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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Revisiting the Intrusion Detection in In-Vehicle Networks</title>
      <link>https://trid.trb.org/View/2672799</link>
      <description><![CDATA[An in-vehicle network (IVN) is the internal communication network that connects all sensors and control units in an autonomous vehicle. Sensors and control units use the IVN to send perception-related messages and control commands for the normal and safe operation of the vehicle. However, the IVN, by design, is vulnerable to network attacks due to a lack of adequate security mechanisms. This paper presents a Dynamic Windowing Intrusion Detection System (DWIDS) that adapts its detection window in real-time based on observed anomalies, enabling accurate and responsive attack detection. Unlike prior methods that focus on static configurations or single-attack detection, DWIDS supports multi-label classification and real-time tuning of detection parameters. The system is evaluated using two public benchmark datasets (CHD and IVN-IDS challenge) which feature diverse and imbalanced attack types. Experimental results demonstrate high performance across key metrics (e.g., >98% precision, >97% recall and F1-score), including for rare attacks. The findings confirm DWIDS’s practicality and robustness for deployment in real-world autonomous vehicle environments.]]></description>
      <pubDate>Thu, 07 May 2026 11:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672799</guid>
    </item>
    <item>
      <title>A Survey of Small Sea-Surface Target Detection for Maritime Search and Rescue</title>
      <link>https://trid.trb.org/View/2672791</link>
      <description><![CDATA[The detection of small surface targets plays a critical role in maritime search and rescue (SAR) operations, ensuring the safety of people and property at sea. This paper provides a comprehensive review of the latest advancements and research in small sea surface target detection for maritime SAR missions. Deep learning-based models facilitate accurate target detection and localization by transforming image or video frames into high-dimensional abstract representations, enabling effective detection in complex sea surface environments. However, challenges such as occlusion, blurring, and reflections on the sea surface significantly complicate small target detection. To address these challenges, this paper summarizes a range of effective approaches, including context information, multi-scale learning, anchor-free detection, super-resolution, attention mechanisms, and sample-oriented approaches. These approaches aim to enhance the performance of small target detection in applications such as uncrewed aerial vehicles (UAV) and uncrewed supply vessels. Furthermore, this paper classifies small target datasets, providing a detailed overview based on their collection methods and application scenarios, while highlighting representative datasets. Through a thorough analysis of both methodologies and datasets, this paper offers valuable insights and directions for the future development of small target detection technology in maritime search and rescue operations.]]></description>
      <pubDate>Thu, 07 May 2026 11:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672791</guid>
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    <item>
      <title>Hardening the Economical Acquisition of Intersection Data to Improve System Integrity</title>
      <link>https://trid.trb.org/View/2694451</link>
      <description><![CDATA[Modern traffic management systems increasingly depend on real-time Signal Phase and Timing (SPaT) data generated by Traffic Signal Controllers (TSCs) to support safety, mobility, and emerging connected-vehicle applications. The applications of SPaT outputs require timely and reliable access to such data from the traffic signal controllers. However, these controllers are safety-critical infrastructure and exposing them to external networks introduces significant cybersecurity and operational risks. Thus, there is a need for mechanisms that provide secure, low-latency access to SPaT data without compromising controller integrity. The information flow methods that are used traditionally for connected and automated vehicle (CAV) environments have several security weak points. There exists a need for new communication protocols through which new system implementation paradigms can be evaluated at higher levels of information security. Two previous Center for Connected and Automated Transportation (CCAT) projects addressed this need by developing and testing a hardware-enforced data diode architecture and device that enable strictly one-directional extraction of SPaT data from traffic signal cabinets. The system prevents any inbound communication to the controller while allowing real-time data dissemination over existing network paths, requiring no new communication infrastructure. The current study was motivated by the potential use of systems engineering and model-based design to reduce development complexity and cost. The Cubicon design methodology, a new graphical language that translates high-level system behavior into executable software, improving maintainability and architectural clarity, was adopted. The phase of the project implemented a lightweight communication protocol with differential SPaT updates to reduce bandwidth usage and improve scalability. Together, these contributions demonstrate a more secure, efficient, and cost-effective approach for extracting and disseminating SPaT data, supporting both current traffic operations and future connected transportation systems. The research product can have profound and far-reaching impacts. For the hundreds of thousands of signalized intersections that currently exist in the United States, the economical and secure acquisition of SPaT information facilitates critical traffic management functions including red-light violation warnings, signal priority, and trajectory planning.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694451</guid>
    </item>
    <item>
      <title>An explicit approach for modeling the performance of transportation networks immediately after an earthquake</title>
      <link>https://trid.trb.org/View/2691683</link>
      <description><![CDATA[Surface transportation systems play a vital role in supporting a region’s functionalities. They are expected to remain operational before and even after a hazardous event (e.g. an earthquake). The importance is evident to estimate the post-disaster performance of traffic systems under a probability-based framework, considering the uncertainties arising from both the hazards and the transportation infrastructure fragilities. This paper proposes an explicit approach for evaluating the performance of transportation systems immediately after an earthquake event. The method estimates the spatial distribution of vehicles in the traffic network in a closed form and thus is relatively efficient compared with traditional methods (e.g. an agent-based method). The applicability of the proposed approach is demonstrated through an application to the post-earthquake performance assessment of the traffic network in Tangshan City, China, a city that suffered catastrophically from the 1976 Tangshan Earthquake. Analytical results show that the proposed method can well reflect both the temporal and the spatial variations of the traffic flow, and thus offers rational support for predicting the post-earthquake traffic scenarios and for optimizing strategies to improve the transportation capability under emergent conditions.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691683</guid>
    </item>
    <item>
      <title>Resilience-based post-earthquake restoration scheduling for urban interdependent transportation-electric power network</title>
      <link>https://trid.trb.org/View/2691674</link>
      <description><![CDATA[As critical lifeline systems, transportation network (TN) and electric power network (EPN) are highly susceptible to natural hazards, such as earthquakes during their service life. At the same time, restoration of damaged TN and EPN is essential to support the post-earthquake reconstruction and emergency rescue in affected areas. Restoration strategies were traditionally developed for TN or EPN separately. However, neglecting the potential interconnection between these two networks in the recovery phase may lead to detrimental consequences, as in real-world scenarios, the obtained strategy may be less efficient or even unfeasible given that recovery of one system is usually dependent on the others for service provision. Accordingly, this paper presents a resilience-based framework for post-earthquake restoration of interdependent transportation-electric power networks. In this framework, restoration independencies and functionality dependencies are introduced to represent the interaction between TN and EPN. Then, a bi-level optimization model with the objective of maximizing seismic resilience is established to characterize the network recovery problem. Furthermore, a solution algorithm that incorporates a genetic algorithm and a chromosome validity test operator is designed to obtain the near-optimal solution. Finally, the proposed framework is illustrated through two numerical examples.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691674</guid>
    </item>
    <item>
      <title>Assessing cybersecurity resilience of digital ports using a BN-FAIR framework</title>
      <link>https://trid.trb.org/View/2697230</link>
      <description><![CDATA[The digitalization of seaports enhances efficiency but heightens exposure to complex cyber–physical disruptions threatening transport continuity and supply chains. This study proposes a probabilistic framework to quantify and strengthen port cyber–physical resilience by integrating Bayesian Networks (BN) for causal inference with the Factor Analysis of Information Risk (FAIR) model for financial impact estimation. The model captures interdependencies across six domains: cyber layer, physical layer, interconnection layer, organizational, external threat, and individual factors. Sensitivity analysis identifies critical vulnerabilities such as legacy software, unpatched systems, hardware failures, and limited cybersecurity awareness. Six targeted strategies, including Zero-Trust Architecture, predictive maintenance, and adaptive governance, are mapped to high-impact nodes to guide investment priorities. By combining probabilistic reasoning with economic quantification, the BN–FAIR framework provides transport policymakers and port operators with a transparent, data-driven tool to enhance resilience and sustainability in maritime logistics systems.]]></description>
      <pubDate>Tue, 05 May 2026 09:26:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697230</guid>
    </item>
    <item>
      <title>Dynamic evolutionary pathway analysis of urban rail transit flood risks and intelligent decision support based on knowledge graphs</title>
      <link>https://trid.trb.org/View/2664366</link>
      <description><![CDATA[With the intensification of global climate change, rainstorm disasters have become increasingly frequent and catastrophic. Urban rail transit (URT) systems, which are primarily constructed underground, possess structural features that make them particularly vulnerable to severe impacts during heavy rainfall events. Such disasters can result in significant casualties and substantial losses. Meanwhile, extensive domain-specific knowledge has been accumulated from historical disaster events. Effectively extracting and utilizing such knowledge is essential for improving disaster risk identification and enhancing emergency management practice. To address these challenges, this study proposes a method for analyzing risk evolution mechanisms by integrating Knowledge Graph and Natural Language Processing (NLP) technologies. The knowledge graph enables structured knowledge representation and facilitates effective knowledge reuse. Building on this, a knowledge-driven decision support model is established by combining the language understanding capability of NLP with the inferential capacity of knowledge graphs. Case studies of representative examples are conducted to validate the effectiveness of the proposed method in this study. The findings show that structuring knowledge in the form of a graph network offers significant advantages for the intelligent analysis of disaster risk evolution. On one hand, a large amount of multi-source, heterogeneous knowledge related to URT flood risks is systematically structured and represented, thereby enhancing the efficiency of knowledge utilization by decision-makers. On the other hand, integrating NLP with knowledge graph–based risk network analysis enables the accurate identification of potential risk paths, providing valuable insights and a foundation for disaster prevention and mitigation decision-making.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664366</guid>
    </item>
    <item>
      <title>Proactive multi-USV maritime search and rescue in stochastic wave environments: A hierarchical, non-causal reinforcement learning framework</title>
      <link>https://trid.trb.org/View/2697767</link>
      <description><![CDATA[Maritime search and rescue (SAR) in stochastic wave environments presents a critical challenge for multi-Unmanned Surface Vehicle (USV) systems and demands a fine balance between search efficiency and operational safety. This paper proposes a novel hierarchical reinforcement learning framework, termed Non-Causal Reward Multi-Agent Proximal Policy Optimization (NCR-MAPPO), to address this challenge. Our framework decouples the mission by employing a strategic guidance system based on International Maritime Organization (IMO) standards for systematic coverage while a tactical motion controller built upon the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm learns cooperative execution. The core innovation is a Non-Causal Reward (NCR) mechanism that incorporates short-term wave field prediction into the decision process, enabling a shift from reactive collision avoidance to proactive seakeeping control. Through comprehensive simulations, we demonstrate the superiority of our framework. Compared to the standard MAPPO baseline, NCR-MAPPO significantly enhances survivability by reducing wave impact incidents by 27% and exposure to hazardous sea states by 25% while maintaining high mission efficiency. This work provides a robust solution for autonomous marine systems by bridging the gap between regulatory compliance and predictive safety control.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:39:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697767</guid>
    </item>
    <item>
      <title>Resilience assessment and enhancement of urban transportation interdependent network under cascading failure</title>
      <link>https://trid.trb.org/View/2664296</link>
      <description><![CDATA[Urban transportation systems are essential for sustaining urban growth and ensuring efficient resource allocation. Existing studies primarily focus on evaluating network resilience after system disturbances, with insufficient attention paid to the response mechanisms during disturbances and the enhancement of resilience afterward. Therefore, we propose a cascading failure model that considers passenger transfer impedance, and design a recovery priority strategy for failed nodes to maximize the resilience of the urban transportation interdependent network (UTIN). Specifically, based on traffic sensing data, we construct a station-centric UTIN to assess structural resilience under various disruption scenarios and different transfer distances. By combining impedance function and flow redistribution, passenger behavior and node load update are considered. Additionally, the recovery priority strategy for failed nodes is discussed. The results indicate: 1) UTINs with longer transfer distances exhibit stronger resistance to risks. When considering impedance costs, the optimal transfer distance is 800 m. 2) During cascading failure propagation, optimizing flow distribution effectively lowers the critical capacity threshold required for system stability, thereby enhancing network resilience. 3) During the recovery phase, different recovery strategies exhibit significant differences in their effectiveness in restoring system resilience. The research findings provide valuable references for disaster prevention, emergency response, and post-disaster recovery in urban transportation systems.]]></description>
      <pubDate>Thu, 30 Apr 2026 11:28:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664296</guid>
    </item>
    <item>
      <title>A GNN-Based Framework for Assessing Flood Impacts on Highway Networks: Integrating Network Structural, Functional, and Social Features</title>
      <link>https://trid.trb.org/View/2685634</link>
      <description><![CDATA[Due to global change, natural disasters such as floods have become more frequent in recent years. An effective impact assessment of highway networks before and during floods can help transportation departments prioritize resources and take necessary emergency measures. Although current works have assessed the flood impacts from different perspectives, none have comprehensively evaluated the integrated impacts that capture network structural, functional, and social features, limiting their reliability for decision-making and resilience planning in engineering management. To address this gap, we proposed a graph neural network (GNN)-based framework that incorporates two synthesized indicators—the disaster impact index and criticality score—to integrate structural, functional, and social features. These multidimensional features were inputs to the GNN model, enabling it to capture complex interdependencies and more accurately predict traffic flow and speed under disasters. The practicality of this framework was demonstrated in the case study of Harris County affected by floods caused by Hurricane Harvey. The results showed that Beltway 8, IH-10, and IH-45 were most vulnerable to potential impacts before the flood, while Beltway 8, US-59, and IH-10 were most impacted during the flood, highlighting the need for proactive preflood preparedness and prioritized postflood recovery for these critical roadways. The proposed framework captures complex interdependencies among multidimensional features and more accurately predicts traffic flow and speed. Consequently, it provides a more realistic prediction of the uncertainties in transportation network performance under disasters, offering a robust and practical tool for resilience planning and resource prioritization of other critical infrastructure systems in engineering management.]]></description>
      <pubDate>Thu, 30 Apr 2026 09:11:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685634</guid>
    </item>
    <item>
      <title>TraCR Foundational Project: TraCR Collective Transportation Cybersecurity Testbeds</title>
      <link>https://trid.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://trid.trb.org/View/2697454</guid>
    </item>
    <item>
      <title>Guiding Electronic Control Unit (ECU) Firmware Fuzzing with Hardware-Level Side-Channel </title>
      <link>https://trid.trb.org/View/2697290</link>
      <description><![CDATA[This project develops a novel electromagnetic (EM) side-channel-guided fuzzing framework for automotive Electronic Control Unit (ECU) firmware security testing. The approach addresses key challenges in ECU security research, namely that firmware is often encrypted, proprietary, and tightly coupled to hardware, making traditional instrumentation-based fuzzing impractical. By capturing and analyzing EM emanations from ECUs during execution, the framework estimates code coverage without requiring firmware modification, instrumentation, or rehosting. The system integrates this EM-based coverage feedback into a fuzzer to guide test case generation via Controller Area Network (CAN) bus communication. The project will conduct extensive fuzzing campaigns on real automotive ECUs from various manufacturers to discover zero-day vulnerabilities and enhance vehicle cybersecurity. ]]></description>
      <pubDate>Wed, 29 Apr 2026 16:47:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697290</guid>
    </item>
    <item>
      <title>Prototype Development and Pilot Deployment of Ground-Based Intelligent Infrastructure for Resilient Positioning, Navigation, and Timing</title>
      <link>https://trid.trb.org/View/2696990</link>
      <description><![CDATA[Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), form the backbone of modern positioning, navigation, and timing (PNT) services. However, these space-based systems are inherently vulnerable to cyberattacks such as jamming, spoofing, as well as unintentional interference, including signal blockage, particularly in dense urban areas, indoor environments, and adversarial environments. The growing dependence on GNSS, driven by the rapid adoption of autonomous and connected systems, has exposed a single point of failure in the global PNT infrastructure. GPS signals are extremely weak at the Earth’s surface, enabling low-cost jammers or spoofers to easily disrupt receivers. In response to the 2020 Executive Order on strengthening national resilience through responsible use of PNT services signed by President Donald J. Trump, US DOT, the Department of War (DoW), and the Department of Homeland Security (DHS) have jointly emphasized the need for complementary and backup PNT capabilities that are interoperable and independently capable of sustaining precision timing and navigation for critical infrastructure during GNSS outages or cyberattacks. The research goal is to develop and demonstrate a prototype ground-based, GPS-compatible, cyber-secure PNT architecture that can generate, synchronize, and broadcast authenticatable GPS-like signals from a network of ground-based nodes, allowing existing GPS receivers to obtain valid PNT solutions without hardware modification. This goal will be achieved through the following specific research objectives: (1) Design and generate authenticable GPS-compatible terrestrial signals that replicate the L1 C/A (coarse/acquisition) waveform while embedding virtual ephemeris and adjusted clock-offset parameters to enable accurate and PNT computation from ground transmitters. (2) Develop intelligent terrestrial nodes (at least four nodes) equipped with chip-scale atomic clocks, edge computer, and transmitters to establish a distributed ground-based PNT architecture. (3) Synchronize terrestrial nodes with a master clock using precision timing distribution techniques to maintain consistent and reliable time alignment across the network. Real-Time Kinematic (RTK) positioning and differential methods will also be explored using the GEODNET hub within the UA network. (4) Demonstrate that an off-the-shelf GPS receiver can deliver a valid PNT solution using terrestrial signals through software-only modifications, thereby validating the practicality, backward compatibility, and deployment readiness of the proposed system.
]]></description>
      <pubDate>Wed, 29 Apr 2026 16:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696990</guid>
    </item>
    <item>
      <title>Artificial Intelligence (AI)-Enabled Post-Quantum Cryptography for Real-World Deployment of Secure and Resilient Communication for Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2696971</link>
      <description><![CDATA[Cellular Vehicle-to-Everything (C-V2X) communication, standardized in 3GPP Release 14/15 PC5 sidelink mode, is the US DOT-approved technology for direct V2V (vehicle-to vehicle)/ V2I (vehicle-to-infrastructure) communications in the 5.9 GHz band. Current standards and specifications (e.g., SAE J3161 and USDOT/ITE RSU requirements) mandate PC5 Mode 4 operation to enable interoperable safety messaging using conventional cryptographic methods, such as Elliptic Curve Cryptography (ECC). However, existing cryptographic methods are vulnerable to quantum-computing-based attacks. Thus, integrating Post-Quantum Cryptography (PQC) into C-V2X communication is imperative to ensure future resilience. However, National Institute of Standards and Technology (NIST)-standardized PQC algorithms introduce large key sizes and computational complexity, resulting in significant latency and bandwidth overhead. These effects risk violating the 100-ms end-to-end delay requirement for 10 Hz Basic Safety Messages (BSMs) and can congest the 5.9-GHz safety channel. Moreover, the direct integration of PQC into current communication standards, such as IEEE 1609.2 and ETSI, poses challenges because these frameworks were originally designed for lightweight ECC-based operations. 
Similarly, post-quantum Homomorphic Encryption (HE) offers robust privacy protection by allowing computation directly on encrypted data without decryption; however, its high computational cost and ciphertext expansion currently limit its use in latency-critical V2X and infrastructure-to-infrastructure (I2I) scenarios. Therefore, deploying PQC and HE within operational testbeds demands optimized scheduling, resource allocation, and adaptive algorithm management to balance cryptographic strength with real-time constraints. To address these challenges, this project aims to develop and evaluate artificial intelligence (AI)-enabled PQC through real-world prototype implementation and testbed integration, thereby enabling the real-world deployment of secure and resilient communication in intelligent transportation systems. Specifically, the objectives of this project are: (i) implementation and real-world evaluation of an AI-enabled PQC integration and dynamic switching framework for C-V2X communication; (ii) real-world evaluation of a privacy-preserving roadside unit (RSU)-Cloud (I2C) communication pipeline using post-quantum homomorphic encryption; and (iii) development of a federated learning framework for collaborative PQC selection policies. To address the USDOT and TraCR 2025–2026 priorities, this project emphasizes field-tested prototypes and operational validation, rather than simulation-only evaluation, to ensure deployment relevance. This project will directly contribute to the deployment of PQC-enabled V2X communication for a secure and reliable connected transportation system.
]]></description>
      <pubDate>Wed, 29 Apr 2026 16:39:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696971</guid>
    </item>
    <item>
      <title>Cybersecurity Analysis to Support Secure Transportation Cyber-Physical Systems</title>
      <link>https://trid.trb.org/View/2696948</link>
      <description><![CDATA[This project is designed to strengthen transportation cybersecurity in an era where artificial intelligence (AI)-enabled and AI-enhanced cybercrime is increasingly capable of scaling deception, automation, and attack sophistication against cyber-physical systems. The project builds directly on the team’s prior legislative gap analysis and the development of TraCR AI, a retrieval-augmented large language model that helps transportation officials and policy makers identify applicable legal obligations and compare regulatory approaches across jurisdictions. The central technical contribution is the development and testing of a modular defensive wrapper for transportation-focused large language model (LLM) and retrieval augmented generation (RAG) tools, intended to detect and mitigate adversarial attacks that exploit legal reasoning systems and to support a testbed for LLM-targeted cybercrime scenarios. In parallel, the project includes legal and policy research that assesses gaps in US frameworks for addressing AI-enabled cybercrime and draws on international examples to inform best practices for governance, enforcement, and secure deployment. The overall objective is to produce deployable defenses and practitioner-facing guidance that improve trustworthiness in AI-assisted compliance and policy analysis for critical transportation infrastructure.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:33:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696948</guid>
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