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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzk1IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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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>
    </image>
    <item>
      <title>Emergency Truck Parking Location Modeling</title>
      <link>https://rip.trb.org/View/2684216</link>
      <description><![CDATA[This research project will develop and apply optimization methods for the modeling of the emergency truck parking problem. This research is directly aligned with the Center for Freight Transportation for Efficient and Resilient Supply Chain (FERSC) goal of advancing research and practice for resilient and safe freight transportation. The results of this research can be used to inform policy and identify needed investments in truck parking facilities. The end goal is to inform the establishment of safe parking facilities to minimize risks for truck drivers and the public that are associated with commercial vehicles stopping at inadequate (sometimes illegal) locations due to the lack of appropriate short- and long-term parking in emergency situations.

A top concern for truck drivers is finding adequate parking. Truck drivers need a safe place to stop for compliance with hours-of-service (HOS) regulations and for other reasons related and unrelated to their jobs. Finding adequate truck parking is even more critical in emergency situations when regular truck parking facilities might not be accessible. This research project will apply optimization methods for the modeling of the emergency truck parking problem. A mathematical programming approach will be used to identify appropriate locations for emergency truck parking under different scenarios of disruptive emergency events. The mathematical model will be tested with an instance developed for Oregon. The results of this research have the potential to inform policy and identify needed investments in truck parking facilities.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:59:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684216</guid>
    </item>
    <item>
      <title>Generating reliable freight disruption measures with freight telematics data</title>
      <link>https://rip.trb.org/View/2684220</link>
      <description><![CDATA[Freight network resilience is critical for economic stability, especially during disasters and infrastructure failures. This study refines disruption measures using Robinsight, COMPASS IOT, and Robinsight telematics data, alongside WAZE crowdsourced data and infrastructure-based instrumentation (TN RDS). Building on prior research, we analyzed freight mobility impacts from events like the Oregon Durkee Fire (2024), Hurricane Helene, and major bridge closures (I-40, I-55, I-84).

Year 3 focuses on validating key disruption indicators, enhancing predictive models, and integrating emerging data sources to assess infrastructure failures and safety risks from freight detours. Aligned with US Department of Transportation priorities, this research provides transportation agencies with actionable insights to improve freight mobility, inform infrastructure investments, and strengthen supply chain resilience. The findings will support data-driven decision-making, ensuring a more adaptive and robust freight transportation system.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:27:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684220</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>Assessing Cybersecurity Risks of Vehicle Accessories: From Wireless Connectivity to Firmware</title>
      <link>https://rip.trb.org/View/2676003</link>
      <description><![CDATA[The research team propose to conduct comprehensive penetration testing on various emerging vehicle accessories. For example, since 2019, the Federal Motor Carrier Safety Administration (FMCSA) has mandated the use of electronic logging devices (ELDs) for most commercial motor vehicle drivers in the United States. These devices are designed to monitor hours of service (HOS) to reduce fatigue-related accidents. Additionally, OBD-II dongles provide diagnostic capabilities for drivers, repair technicians, and insurance companies. Other examples include dash cameras, vehicle health monitors, and infotainment adapters. Recent research including that of the research team has shown that accessories (e.g., ELD, and CarPlay adapter) can serve as attack vectors for compromising vehicle systems. Given that modern vehicles are safety-critical systems, vulnerabilities in these accessories may pose serious real-world risks. More specifically, these accessories typically operate via wireless connections to smartphones, allowing users to manage device settings and monitor performance through companion apps. As a result, vulnerabilities may exist across three components: (1) wireless connectivity (e.g., Bluetooth), (2) mobile applications, and (3) device firmware. As a result, the research team proposes to conduct a comprehensive penetration test on these in-vehicle accessories to reveal any potential vulnerabilities. 

First, the research team will examine the wireless connection between accessories and smartphones, the initial point of interaction. If unsecured, this connection could be exploited by an attacker to gain unauthorized access and control. The research team's prior work on OBD-II dongles has shown that many of these devices lack authentication, allowing attackers to connect even while a driver is actively using them. The research team will assess whether similar vulnerabilities are present in other types of accessories. Next, the team will reverse engineer the companion applications. Building on its earlier work, which revealed CAN command embedded in app code, the research team will extend its analysis to additional accessories. CAN commands are powerful; they can be used to perform operations such as unlocking doors or activating turn signals. Moreover, these apps may store sensitive data, especially in the case of ELDs, which require user authentication to track driver identity and activity. The research team will develop an automated framework that can extract and analyze relevant data from applications, regardless of devices.
Finally, the research team will collect and analyze firmware from these accessories to identify embedded security flaws. The research team will create a methodology to automate vulnerability detection, using techniques such as fuzzing, symbolic execution, and fingerprinting. If the firmware uses outdated or vulnerable open-source components, these could be inherited flaws that present systemic risks.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:17:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676003</guid>
    </item>
    <item>
      <title>Ensuring PNT Resiience</title>
      <link>https://rip.trb.org/View/2676001</link>
      <description><![CDATA[With CARMEN+ support the research team has characterized the timing properties of modulation from the Starlink constellation in order to assess its suitability for providing opportunistic pseudorange-based positioning, navigation, and timing (PNT) as a backup to Global Navigation Satellite System (GNSS). With the same purpose, the team has also uncovered key features of the OneWeb signal structure and has demodulated its data for the first time. The findings have indicated that opportunistic pseudorange-based PNT is not feasible using Starlink signals without aiding from a network of ground receivers. But given such a network, the team has achieved 10-meter-level positioning and 30-ns timing using Starlink signals. The next phase will extend this project along several lines: (i) characterize the modulation timing stability of OneWeb as the team has done with Starlink, (ii) deploy a network of 2 or 3 reference stations so that all ephemeris and transmission time modeling errors may be eliminated, (iii) employ super-resolution techniques to more precisely estimate modulation (e.g., Starlink frame) time of arrival, and (iv) analyze the pattern of assigned beams and side beams from Starlink satellites to predict how many unique satellites would typically be available for a PNT solution, and with what dilution of precision. For these studies, the team will capture and analyze broadband Starlink, OneWeb, and Kuiper data with their own RF equipment from multiple stations. The team believes that the outcome of this work will be of great importance, namely, a backup PNT system with global reach, decimeter positioning, nanosecond timing, inherent signal authentication (via cross-checking unpredictable broadband payload data and against a reference network), and improved resistance to jamming compared to traditional GNSS. Furthermore, the team aims to transfer this technology to their project partners for commercialization.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:15:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676001</guid>
    </item>
    <item>
      <title>Context Aware Optimal Information Selection for Reliable, Resilient, Secure, and Efficient
Cooperative Perception</title>
      <link>https://rip.trb.org/View/2676000</link>
      <description><![CDATA[Cooperative perception significantly enhances a vehicle's local field of view by leveraging shared information from nearby vehicles, thus improving overall situational awareness. However, in densely populated environments, cooperative perception can place substantial strain on both communication band-width and computational resources. Such scenarios often result in excessive redundant information, where multiple vehicles repeatedly report the same objects, provide data at unnecessarily high frequencies, or share information irrelevant to the ego vehicle's current context. These issues cumulatively increase computational overhead prior to data fusion and lead to prolonged decision-making times.
Therefore, an effective filtering mechanism is necessary to selectively retain only the most informative objects. Higuchi et al. proposed a value anticipation-based Vehicle-to-Vehicle (V2V) communication approach. In their method, the sender evaluates the potential informational value to receivers and, based on real-time network conditions, either defers or cancels transmissions. This ensures that primarily essential information is disseminated to neighboring vehicles. In another related study, Zhou et al. introduced the Augmented Informative Cooperative Perception (AICP) algorithm, which incorporates both a routing mechanism and message filtering at the receiver side. Their algorithm utilizes an informative-ness measure to assess and select messages, optimizing resource use while ensuring relevant data is received.

While redundant messaging is typically seen as a problem due to its computational demands, it can also provide significant benefits in enhancing security within V2X communications. Specifically, redundancy can enhance detection of malicious behavior through corroborative data from trustworthy vehicles, thereby improving the security of V2X communications. Lie et al. proposed Misbehavior Detection for Collective Perception Services in Vehicular Communications (MISO-V), which leverages redundancy from received V2X messages to validate incoming perception information. Upon verifying a new message against redundant data, the receiver updates the sender’s trust score based on whether the information is classified as benign or potentially malicious. This updated trust score subsequently guides down-stream tasks in determining whether to integrate or discard information provided by that sender.

Balancing redundancy is thus crucial - maintaining an optimal level of redundancy can simultaneously enhance security and sustain computational efficiency. A suitable approach involves dynamically adjusting redundancy based on multiple factors, including source reliability (assessed via trust mechanisms), the planned route of the ego vehicle, prevailing network conditions, and the Age of Information (AoI). This strategy ensures that cooperative perception remains robust, secure, and scalable, supporting accurate and timely decision-making within cooperative vehicle networks.

The aim is to establish a balance between purposeful and efficient redundancy and safety against potential attack scenarios, optimizing the use of communicated data and the reliability of data fusion necessary for downstream tasks such as planning and control. The research team will explore information redundancy, perception inconsistencies, context aware fusion, spoofing and other attack scenarios, and the detection of attack patterns and will employ optimization strategies and reinforcement learning techniques. The focus will include intersection scenarios with varying traffic densities and connectivity levels. In addition to using the VeReMi dataset, the team will explore extensions to more realistic collaborative perception message attach scenarios for evaluation and validation.
]]></description>
      <pubDate>Mon, 02 Mar 2026 19:08:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676000</guid>
    </item>
    <item>
      <title>Advanced InSAR–UAV-LiDAR Flood-Deformation Risk Monitoring for Efficient Mobility</title>
      <link>https://rip.trb.org/View/2669656</link>
      <description><![CDATA[El Paso’s critical transportation corridors face compounding risks from ground deformation and flash flooding that can severely disrupt efficient mobility, impede traffic flow, and challenge infrastructure reliability. Such infrastructure disruptions compromise public safety by delaying emergency response access and increase collision risk on compromised roadways. Despite advances in satellite monitoring and hydrologic modeling, no integrated system currently provides transportation agencies with rapid and actionable, near-real-time alerts for combined flood-deformation hazards. This project is designed to support uninterrupted mobility directly by developing and demonstrating a unified monitoring framework that fuses millimeter-precision Interferometric Synthetic Aperture Radar (InSAR) deformation maps with Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) terrain models and Synthetic Aperture Radar (SAR)-derived soil-moisture indices to deliver actionable risk assessments. The research addresses a core challenge in maintaining efficient mobility: predicting when and where infrastructure vulnerabilities will coincide with flood conditions. Using validated Persistent Scatterer (PS) and Small Baseline Subset (SBAS) InSAR processing chains, high-resolution UAV-LiDAR surveys, and machine learning algorithms trained on historical events, the proposed system will provide transportation agencies with advanced warning, which enables proactive response and traffic management. The project will produce a composite flood-deformation risk index with demonstrated 90% accuracy in hazard detection. An edge-computing prototype will be deployed in partnership with the Texas Department of Transportation (TxDOT) to operationalize the fusion algorithms, enabling 24-hour processing turnaround and secure web-based risk visualization. Through formal partnerships with TxDOT and El Paso Water, the system will integrate real-time flow gauge data and infrastructure databases to enhance model calibration and validation. The project includes comprehensive technology transfer components, such as Docker-containerized software, training workshops for state Department of Transportation (DOT) engineers, and a commercialization brief outlining licensing pathways for rapid deployment across additional corridors.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:40:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669656</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>LLM-Orchestrated Multi-Layer Digital Twin Network for Cyber-Resilient Traffic Management</title>
      <link>https://rip.trb.org/View/2663602</link>
      <description><![CDATA[Modern connected traffic systems are increasingly vulnerable to cyberattacks capable of propagating rapidly across networked infrastructure, inducing unsafe signal states, traffic congestion, and emergency response delays. Existing anomaly detection approaches including statistical thresholds, rule-based Automated Traffic Signal Performance Measures (ATSPM) and Signal Phase and Timing (SPaT) flags, and classical machine-learning methods such as Isolation Forest and one-class Support Vector Machines operate on limited data modalities and cannot capture cross-layer cyber-physical interactions or operator intent, leaving critical detection gaps in complex attack scenarios.
This project develops a distributed multi-layer digital twin (DT) network for urban traffic systems, enhanced by a large language model (LLM) for context-aware cyber anomaly detection. The framework mirrors physical traffic behavior, cyber infrastructure status, and operational decision processes across a corridor of 4–6 interconnected intersections, enabling early identification of unsafe and malicious events that threaten roadway safety. Each traffic unit is represented by coordinated Physical, Cyber, and Decision Layers: the Physical Layer models real-time mobility and safety conditions using ATSPM, SPaT/MAP data, and detector activity; the Cyber Layer mirrors controller firmware, communication telemetry, and roadside unit status; and the Decision Layer captures operator actions, timing plan updates, and agency-defined safety constraints. A customized transportation-aware LLM ingests both structured telemetry and unstructured logs to generate semantic feature embeddings that capture cross-layer and cross-node dependencies.
A hybrid neural anomaly detection engine integrates Temporal Convolutional Networks (TCNs) to learn evolving traffic and communication behaviors over time with Graph Neural Networks (GNNs) to capture spatial interactions and coordinated disruptions across interconnected intersections. This TCN–GNN architecture enables accurate recognition of both localized cyber intrusions and distributed corridor-level attacks. Detection performance is validated against controlled cyber-attack scenarios—including SPaT spoofing, firmware manipulation, and malicious timing-plan overrides—executed within the DT environment. Upon anomaly detection, the LLM generates actionable mitigation suggestions, such as isolating compromised controllers or reverting to safe fallback signal plans, which are evaluated within the digital twin to ensure that every recommendation supports operational safety, low latency, and service continuity.
The 12-month effort proceeds in two phases: development and calibration of the distributed multi-layer DTs with LLM integration for context modeling, followed by anomaly detection training, validation, and mitigation evaluation. Target performance metrics include detection accuracy of at least 90%, false-positive rates below 10%, decision-support latency improvements of at least 30%, and safety metric improvements of at least 20%. The project delivers a pilot-ready prototype, detailed deployment guidelines, and an open software repository to accelerate adoption by transportation agencies. 
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:28:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663602</guid>
    </item>
    <item>
      <title>Cybersecurity Assurance via AI-Driven Digital Twins for Transportation Safety  </title>
      <link>https://rip.trb.org/View/2663600</link>
      <description><![CDATA[Transportation infrastructure increasingly depends on networked sensor systems for structural health monitoring, yet many operational deployments lack robust data-integrity protections, rendering them vulnerable to cyber-physical attacks. Manipulated sensor readings can misrepresent bridge health, rail conditions, or load limits, thereby creating risks of undetected structural failure, service closures, or catastrophic crashes. Because cyber manipulation directly produces false-safe readings, delays critical maintenance actions, and conceals structural distress, cybersecurity protection constitutes a core safety requirement, not an ancillary concern, for modern monitoring infrastructure.
This project develops a secure, artificial intelligence (AI)-driven digital twin framework that continuously compares real-time sensor data against expected behavioral responses to detect spoofing, tampering, replay, and delay manipulation, and other cyber-physical disruptions. The digital twin is intentionally implemented as a lightweight behavioral model; its purpose is not full structural simulation but rather the generation of expected-response profiles that serve as the ground-truth reference for anomaly detection. Combined with secure sensing hardware, AI-based detection algorithms, and survivability logic, the integrated system maintains reliable monitoring capability even under partial cyber compromise. The framework supports the U.S. Department of Transportation (USDOT) Safe System Approach by preventing cyber-induced safety failures and provides a clear pathway to pilot deployment through a Python-based prototype, agency demonstrations, and structured partner engagement.

Key milestones include the twin baseline model, secure sensing validation, AI detection module completion , and a survivability demonstration with partner input. The resulting system provides transportation agencies with a low-cost cybersecurity layer that protects safety-critical sensing systems from data manipulation and disruption. Deliverables include a Python detection module, interactive dashboard, and validated datasets compatible with existing DOT workflows. By ensuring the trustworthiness of monitoring data, the proposed approach reduces hazard risk, strengthens maintenance decision-making, and scales across bridges, tunnels, and rail systems, offering a realistic and immediate path to pilot adoption within USDOT transportation-cybersecurity priorities.
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:23:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663600</guid>
    </item>
    <item>
      <title>From perception to preparedness: Virtual reality simulations of flooded roadways in coastal communities (UPRM)</title>
      <link>https://rip.trb.org/View/2663232</link>
      <description><![CDATA[Project Description: Coastal flooding regularly disrupts transportation networks, damages infrastructure, and limits access to essential services through storm surge, tidal inundation, and extreme precipitation. These events result in vehicle failures, stranded motorists, pavement damage, and delays in emergency response and daily mobility. Communities with aging infrastructure, limited resources, or constrained evacuation options face heightened vulnerability. The total annual economic burden of flooding in the U.S. ranges from $179.8 to $496.0 billion (US Congress JEC, 2024). In addition, the National Weather Service and the Centers for Disease Control and Prevention report that over half of all flood-related drownings occur when a vehicle is driven into hazardous floodwater. Understanding how drivers decide whether to cross or avoid flooded roads is essential for designing warnings, signage, and roadway treatments that reduce risky behavior and improve outcomes. The use of virtual reality (VR) and immersive 360° scenarios can let residents experience rising water, blocked routes, and mitigation measures without real-world risk, increasing realism and emotional stimulus. Scenario-based VR visualizations can help translate technical flood data into intuitive, actionable information for nontechnical audiences. Local resilience depends not only on infrastructure but also on household-level preparedness and decision-making, including how individuals interpret alerts and respond to flood risks. Chacon-Hurtado (2013) advocates for embedding community preferences and preparedness considerations directly into transportation decision-making frameworks, arguing that investments should be evaluated not only on engineering metrics but also on how they advance local capacity to act under hazard conditions. 
This project will employ virtual reality (VR) simulations of flooded highways that are being developed by the University of Puerto Rico at Mayagüez (UPRM) team to study human behavior and perception in flood scenarios, with three main goals: (1) Enhance public understanding of flood risks by immersing participants in realistic coastal flooding scenarios, (2) Evaluate driver decision-making when encountering flooded roadways, analyzing how variables such as water depth, roadway conditions, and alert systems (e.g., signage, ADAS, in-vehicle alerts) influence choices, and 
(3) Assess community preferences for flood mitigation strategies, using immersive experiences to gather feedback on potential interventions. 
Two VR approaches will be implemented. The first involves a driver simulator with 24–36 participants navigating flooded roadway scenarios to assess behavioral responses under controlled conditions. The second approach will engage community members from coastal municipalities like Isabela, Puerto Rico, in immersive 360° simulations to explore perceptions of flood risk and mitigation strategies. Pre- and post-tests will measure changes in knowledge, perception, and behavioral intent. Insights from both simulations will inform the design of more effective alert systems and flood mitigation strategies that reflect community preferences and improve safety. The findings will support transportation and emergency planning professionals in developing human-centered solutions for flood-prone coastal areas.

]]></description>
      <pubDate>Sat, 31 Jan 2026 12:03:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663232</guid>
    </item>
    <item>
      <title>Professional capacity building and field-based education in coastal transportation durability (UPRM)</title>
      <link>https://rip.trb.org/View/2663231</link>
      <description><![CDATA[Project Description: Coastal transportation assets are routinely exposed to coastal hazards, including flooding, erosion, saltwater intrusion, and storm impacts, that undermine safety, mobility, and service continuity. Capacity building in effective vulnerability assessment and risk management requires more than technical modeling: practitioners need applied skills in infrastructure screening, community‑sensitive evaluation, data‑driven decision making, risk communication, and cross‑agency coordination. The US-DOT developed in 2015 a spreadsheet-based tool called Vulnerability Assessment Scoring Tool (VAST) to assist in documenting the vulnerability of transportation assets in a study area. The assessment includes (1) determining the scope of the vulnerability assessment, (2) selecting appropriate indicators, (3) collecting data about those indicators, and (4) devising an approach to convert raw data about indicators into scores. The result is a set of vulnerability scores that can be used to rank assets by their level of vulnerability or inform other analyses of the results. Today, state and local agencies often lack the staffing and training to use screening tools consistently or to translate assessment results into prioritized investments, while college education delivers essential theory but rarely provide the real‑world, community‑engaged practice needed to make assessments actionable. To close current workforce gaps, this project proposes two complementary capacity‑building tracks: a college‑level track that will prepare upcoming engineers and architects with interdisciplinary foundations and hands‑on experiences, and a professional‑level track that will help upskill practicing engineers in the application of vulnerability assessment methods and tools, such as FHWA VAST. Both tracks share core competencies but differ in depth, delivery, and assessment to match learners’ roles and incentives. 
This project will address professional capacity gaps by developing educational modules, workshops, and training materials for students, communities, and professionals. These resources will be delivered through the University of Puerto Rico at Mayagüez (UPRM) Interactive Learning Hub (ILHUB) and in-person sessions via the Puerto Rico LTAP training program. The ILHUB serves as an online repository focused on coastal resilience and community preparedness. The educational approach integrates coastal resilience, transportation performance, and blue economy strategies into accessible learning resources, enhancing technical knowledge and community capacity across diverse stakeholders. The UPRM team is applying the VAST approach using data from the PR-466/4466 coastal highway corridor in Isabela, Puerto Rico, incorporating community characteristics into the scoring method to enrich the assessment with contextual insights. Professionals will strengthen their competencies in blue economy and transportation durability strategies, as well as in applying the Enhanced VAST. Building on this effort, the project will develop instructor-led and self-paced online training modules to equip professionals with the skills to apply the assessment tool in real-world contexts. Puerto Rico LTAP will assist in delivering these sessions and disseminating the educational materials through its network. On the college track, students will gain hands-on experiences through field case studies of transportation vulnerability situations in coastal zones that will complement their classroom education, strengthen their problem-solving and critical thinking skills, and improve their career readiness. Communities will be also engaged as part of the case studies to improve their understanding of how the durability of transportation systems supports coastal livelihoods.

]]></description>
      <pubDate>Sat, 31 Jan 2026 12:00:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663231</guid>
    </item>
    <item>
      <title>Vulnerability assessment and durability of coastal freight networks (UPRM)</title>
      <link>https://rip.trb.org/View/2663230</link>
      <description><![CDATA[Project Description: Freight networks, including ports, coastal highways, bridges, and distribution hubs, are critical lifelines that sustain regional economies, enable everyday commerce, and support emergency response after catastrophic events. The coastal location of this essential transportation infrastructure makes these assets uniquely vulnerable to extreme natural events such as flooding, storm surge, coastal erosion, and compound hazards. The Puerto Rico’s 2050 Long Range Transportation Plan explicitly calls for reducing transportation vulnerabilities to extreme weather effects and improving connectivity. Puerto Rico could serve as a critical logistics hub for U.S. freight operations in the Caribbean, offering strategic access to regional markets and maritime routes. But recent storms Hurricane María (2017) and Hurricane Fiona (2021) have highlighted the freight network’s fragility and the urgent need for targeted resilience measures. 
The assessment of Puerto Rico’s freight network, one that relies solely on the performance of the highway system, can be a case study to evaluate the system vulnerabilities derived from natural flood hazards, aging infrastructure, urbanization in coastal areas, and congestion in strategic corridors. A rigorous vulnerability assessment combines data from hydrologic and coastal flood modeling with traffic flows, asset condition inventories, and safety records to identify critical and single-point-of-failure links. This integrated analysis can provide a method to reveal which corridors and nodes are most likely to fail under different flood scenarios, how congestion and limited redundancy amplify delays, and which assets require immediate reinforcement or operational changes. It can also uncover system-level interdependencies among ports, road networks, and distribution hubs that are not visible from isolated asset inspections. This project can assist local transportation agencies, freight operators, and decision-makers in identifying risks to the freight network, improving the assessment of infrastructure assets by including the interdependence between ports, road networks, and distribution hubs, and prioritize improvements in strategic planning and project development. This project is envisioned as a two-year program. Year 1 will define Puerto Rico’s primary freight network anchored at the ports of San Juan and Ponce, map major distribution points, and develop an interactive dashboard showing asset condition, corridor flows, crash hotspots, and flood-vulnerable links and nodes. Four analytical dimensions will be assessed: infrastructure condition, traffic flows, safety, and durability, using official data, operational reports, and geospatial analysis to identify hotspots and critical vulnerabilities. Year 2 will focus on network optimization and investment prioritization, applying stochastic and optimization models to produce a prioritized, implementable resilience strategy. A Texas State University team will collaborate in the review of stochastic and optimization approaches, the evaluation of data requirements and computational complexity, and provide recommendations about the best model(s) for optimizing freight flows and prioritizing investments from ports to distributors.

]]></description>
      <pubDate>Sat, 31 Jan 2026 11:32:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663230</guid>
    </item>
    <item>
      <title>Identifying and evaluating the most effective actions to prepare Puerto Rico’s primary ports and freight road transportation infrastructure for flooding disruptions using stochastic models</title>
      <link>https://rip.trb.org/View/2662990</link>
      <description><![CDATA[One of the seven issues listed in the freight assessment section of the 2050 Long Range Multimodal Transportation Plan (LRMTP, approved in 2023) encompasses the need for Puerto Rico’s ports and road freight transportation network (RFTN) to be less vulnerable to extreme weather events that affects the durability of the infrastructure and disrupts the movement of goods and services. Puerto Rico has an excellent geographic location for the transshipment of goods to other places in the Americas. Strategies to mitigate infrastructure damage to ports and roads resulting from overuse and to keep the system operating effectively will help Puerto Rico maintain its position as a global logistics hub. The development of an adaptable highway transport system is crucial, as railroads are not well-developed to undertake the freight transport needs, and the use of the marine-based freight M2 route connecting main and secondary ports is only emerging. 
The objective of this research project is to quantify and classify the impact of certain operational decisions made before and after flood-related weather events on four performance or optimization criteria: ports and RFTN infrastructure, traffic flows, safety, and flexibility to avoid delays and disruptions. The operational decisions to include are: increasing ports’ operating hours, locating regional hub-and-spoke points where freight coming from the ports is transferred from large trucks to smaller vehicles and routed to the distribution points, determining existing or to be developed alternative roads that reduce congestion at hotspots, and routing loads between ports. To accomplish the objective, TXST will develop a preliminary stochastic programming model to optimize a prototype of Puerto Rico’s RFTN, considering multiple flooding scenarios, forecasts of freight demand over 5 and 10 years, and the above-mentioned operational decisions and optimization criteria. A variant of the developed model, which represents the current operations of ports and roads without incorporating any of the proposed operational decisions, will be used for comparison purposes. The main freight distribution points and associated demands to input into the models will be identified in cooperation with the listed project partner faculty at UPRM.  Puerto Rico’s industry, government agencies, and consultants for these agencies will be sources to get the models’ input data, as well as information available online. If needed, the distribution points will be clustered.  In this preliminary model, the unavailable data will be identified and estimated. The model will demonstrate to the Puerto Rico Department of Transportation and Public Works, the Puerto Rico Highway and Transportation Authority, and other relevant agencies a process they can apply for making informed decisions to enhance the durability and resilience of port and RFTN infrastructure under uncertainty caused by flooding and the relevance of collecting any highly relevant and missing data.]]></description>
      <pubDate>Thu, 29 Jan 2026 16:19:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2662990</guid>
    </item>
    <item>
      <title>Role of emerging transportation technologies and safety initiatives in mitigating crashes in coastal communities</title>
      <link>https://rip.trb.org/View/2661744</link>
      <description><![CDATA[Coastal communities face heightened crash risks due to hazards such as hurricanes, flooding, and roadway degradation. Traditional safety countermeasures often fail to address these compounded risks, especially where evacuation routes are limited. This project will investigate how emerging transportation technologies (e.g., connected vehicle systems, advanced driver assistance systems, smart corridors) and safety initiatives (e.g., hazard-responsive traffic management, roadway design measures) can mitigate crash risks in coastal regions. Using literature review, geospatial screening of coastal corridors, and expert validation, the team will develop a prototype decision-support tool linking crash scenarios common in coastal environments with candidate technologies and initiatives. The outcome will provide agencies with a concise, practical framework to assess and prioritize safety solutions that improve infrastructure durability and resilience under coastal hazards.]]></description>
      <pubDate>Thu, 29 Jan 2026 16:13:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2661744</guid>
    </item>
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