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    <title>Research in Progress (RIP)</title>
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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>Drone Network Design for Comprehensive Emergency Response in Rural Utah</title>
      <link>https://rip.trb.org/View/2786771</link>
      <description><![CDATA[Develop an optimized drone network design for comprehensive emergency response in rural Utah to deliver much needed supplies during times of emergency.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:13:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786771</guid>
    </item>
    <item>
      <title>Improving the Performance of Transportation Networks for Disaster Evacuation by Accounting for Friction Factors</title>
      <link>https://rip.trb.org/View/2786706</link>
      <description><![CDATA[The performance of the transportation system under the stress of natural and man-made disasters is one of the most important aspects of transportation system design. In light of recent failures, there has been a push at the state and federal levels to improve U.S. infrastructure resilience. As things stand, disaster preparedness is a secondary consideration, and the models and best practices on which it is based are insufficient. Planning for disasters is a complex process that must account for transportation network design, disaster dynamics, and human behavior under duress. Tools that assist in planning must account for the process holistically, and planners must be willing to consider fundamental reconfiguration of transportation systems that give residents a greater chance of survival in the worst-case scenarios. This seed grant will focus on fundamental methodological development and kick off research into the holistic optimization of evacuation plans and the systems on which they depend, accounting for the various friction factors that often get in the way. The methodology developed in this project and subsequent work will substantially advance the state of the art towards a safer, better-performing transportation network. Expected results are new mathematical modeling/optimization techniques, open-source code, collected and processed datasets, publications, and targeted stakeholder engagement.]]></description>
      <pubDate>Mon, 05 Oct 2026 13:24:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786706</guid>
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    <item>
      <title>Cyber Resilience of Connected and Autonomous Transportation Systems (Phase II): Game-theoretic Security Assurance of Network-level Traffic Signal Control</title>
      <link>https://rip.trb.org/View/2778265</link>
      <description><![CDATA[Two trends are transforming transportation systems. On the bright side, the increasing complexity of cyber-physical technological advances enable connectivity and autonomy through seamless integration of computation, communication, sensing, and control, holding promise for societal and economic impacts. On the dark side, however, these tightly coupled cyber-physical interdependencies can be self-defeating as they can pose new vulnerabilities to accelerating disruptions in cyber space (i.e., cyber-attacks), raising concerns about the safety, security, and privacy of the transportation system users. In view of these two overlapping trends, it is crucial to bolstering the transportation cyber-physical resilience. Building on the findings from a comprehensive literature review of transportation cyber resilience in Phase I of this project, Phase II focuses on security assurance for network-level (connected) and artificial intelligence (AI)-powered (autonomous) traffic signal control systems.

Learning-based control methods, such as deep reinforcement learning (DRL), have gained traction in traffic signal control due to their computational capability to efficiently deal with real-time changes in traffic flow dynamics. Despite growing popularity, DRL has been recently found to be vulnerable to adversarial attacks, which precludes its use in safety-critical traffic signal control applications unless its vulnerabilities are mitigated. From a methodological standpoint, mitigating the security issues facing DRL is still a daunting if not impossible task. The well-established methods in the area of (un)supervised machine learning security do not methodologically suit DRL security, since DRL deals with sequential decision-making, i.e., non-fixed distribution of training data, based on interactions with an environment. The interdependence of the current decision on the ones taken in previous time steps increases the degrees-of-freedom of adversarial attacks on DRL, e.g., an adversary can perturb multiple state observations of the environment over time.

Given the above research gap in DRL security assurance, most relevant studies resort to empirical (aka heuristic) methods, in which attack models sample only some of the possible disturbances (e.g., self-crafted perturbed data) and the devised defense strategies do not generalize out of the experienced attacks. Leaping from empirical security toward certified security, this project proposes a game-theoretic approach to robustifying DRL-based traffic signal control at the network level. The game-theoretic approach provides computational security assurance through explicitly modeling an adversary’s destabilizing perturbations rather than sampling only some of the possible perturbations. The network-level scope of the proposed project prevents higher-order cyber risks compared to isolated traffic signal control, through the explicit consideration of the underlying network effects, such as inter-agent (intersection) interactions over networks.  ]]></description>
      <pubDate>Fri, 18 Sep 2026 10:00:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778265</guid>
    </item>
    <item>
      <title>Accessibility-Based Transportation Network Resilience Planning: Extending Vermont's TRPT</title>
      <link>https://rip.trb.org/View/2775484</link>
      <description><![CDATA[Vermont’s Transportation Resilience Planning Tool (TRPT) assesses the criticality of roadway links by measuring how much vehicle hours of travel (VHT) increases across the network when a link fails. This metric, and similar metrics used in other network resilience applications, are practical as a measure of mobility, but they obscure the question that matters most to communities and planners: which places lose their ability to reach needed destinations? Accessibility — the ease with which people can reach destinations such as jobs, healthcare, grocery stores, and emergency services — is the true purpose of transportation infrastructure, and a road network criticality measure grounded in accessibility can reveal both which links are critical and which communities bear the burden when those links fail. This project will develop an Accessibility Criticality Index (ACI) for Vermont's statewide roadway network and evaluate it against the existing mobility-oriented Network Criticality Index (NCI) used in Vermont's TRPT. The accessibility metric will be developed using publicly available destination data and the Vermont statewide Travel Demand Model (VTDM). The core research questions are: (1) How do link criticality rankings differ when measured using accessibility rather than mobility, and what does that reveal about the current TRPT's blind spots, and (2) what Vermont towns and census tracts stand to see the greatest access loss when links fail, and how does community vulnerability differ when measured using accessibility rather than mobility? Findings will directly inform a proposed Phase 2 project to integrate the ACI into the TRPT web application.]]></description>
      <pubDate>Tue, 08 Sep 2026 16:11:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775484</guid>
    </item>
    <item>
      <title>Drone-in-a-Box: Enhancing Public Safety and Environmental Monitoring on Cape Cod</title>
      <link>https://rip.trb.org/View/2775052</link>
      <description><![CDATA[Cape Cod, a popular tourist destination, faces unique challenges due to its geographic isolation, seasonal population fluctuations, and increasing environmental concerns. This research proposes leveraging drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges. DiB systems offer persistent aerial surveillance and rapid response capabilities, making them ideal for various applications in remote and dynamic environments.

The research team for this project includes representatives from 
Massachusetts Department of Transportation (MassDOT) Aeronautics, MassAutonomy, and Endicott College.  Endicott College will identify appropriate team members based on the expertise needed for each aspect of the project.

This project is significantly strengthened by the collaboration and support of the Wellfleet Police and Fire Departments, who will provide valuable expertise, operational insights, and access to critical resources.

The objectives of this project are to: (1) Evaluate the technical feasibility of deploying DiB systems in the Cape Cod environment, considering factors such as weather conditions, Federal Aviation Administration (FAA) regulations, and communication infrastructure.
(2) Assess the operational effectiveness of DiB systems in each use case, measuring improvements in traffic flow, emergency response times, and shark detection accuracy.
(3) Analyze the cost-benefit of DiB implementation compared to traditional methods. 
(4) Investigate public perception and acceptance of drone technology for public safety and environmental monitoring.
The project will yield data and associated reports detailing the effectiveness of the use of drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges.
]]></description>
      <pubDate>Fri, 04 Sep 2026 14:40:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775052</guid>
    </item>
    <item>
      <title>Synthesis: Flood Early Warning Systems in Texas and the United States</title>
      <link>https://rip.trb.org/View/2768432</link>
      <description><![CDATA[The research team will synthesize Flood Early Warning Systems (FEWS) deployed across Texas and the United States, focusing on systems applicable to State Departments of Transportation (DOTs). Researchers will evaluate automated gates, flashing beacons, rainfall sensors, and stream gauges, and will assess their performance during documented flood events. The research team incorporate guidance from the Texas Water Development Board (TWDB), the 2024 State Flood Plan, and local initiatives including the City of Austin beacon systems and the Houston TranStar network, and will benchmark practices from comparable states.]]></description>
      <pubDate>Fri, 28 Aug 2026 10:01:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2768432</guid>
    </item>
    <item>
      <title>Quantum Bayesian Networks for Cyber-Physical Threat Prediction in Pipeline SCADA Systems</title>
      <link>https://rip.trb.org/View/2762051</link>
      <description><![CDATA[Pipeline supervisory control and data acquisition (SCADA) systems are cyber-physical systems whose expanding connectivity to enterprise networks and remote services has broadened the cyber-attack surface. Intrusions can propagate beyond digital networks to manipulate physical processes, producing operational disruption, safety hazards, environmental damage, and financial loss. Predicting such cross-domain threats is difficult because attacks are rare and governed by complex causal dependencies, and classical sampling-based inference scales poorly under rare-evidence conditions.
This project constructs a Bayesian network representing causal relationships among cyber-attacks, physical impacts, and sensor observations in a pipeline SCADA environment, informed by a hardware-in-the-loop pipeline testbed with conditional probability tables derived from operational data and engineering judgment. After establishing a classical inference baseline for posterior estimation under normal and rare-evidence queries, the network is encoded as a quantum circuit that preserves its causal structure. Three quantum inference families are implemented and evaluated: quantum rejection sampling, quantum likelihood weighting, and a variational Born-machine approach for posterior attack-probability estimation. Methods are benchmarked on a noiseless simulator and on superconducting quantum hardware, with comparison across accuracy, sample efficiency, and circuit-depth requirements.
Expected outputs include a structured probabilistic model of cyber-physical threat propagation, a quantum-circuit implementation suited to near-term hardware, a comparative evaluation report, open-source code, and peer-reviewed publications. Findings will clarify where quantum inference can improve sampling efficiency for rare-event queries in critical infrastructure security and where current hardware constrains deployment, informing risk-assessment workflows for pipeline operators, the Pipeline and Hazardous Materials Safety Administration (PHMSA), and state DOTs, and laying groundwork for quantum-enhanced digital twin and resilience frameworks.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:17:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762051</guid>
    </item>
    <item>
      <title>Bayesian Network Modeling of Cyber-Physical Risk and System Dependencies in Railway Control Systems</title>
      <link>https://rip.trb.org/View/2762050</link>
      <description><![CDATA[Railway systems increasingly depend on integrated control, communications, and operational technologies, and this growing connectivity exposes them to cyber disruptions that can cascade across interdependent subsystems and compromise operations and safety. Existing cybersecurity assessment methods are largely static or deterministic and poorly suited to representing cascading effects in complex railway environments, motivating a probabilistic framework for understanding disruption propagation, critical vulnerabilities, and the resilience benefits of alternative mitigation strategies.
This project develops a railway-specific Bayesian network model representing major cyber-physical components, dependencies, and disruption pathways in railway control systems. The methodology first identifies and characterizes critical railway subsystems and their cyber-physical interdependencies, then defines the model structure and key variables capturing system states, disruption pathways, and operational consequences. Conditional probabilities are developed from available empirical evidence, published literature, and expert-informed estimates. Bayesian inference is applied to quantify how cyber events propagate through the system and influence safety and operational outcomes, and scenario-based resilience analysis identifies critical bottlenecks, assesses mitigation effectiveness, and compares risk-reduction strategies.
Expected products include a validated probabilistic modeling framework for cyber-physical risk in railway control systems, quantitative risk metrics for operational disruption and safety impact, scenario-based analysis tools, and methods for identifying high-impact components and vulnerability concentrations. Outputs will support railway operators, infrastructure owners, and regulators in cyber risk management, infrastructure hardening, investment prioritization, and operational resilience planning, and may inform future guidance and regulatory discussions on railway cybersecurity.
]]></description>
      <pubDate>Wed, 19 Aug 2026 16:14:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2762050</guid>
    </item>
    <item>
      <title>Flood-resilient Transport System Through Integrated Modeling, ML &amp; Immersive AR/VR </title>
      <link>https://rip.trb.org/View/2732359</link>
      <description><![CDATA[Extreme rainfall events increasingly disrupt urban transportation systems by overwhelming drainage infrastructure and causing localized road flooding that impedes last-mile freight delivery, delays emergency response, and disrupts the broader multimodal supply chain. These disruptions limit access to essential services, delay emergency response, and threaten public safety. Building on the research team's previously developed framework (F25-26), this project advances a data-driven approach for high-resolution prediction of urban road flooding in Jackson, Mississippi, integrating geospatial databases, process-based H-H modeling (aligning with rigorous U.S. Army ERDC methodologies), and machine learning (ML) and artificial intelligence (AI) techniques to identify flood-prone road and railway segments. The ML-based surrogate models will maintain computational efficiency, enable timely identification of vulnerable transportation networks, and support emergency response by feeding into JSU Water Lab’s broader web-based-visualization interfaces. This project introduces an interactive K–12 STEM module, age-appropriate hands-on activities along with STEM curriculum module designed for upper-level Civil Engineering undergraduate and graduate students at JSU. This initiative transforms research outcomes into the classroom to modernize workforce training using integrated Augmented Reality (AR) and Virtual Reality (VR) and will be tested with summer student exchange programs. Students can explore and 3D print several transportation infrastructure components, such as culverts, bridges, and urban drainage systems, and evaluate their performance under simulated flood conditions, and experience AR/VR based immersive simulators. Using AR/VR tools, including the Meta Quest platform, available in the PI lab, future transportation engineers will visualize flood scenarios in immersive 3D environments built from existing topographical assets in Unity or Unreal Engine. By combining advanced predictive modeling with experiential learning, the project promotes advanced STEM engagement, and high-tech workforce development, aligning with broader goals of improving multimodal transportation system resilience. While the primary focus is on urban road and rail flooding, these transportation corridors serve as critical connectors to Mississippi's inland waterway freight network, including facilities linked to the Pearl River system and regional multimodal freight movements. Roadway disruptions during extreme rainfall events can delay freight access to ports, intermodal terminals, water-dependent industrial facilities, and affect supply-chain resilience. By identifying flood-vulnerable roadway and railway segments, the proposed framework will support more reliable connectivity between surface transportation infrastructure and maritime freight operations]]></description>
      <pubDate>Tue, 21 Jul 2026 16:34:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732359</guid>
    </item>
    <item>
      <title>Community Response Enhanced Education on Disasters (CREED): Virtual Reality Training to Enhance Transportation</title>
      <link>https://rip.trb.org/View/2732358</link>
      <description><![CDATA[Artificial Intelligence/Virtual Reality (AI/VR) simulation activities enhance critical emergency management functions—planning, forecasting, threat detection, security, and information sharing—ultimately improving the preservation of life and property. These tools create a safe, immersive environment where participants build problem-solving skills, decision-making ability, and confidence in disaster response. This project will deliver education, professional development, and continuous improvement in emergency preparedness for both aspiring and current professionals. The research team will invite high school and community college students to participate alongside university students and practitioners. Participants will engage in hands-on training and live demonstrations using advanced technologies such as virtual reality (VR) and artificial intelligence (AI)-driven simulations. The program fosters multidisciplinary collaboration among disciplines: Emergency Management Technology, Meteorology, Computer Science/Engineering, Health Science, and Journalism, and Media Studies. Through classroom instruction, workshops, and interactive training demos, students will work directly with emergency management professionals, strengthening real-world skills, supporting school-to-work transitions, and enhancing career readiness.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:29:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732358</guid>
    </item>
    <item>
      <title> Foundational Study for Transportation Network Cyber Threat Detection Using AI/ML
</title>
      <link>https://rip.trb.org/View/2719314</link>
      <description><![CDATA[Primarily, this effort will build a foundational software prototype to identify, source, and package data for use in machine learning (ML)-based anomaly detection models. Secondly, Georgia Tech (GT) will work in close partnership with Georgia Department of Transportation (GDOT) to assess one (1) out of the eight (8) representative cyber threats and platforms, and characterize detection performance using standardized performance measures (e.g., classification accuracy, precision and recall).
]]></description>
      <pubDate>Thu, 25 Jun 2026 11:32:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719314</guid>
    </item>
    <item>
      <title>Identifying, Assessing, and Managing Events for Critical Infrastructure Resilience in Surface Transportation</title>
      <link>https://rip.trb.org/View/2712209</link>
      <description><![CDATA[State departments of transportation (DOTs) play an important role in protecting critical transportation infrastructure from natural hazards, human-caused events, and emerging threats. However, there is currently no consistent methodology for identifying and assessing critical infrastructure within surface transportation systems. Definitions of “criticality” vary across agencies, and existing approaches often lack integration with broader resilience, security, and emergency management frameworks.

Previous research has focused on specific threats, such as terrorism or cybersecurity, or on resilience to natural hazards, but gaps remain in developing proactive, risk-based approaches that address the full range of threats and system interdependencies. Transportation systems are closely linked with other infrastructure sectors, such as power and water systems, and disruptions can have cascading impacts across regions.

Research is needed to help state DOTs better define their role in coordinating with law enforcement, emergency responders, and other planning partners and system owners to enhance preparedness, response capabilities, proactive resilience planning, stakeholder coordination, and implementation of national infrastructure protection frameworks.

The objectives of this research are to (1) identify and assess surface transportation system interdependencies and develop a risk-based approach to managing a wide range of threats; and (2) develop an infrastructure resilience guide with case studies, a list of stakeholder roles and responsibilities, and decision-making tools to help state DOTs identify, assess, and manage risks to critical infrastructure within surface transportation systems.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:41:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712209</guid>
    </item>
    <item>
      <title>Development of Additional Risk Models for the Transportation Asset Risk and Resilience Manual</title>
      <link>https://rip.trb.org/View/2712191</link>
      <description><![CDATA[State departments of transportation (DOTs) are increasingly incorporating risk and resilience considerations into transportation asset management and system performance decisions. NCHRP Project 23-32, “Transportation Asset Risk and Resilience (TARR),” is developing quantitative models for evaluating risks associated with natural hazards and transportation assets; however, several priority threat-asset pairs were not included within the initial project scope because of funding and schedule constraints.

Additional modeling capabilities are needed to help agencies evaluate risks associated with human-caused threats, technological failures, and additional natural hazards affecting transportation systems. Together, these include threats such as cybersecurity incidents, wildfires, post-fire debris flows, coastal erosion, landslides, and critical infrastructure failures that can significantly affect infrastructure performance and resilience planning.

The objective of this research is to expand the Transportation Asset Risk and Resilience (TARR) Manual by developing additional quantitative threat-asset risk models to support transportation agencies in evaluating infrastructure risks and system resilience. The research will build upon the analytical framework developed through NCHRP Projects 23-09 and 23-32.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:04:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712191</guid>
    </item>
    <item>
      <title>Traveler Information for Rural Maryland</title>
      <link>https://rip.trb.org/View/2701236</link>
      <description><![CDATA[Disseminating traveler information in rural Maryland has long been challenging due to the limited deployment of Intelligent Transportation System (ITS) devices, such as dynamic message signs (DMS) and highway advisory radios (HARs). This challenge becomes particularly acute during major events, such as hurricanes or large-scale evacuations, when clear and accessible communication is critical. HARs, which operate on AM radio frequencies, have been a key tool for disseminating detailed information, but their reliance on outdated technology has made maintenance costly and increasingly unfeasible as spare parts become unavailable. The Maryland Department of Transportation State Highway Administration (MDOT SHA) has already retired half of its HARs and faces difficulty maintaining the remaining units, which are still vital in certain areas. ]]></description>
      <pubDate>Wed, 13 May 2026 09:12:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701236</guid>
    </item>
    <item>
      <title>TraCR Foundational Project: TraCR Collective Transportation Cybersecurity Testbeds</title>
      <link>https://rip.trb.org/View/2697454</link>
      <description><![CDATA[The National Center for Transportation Cybersecurity and Resiliency's (TraCR's)
foundational project aims to develop technological tools, prototypes, testing platforms, and facilities to ensure the cybersecurity and cyber-resilience of multimodal transportation systems and related infrastructure. The project is led by Clemson University (Clemson) under the strategic direction of Dr. Ronnie Chowdhury (Lead PI), with coordination support from Dr. Sabbir Salek (Co-PI), and involves all eight other TraCR partner institutions organized into four subgroups. A structured project governance framework, including biweekly subgroup meetings, monthly full-team coordination meetings, quarterly progress reporting and advisory board engagement, ensures alignment with project milestones, integration across teams, and effective monitoring of technical progress and deliverables. 

Clemson collaborates with Benedict College (Benedict), South Carolina State University (SCSU), and the University of Texas at Dallas (UTD) to advance a comprehensive, automated threat modeling capability for multimodal transportation systems. Building on the Transportation Cybersecurity and Resiliency Threat Modeling Framework (TraCR-TMF), the team conducts testbed-in-the-loop evaluations within Clemson’s real-world cybersecurity testbed, implementing digital-twin-based cybersecurity analysis of in-vehicle networks, and engaging state transportation agencies to assess operational transferability. Additionally, the team will work to integrate graph-based reasoning models into threat modeling, deploy supervised ModernBERT classifiers, and align with the MITRE Embedded Systems Threat Matrix to strengthen structured system-to-vulnerability mapping and improve threat coverage across transportation cyber-physical systems.

The other partner institutions will develop additional real-world and virtual testing platforms to support cybersecurity experimentation for multimodal transportation. Florida International University (FIU) and the University of Alabama at Tuscaloosa (UA) are jointly advancing the Open-Source Connected and Automated Mobility Co-Simulation (OpenCAMS) environment and related simulation platforms, integrating SUMO, CARLA, and network simulation tools, to evaluate privacy-aware multimodal large language models and post-quantum-secure C-V2X communications. Their efforts further include the development and validation of spoofing attack models targeting Basic Safety Message transmissions and multi-frequency GPS receivers, as well as investigations into backdoor-resilient perception systems and the security of vision-language models for intelligent transportation applications.

Purdue University (Purdue) and the University of California, Santa Cruz (UCSC) are advancing adversarial testing methodologies through integrated physical-virtual experimentation frameworks that combine miniature autonomous vehicle testbeds, CARLA/METS-R simulation coupling, and scenario-based vulnerability discovery. These activities include simulation-to-real validation of perception and traffic signal spoofing attacks, evaluation of V2X safety message vulnerabilities, cybersecurity analysis of shared micromobility Bluetooth pairing protocols, implementation of lightweight post-quantum cryptographic protections for vulnerable road user beacons, and closed-loop security assessments of traffic signal controller infrastructures, along with investigations of secure multimodal AI agents and memory-augmented reasoning architectures for autonomous robotic transportation systems.

In addition, Morgan State University (MSU) is enhancing its connected vehicle cybersecurity experimentation capabilities by developing replay-attack models targeting C-V2X onboard units and evaluating mitigation strategies in its real-world testbed environment, in collaboration with Clemson. These efforts quantify communication-level impacts on safety-critical applications and support the development of deployable countermeasures to strengthen resilience against wireless attack vectors affecting connected transportation infrastructure.
]]></description>
      <pubDate>Thu, 30 Apr 2026 12:19:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697454</guid>
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