<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Artificial Intelligence (AI)-Enabled Post-Quantum Cryptography for Real-World Deployment of Secure and Resilient Communication for Intelligent Transportation Systems</title>
      <link>https://rip.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://rip.trb.org/View/2696971</guid>
    </item>
    <item>
      <title>Cybersecurity Analysis to Support Secure Transportation Cyber-Physical Systems</title>
      <link>https://rip.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://rip.trb.org/View/2696948</guid>
    </item>
    <item>
      <title>Evaluating Fuzzed Connected Vehicle Data to Support Travel Demand Modeling </title>
      <link>https://rip.trb.org/View/2663276</link>
      <description><![CDATA[The Virginia Department of Transportation (VDOT) is currently developing a method to use connected vehicle (CV) data to support the development of travel demand models. The work includes estimating nuanced information about trip time, trip distance, and path patterns with fine geographic and temporal resolution. CV trajectory data providers may use algorithms that affect the raw vehicle trajectory data for privacy reasons. These algorithms may affect the feasibility, accuracy, and robustness of VDOT’s application of CV data for planning purposes. This project assesses the impact of such algorithms used by two different CV trajectory data providers on potential VDOT application scenarios related to calibration and validation of transportation planning models. This research will further assess the potential of using such data to support the development of truck ODs, and if feasible, a valuable enhancement to the current VDOT truck origin-destination (OD) estimation procedure.
]]></description>
      <pubDate>Sun, 01 Feb 2026 11:00:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663276</guid>
    </item>
    <item>
      <title>GraphSecure Security Enhancement for Autonomous Vehicle Networks with Knowledge Graph</title>
      <link>https://rip.trb.org/View/2559306</link>
      <description><![CDATA[The project aims to address cybersecurity challenges in connected autonomous vehicles (CAVs), particularly the risk of malicious actors disseminating false information in vehicular networks. The project will develop a scalable, knowledge-graph-based framework, GraphSecure, designed to enhance message authentication and anomaly detection in CAV environments. Objectives: (1)  Real-time Event Detection and Knowledge Graph Construction: Develop a real-time event detection system that integrates diverse data streams from vehicle sensors and traffic systems to dynamically construct distributed knowledge graphs. (2) Privacy-Preserving Data Sharing in Knowledge Graphs: Implement privacy-preserving frameworks that ensure secure data sharing within knowledge graphs, utilizing cryptographic and anonymization techniques. (3) Graph-Based Authentication Protocols: Create protocols that leverage relational and contextual knowledge within graphs for fast, accurate message authentication. (4) Validation and Prototyping: Conduct real-world validation and simulation tests to evaluate the reliability, effectiveness, and practical scalability of GraphSecure.
]]></description>
      <pubDate>Thu, 29 May 2025 21:34:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2559306</guid>
    </item>
    <item>
      <title>Managing and Sharing Traffic Management Systems Video

</title>
      <link>https://rip.trb.org/View/2558409</link>
      <description><![CDATA[Traffic management systems (TMSs), which integrate advanced technologies, software, and data, are essential tools for enhancing the safety, efficiency, and reliability of surface transportation. These systems play a vital role in helping agencies meet the growing and evolving mobility needs of travelers, service providers, partner agencies, and the general public.

Traditionally, TMSs provided only static images of roadway conditions, but technological advancements have transformed this practice into 24/7 live-streaming video feeds of traffic conditions. Increasingly, individuals and private companies are capturing, scraping, or archiving these video feeds, and often repackaging and selling the data to public or private customers, raising legal, technical, and operational challenges for transportation agencies.

Most TMSs do not record or archive video feeds due to concerns over legal obligations and public information requests, risks of releasing sensitive or personally identifiable information (PII), potential liability from unintended uses, and technical burdens of video management. The rising expenses of data storage and telecommunications add complexity to video management.

Research is needed to help agencies evaluate the implications, benefits, and risks of sharing TMS video.

OBJECTIVE: The objective of this research is to develop a guide for transportation agencies on managing and sharing access to TMS video. The research will identify current practices, challenges, unintended consequences, and opportunities for improvement.]]></description>
      <pubDate>Tue, 27 May 2025 20:58:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558409</guid>
    </item>
    <item>
      <title>Legal Aspects of Airport Programs. Topic 16-03. Legal Requirements and Liabilities Arising from Airport Security</title>
      <link>https://rip.trb.org/View/2555868</link>
      <description><![CDATA[Airports want to create a travel environment that is efficient and responsive to passengers' needs without compromising security. Airport operators share responsibility for the ever-evolving security requirements with federal and local agencies. Security practices should be designed with individuals’ rights in mind, but finding a balance between safety and security and individuals’ rights can be a challenge. These challenges encompass all passengers, visitors, and airport workers subject to security protocols.

Airports are confronted with a multifaceted framework of requirements related to security, such as constitutional and civil rights and privacy, and new requirements and technology add complexity. Research is needed to support airports to understand their legal responsibilities, the potential risks, and the implications for airport operations, passengers, and stakeholders.

The objective of the research is to examine public facing airport security measures and help airports understand the legal requirements and liabilities arising from airport security. The final report should: (1) provide stakeholders with the current legal and regulatory airport security requirements; (2) identify the parties responsible (e.g., airport operator, federal agency, etc.); and (3) identify additional practices undertaken by airports that are not required by current legal and regulatory airport security requirements. The report should identify and analyze the risks or exposures that may arise in the context of these requirements and practices. The report should identify and analyze novel legal issues involved with anticipated or proposed airport security measures.

The research should supplement ACRP Legal Research Digest 27: The Fourth Amendment and Airports to include relevant cases decided after that publication.]]></description>
      <pubDate>Tue, 20 May 2025 14:39:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2555868</guid>
    </item>
    <item>
      <title>Data Analysis for Security and Privacy in Advanced Traffic Management Systems (ATMs)</title>
      <link>https://rip.trb.org/View/2549186</link>
      <description><![CDATA[The project is driven by two main objectives: 1) Real-Time Intrusion Detection Algorithm (DL_TraSec): The first objective involves the creation of a cutting-edge intrusion detection algorithm that harnesses the power of deep learning. This algorithm, named DL_TraSec, is purpose-built to cater to the intricacies of advanced traffic management systems (ATMS). By analyzing real-time data streams, it will identify anomalies and patterns indicative of impending DDoS attacks. The aim is to proactively prevent such attacks before they disrupt the system. 2) Intrusion Detection System (ID_TraSec): Building upon the DL_TraSec algorithm, the second objective is to develop an integrated intrusion detection system, ID_TraSec. This system will not only accurately analyze the ongoing behaviors of potential DDoS attackers but also predict their actions. The ID_TraSec system will act as a comprehensive shield against a variety of DDoS flooding attacks targeting different components of the ATMS infrastructure.

The proposed system's efficacy spans multiple levels of the ATMS architecture: 1) Central Control System: The system will bolster the Central Control System, enhancing its resistance to DDoS attacks and ensuring continuous operation. 2) ATMS Sub-systems (Corridors or Areas): It will secure ATMS sub-systems by vigilant monitoring and prompt response to DDoS threats specific to these zones. 3) Intersection Advanced Transportation Controllers: The system will safeguard the critical intersection controllers, a key component of efficient traffic management. The project's methodology is dynamic and interdisciplinary, combining several elements to form a comprehensive solution. It integrates: 1) Literature Synthesis: A thorough review of existing literature on DDoS attacks, deep learning algorithms, and traffic management systems will inform the project's direction. 2) Conceptual Models: Building upon the literature, conceptual models will be developed to design and structure the DL_TraSec algorithm and the subsequent ID_TraSec system. 3) Real-Time Big Data: The collection and analysis of real-time big data from the ATMS environment will provide the foundation for refining and validating intrusion detection algorithms. 4) Algorithm Development: Novel algorithms will be crafted, leveraging deep learning techniques, to enable the accurate detection and prediction of DDoS attacks in real-time scenarios.]]></description>
      <pubDate>Tue, 06 May 2025 16:44:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2549186</guid>
    </item>
    <item>
      <title>Smart Transportation Digital Infrastructure: Advancing System Equity, Resilience, and Safety through Multi-Source Open-Standard Data Integration</title>
      <link>https://rip.trb.org/View/2549292</link>
      <description><![CDATA[The recently emerging trend of sensor technology, ubiquitous and high-performance computing is creating a revolutionary paradigm shift in the coming years. Through data and feedback, both simulated and real, a Digital Infrastructure (DI) for smart cities has received increasing attention. The pandemic, in many cases, is accelerating this need, as there are critical needs for analyzing the health and safety of citizens. With the rise of the digital infrastructure, cities have many adoptions in transportation, utilities, buildings, and citizen services. For community mobility applications, the pairing of the virtual and physical world allows analysis of data and monitoring of systems, evaluating different improvement strategies, and planning the future by using simulations. Smart Transportation Digital Infrastructure (STDI) is to create sustainable urban systems that benefit the citizens and societies at large. It represents a fundamentally new approach for close-loop large-scale system modeling, ubiquitous communication, and diverse data synthesis and can provide an integrated solution for data, simulation, connection, and human interaction, which are the four key elements of achieving the paradigm’s main functions for smart community applications. 

There are three critical challenges for STDI: digital at scale, decision intelligence in data-intensive systems, and consistency between objectives, decisions, and execution. Open-STDI could not only dramatically reduce the cost and complexity of managing computers and simulation models but also redefines what is tractable regarding dispersed bi-directional intra-system communication between different community stakeholders and citizens. Therefore, connected and smart communities represent an ideal DI application, but one that requires transformative advances both within the traditional domains of city planning, community policy analysis, network behavior, and demand forecasting but also within the emerging field of DI itself.

This project aims to develop an Open data hub and Open-source data analysis platform for transportation-focused Open-STDI applications. That is, the proposed framework Open-STDI will deliver rapid prototyping of STDI and enable smarter multimodal policy decisions for transforming the livability, sustainability, and resilience of the community. A successful STDI in the project will enable both: (1) integration of a variety of legacy and emerging transportation data sources, covering supply, demand, resilience, safety, and security aspects, etc.; and (2) integration of data analysis, data visualization, traffic estimation on a unified platform. Designers, faculties, and engineers can use the integrated platform for quick, inexpensive prototyping of new ideas, which further provides a potential for creating new forms of citizen engagement by communities and new approaches to city operations and management by city planners. This project, in collaboration with the IEEE Department of Global Sales & Customer Operations, will primarily utilize data from IEEE National Performance Management Research Data Set (NPMRDS) and OpenStreetMap data to understand mobility characteristics, and use Google mobility data to discover resilience features of transportation system.
]]></description>
      <pubDate>Mon, 05 May 2025 16:01:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2549292</guid>
    </item>
    <item>
      <title>Cybersecurity Analysis and Guidance to Support Secure Transportation Cyber-Physical Systems</title>
      <link>https://rip.trb.org/View/2526705</link>
      <description><![CDATA[The overarching goal of the continued work on this project is to put what the research team has learned in their first year of research with respect to the legislative schematic of the U.S. in context with the international scope of cybersecurity policy. Specifically, the objectives of this project are to evaluate United States cybersecurity transportation law and policy compared to existing international models to support continued domestic regulatory development and update the policy toolkit (including large language model (LLM) and guidance report) with insights from an expanded legislative corpus, visualized data, extended industry feedback and training, to provide a comparative analysis model of transportation cybersecurity research (identifying how the U.S. can improve its policy based on industry and international input).]]></description>
      <pubDate>Thu, 20 Mar 2025 17:15:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2526705</guid>
    </item>
    <item>
      <title>Liability of Transportation Entity for the Unintentional Release of Secure Data or the Intentional Release of Monitoring Data on Movements or Activities of the Public</title>
      <link>https://rip.trb.org/View/2487295</link>
      <description><![CDATA[Transportation entities collect various amounts of data for transportation related purposes. Without debating the legitimacy of the purpose for the specific data collected, what liability exists for the accidental release of data that was to be securely held by the entity for a transportation related purpose? Similarly, what liability exists for the intentional release of data generated from the monitoring of the movements or activities of the public?  The main objective of this research is to review what statutes, regulations or common law exist regarding the release of data collected for transportation purposes. Included in this research are questions concerning the application of public records laws and the application of any constitutional, statutory or common law privacy rights. ]]></description>
      <pubDate>Wed, 08 Jan 2025 16:11:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487295</guid>
    </item>
    <item>
      <title>Develop an Intelligent Digital Assistant for Project Planning</title>
      <link>https://rip.trb.org/View/2420077</link>
      <description><![CDATA[Texas Department of Transportation (TxDOT) manages an extensive repository of electronic and digital data about past projects. An intelligent decision support tool is essential for processing and making sense of the available data to improve the accuracy of estimating project cost and duration as well as supporting management decision such as resource allocation, project prioritization, risk assessment, and strategic planning. However, special attention should be given to the security of such a tool as it accesses an unprecedented large amount of data and communicates with key TxDOT planners. Any unauthorized access could potentially impact the fair bidding process. The overall goal is to develop a Secure Construction Project Planning Digital Assistant (Secure-CPPDA).]]></description>
      <pubDate>Thu, 22 Aug 2024 16:53:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420077</guid>
    </item>
    <item>
      <title>Establishment of a Public-Private Transportation Data Exchange Center</title>
      <link>https://rip.trb.org/View/2414020</link>
      <description><![CDATA[State departments of transportation (DOTs) across the country are paying third-party vendors to provide traffic data from their own roadways. The data is costly, and the source of the data is not verifiable.  An entire industry is emerging that increasingly perceives DOTs as their primary financial source.  Currently a significant number of vehicles are equipped with sensors, cameras, and in some cases lidar technology, which have the capability to provide DOTs accurate information pertaining to vehicular movements on United States roadways. This information is currently retained by the Original Equipment Manufacturers (OEMs). If this information was shared with the departments of transportation, it could lead to a safer and more efficient system for their users.  Cooperative efforts by a consortium of State entities to facilitate this data exchange could yield substantial benefits for the DOT, the OEMs, but most importantly the people driving on the roadways. Historically, there has been a reluctance within the private sector to share information with government agencies.  The premise of this TPF study is to collaboratively look at the development of a data repository that could act as an impartial arbiter of data to ensure all personal identifying details are excluded. 

The goal of the project is to develop a secure computing, data analytics, and storage infrastructure with a data repository (data warehouse or data lake) that will collect all relevant vehicle data as well as other types of data (including environment data, weather data, among other sources) and share the data with DOTs for data analyses without any identifying information attached to improve transportation decision-making.]]></description>
      <pubDate>Wed, 07 Aug 2024 15:56:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2414020</guid>
    </item>
    <item>
      <title>Policy Analysis and Guidance to Support Secure Transportation Cyber-Physical Systems</title>
      <link>https://rip.trb.org/View/2334604</link>
      <description><![CDATA[In a world of automated mobility, innovative but legally unprecedented technological advances are creating a host of policy issues for legislative and regulatory bodies. Although the need for regulatory and enforcement measures is dire, there is no singular federal law or federal regulatory framework that governs cybersecurity or data privacy focusing on transportation in the United States. The overarching goal of this project is to perform a nationwide survey of existing federal and state cybersecurity and privacy regulatory measures and analyze that legislative landscape in light of identified risks and threats to the transportation industry.  The project attempts to answer: (i) what federal and/or state agencies are responsible for governing cybersecurity practices in the U.S., including risk assessment, preventative measures, detection of breaches, and remedial enforcement; and (ii) how do industry experts assess the greatest risks/threats to ensuring cybersecurity in the transportation sector?  Key contributions include developing a novel prompt-based LLM model and a domain-specific question-answering system that will ensure the security of various systems in the transportation domain.  The results of the above-discussed review and analysis could be used to construct a comprehensive transportation cybersecurity policy guidance document and/or toolkit.]]></description>
      <pubDate>Mon, 05 Feb 2024 16:09:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2334604</guid>
    </item>
    <item>
      <title>Privacy-preserving Transportation Data Analytics Using Synthetic Data Generation</title>
      <link>https://rip.trb.org/View/2334602</link>
      <description><![CDATA[The sharing of large-scale transportation data is beneficial for transportation planning and policymaking. However, it also raises significant security and privacy concerns, as the data may include identifiable personal information, such as individuals' home locations. To address these concerns, synthetic data generation based on real transportation data offers a promising solution that allows privacy protection while potentially preserving data utility. Although there are various synthetic data generation techniques, they are often not tailored to the unique characteristics of transportation data, such as the inherent structure of transportation networks formed by all trips in the datasets. In this paper, we use New York City taxi data as a case study to conduct a systematic evaluation of the performance of widely used tabular data generative models. In addition to traditional metrics such as distribution similarity, coverage, and privacy preservation, we present a novel graph-based metric tailored specifically for transportation data. This metric evaluates the similarity between real and synthetic transportation networks, providing potentially deeper insights into their structural and functional alignment. We also introduced an improved privacy metric to address the limitations of the commonly used one. Our experimental results reveal that existing tabular data generative models often fail to perform as consistently as claimed in the literature, particularly when applied to transportation data use cases. Furthermore, our novel graph metric reveals a significant gap between synthetic and real data. This work underscores the potential need to develop generative models specifically tailored to take advantage of the unique characteristics of different domains, such as transportation.]]></description>
      <pubDate>Mon, 05 Feb 2024 16:01:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2334602</guid>
    </item>
    <item>
      <title>Secure and Privacy-Preserving Federated Learning for Connected and Automated Vehicles </title>
      <link>https://rip.trb.org/View/2334515</link>
      <description><![CDATA[In this project, the research team aims to deploy, integrate, and validate privacy-preserving and secure learning solutions for connected and automated vehicles (CAVs). The proposed solution includes four major goals: (1) an integrated anomaly detection technique to detect and isolate backdoor attacks in federated learning (FL) settings for CAVs; (2) a hybrid approach that maintains concrete security against backdoor attacks in CAV applications; (3) a privacy preservation mechanism to ensure CAVs data is protected against data leakage; and (4) training proposed learning models using real-world and synthetic CAV data for assessment and validation purposes. 
These four goals will mainly contribute to “Security and Resiliency” of intelligent transportation systems while ensuring “Data Privacy” which is aligned with USDOT goals to secure transportation systems and the National Center for Transportation Cybersecurity and Resiliency's (TraCR’s) vision. This project develops a distributed learning architecture to serve as a platform for future projects. For example, it can be used to develop and evaluate other privacy-preserving techniques for intelligent transportation systems use cases. 
]]></description>
      <pubDate>Mon, 05 Feb 2024 15:46:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2334515</guid>
    </item>
  </channel>
</rss>