, and proactive repositioning toward forecasted demand. Phase 1 also establishes a comparison structure against a traditional human-driver ride-hailing system and an initial simulation capability that traces routes and estimates vehicle miles traveled, deadhead miles, passenger waiting time, revenue, and related performance measures.

Phase 2 will build directly on this foundation and is the primary focus of the next stage of the project. In Phase 2, the team will scale the optimization and simulation framework so it can solve problems at the size of major metropolitan areas. This includes extending the model to larger networks and richer demand patterns, improving computational tractability for larger instances, and strengthening the simulation module so it can evaluate passenger-vehicle matches and route decisions under more realistic operating conditions. To make the model scalable, the team will aggregate the service region into zones and solve the resulting problems repeatedly over short rolling horizons. The team will also need to calibrate the demand forecasting and routing inputs for large urban networks and test the algorithms on progressively larger instances to ensure that the solution quality and computation time remain practical. The purpose of Phase 2 is to determine how much centralized control of autonomous fleets can reduce system-wide travel, deadhead mileage, waiting times, and congestion when evaluated on realistic metropolitan-scale settings.

]]></description>
      <pubDate>Wed, 22 Jul 2026 17:37:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733194</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicate Lanes- Phase 2</title>
      <link>https://rip.trb.org/View/2733038</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 must 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. 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.

During the first phase of the project, the research team developed microscopic traffic simulation model which the team validated using real data from I-710. The network considered was part of I-710 and the team assumed as a first step single origin-destination (OD) flows. The team considered the scenario where trucks sharing the same road network as passenger cars become automated and operate on dedicated truck lanes 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, thereby addressing the issue of safety.

The ongoing phase 1 study shows that by removing a number of trucks which are about 0.4% of all vehicles during a high peak traffic and have them automated and operating on dynamically dedicated lanes during off peak traffic the travel time for trucks is reduced by 4.5% while the travel time of passenger vehicles during the high peak traffic decreases by about 3%. These preliminary findings suggest that temporal rescheduling of freight demand, combined with dynamic lane management, could improve both freight and overall network performance. In phase 1 the team simply used the traffic simulator to test their ad hoc approach of moving trucks from high peak to low peak traffic without any form of optimization.

In phase 2 the team plans to extend the approach as follows: (1) The team will expand the road network to include some of the most popular truck routes covering short medium and long-haul scenarios. The issue of parking and refueling in the absence of driver will also be addressed. (2) The team will extend the results of phase 1 to multiple interacting OD pairs, allowing the framework to capture more realistic freight demand patterns and network-level coordination effects. (3) The team considers the case of truck platoons which will include fully automated truck platoons but also the more realistic case where the first truck in the platoon has a human driver. In other words, the lead truck will be driven by a human driving and following trucks will be electronically connected and fully automated. Truck platooning is an attractive concept as it has shown to have the potential of reducing aerodynamic drag and contribute to significant fuel savings. (4) The team plans to optimize their decisions of temporal rescheduling of freight demand, combined with dynamic lane management to achieve the best possible outcome. The team views the problem as assigning loads in 2 dimensions temporal and spatial in a way that reduces travel time and lowers fuel cost for both trucks and passenger vehicles.]]></description>
      <pubDate>Wed, 22 Jul 2026 17:28:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733038</guid>
    </item>
    <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- Phase 1</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 - Phase 1</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 Fleet Composition via Machine Vision and AI for use in Pedestrian Safety and Risk Exposure Studies
</title>
      <link>https://rip.trb.org/View/2669652</link>
      <description><![CDATA[In 2024, Georgia Tech researchers developed automated procedures to capture very consistent vehicle images using portable high-resolution video cameras positioned on Interstate overpasses.  The team collected and processed more than one-million vehicle images from four locations in the Atlanta Metro Area for a State Road and Tollway Administration research project, in which a large subset of vehicle images were coded by make-and-model.  The team subsequently developed machine-vision models and AI tools to identify vehicle make-and-model combinations as part of a 2024 CHEM research project, for use in a variety of pedestrian safety and risk exposure studies.  In the initial model development work, the team worked in Georgia Tech’s PACE distributed computing system.  However, the resulting models are so fast, the team has concluded that the AI fleet composition models can run in real-time.

In this follow-on project, the research team will refine the current models to further reduce computational requirements so that the AI models can be used in edge-computing, which will process vehicle fleet composition on site, without transmitting video data to a data center.  The team’s second challenge is to design and package an efficient portable computing system with a high-end graphics card that can operate under year-round temperature and humidity conditions.  The team will balance system performance with power- draw and heating/cooling requirements.  Overpass video is very consistent, providing elevated and unobscured rear views of vehicles.  In the third phase of the project, the team will develop protocols for collecting video from major arterials and will develop machine-vision models from a wider variety of camera views (as constrained by intersection design and safe placement of equipment).  The team anticipates that arterial corridor implementation will be much more complicated and that strict camera placement protocols may be needed to reach the accuracy of overpass-image-derived models.

The team anticipates that equipment development (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 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>
  </channel>
</rss>