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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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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>Assessing Cybersecurity Risks of Vehicle Accessories: From Wireless Connectivity to Firmware</title>
      <link>https://rip.trb.org/View/2676003</link>
      <description><![CDATA[The research team propose to conduct comprehensive penetration testing on various emerging vehicle accessories. For example, since 2019, the Federal Motor Carrier Safety Administration (FMCSA) has mandated the use of electronic logging devices (ELDs) for most commercial motor vehicle drivers in the United States. These devices are designed to monitor hours of service (HOS) to reduce fatigue-related accidents. Additionally, OBD-II dongles provide diagnostic capabilities for drivers, repair technicians, and insurance companies. Other examples include dash cameras, vehicle health monitors, and infotainment adapters. Recent research including that of the research team has shown that accessories (e.g., ELD, and CarPlay adapter) can serve as attack vectors for compromising vehicle systems. Given that modern vehicles are safety-critical systems, vulnerabilities in these accessories may pose serious real-world risks. More specifically, these accessories typically operate via wireless connections to smartphones, allowing users to manage device settings and monitor performance through companion apps. As a result, vulnerabilities may exist across three components: (1) wireless connectivity (e.g., Bluetooth), (2) mobile applications, and (3) device firmware. As a result, the research team proposes to conduct a comprehensive penetration test on these in-vehicle accessories to reveal any potential vulnerabilities. 

First, the research team will examine the wireless connection between accessories and smartphones, the initial point of interaction. If unsecured, this connection could be exploited by an attacker to gain unauthorized access and control. The research team's prior work on OBD-II dongles has shown that many of these devices lack authentication, allowing attackers to connect even while a driver is actively using them. The research team will assess whether similar vulnerabilities are present in other types of accessories. Next, the team will reverse engineer the companion applications. Building on its earlier work, which revealed CAN command embedded in app code, the research team will extend its analysis to additional accessories. CAN commands are powerful; they can be used to perform operations such as unlocking doors or activating turn signals. Moreover, these apps may store sensitive data, especially in the case of ELDs, which require user authentication to track driver identity and activity. The research team will develop an automated framework that can extract and analyze relevant data from applications, regardless of devices.
Finally, the research team will collect and analyze firmware from these accessories to identify embedded security flaws. The research team will create a methodology to automate vulnerability detection, using techniques such as fuzzing, symbolic execution, and fingerprinting. If the firmware uses outdated or vulnerable open-source components, these could be inherited flaws that present systemic risks.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:17:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676003</guid>
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    <item>
      <title>Ensuring PNT Resiience</title>
      <link>https://rip.trb.org/View/2676001</link>
      <description><![CDATA[With CARMEN+ support the research team has characterized the timing properties of modulation from the Starlink constellation in order to assess its suitability for providing opportunistic pseudorange-based positioning, navigation, and timing (PNT) as a backup to Global Navigation Satellite System (GNSS). With the same purpose, the team has also uncovered key features of the OneWeb signal structure and has demodulated its data for the first time. The findings have indicated that opportunistic pseudorange-based PNT is not feasible using Starlink signals without aiding from a network of ground receivers. But given such a network, the team has achieved 10-meter-level positioning and 30-ns timing using Starlink signals. The next phase will extend this project along several lines: (i) characterize the modulation timing stability of OneWeb as the team has done with Starlink, (ii) deploy a network of 2 or 3 reference stations so that all ephemeris and transmission time modeling errors may be eliminated, (iii) employ super-resolution techniques to more precisely estimate modulation (e.g., Starlink frame) time of arrival, and (iv) analyze the pattern of assigned beams and side beams from Starlink satellites to predict how many unique satellites would typically be available for a PNT solution, and with what dilution of precision. For these studies, the team will capture and analyze broadband Starlink, OneWeb, and Kuiper data with their own RF equipment from multiple stations. The team believes that the outcome of this work will be of great importance, namely, a backup PNT system with global reach, decimeter positioning, nanosecond timing, inherent signal authentication (via cross-checking unpredictable broadband payload data and against a reference network), and improved resistance to jamming compared to traditional GNSS. Furthermore, the team aims to transfer this technology to their project partners for commercialization.]]></description>
      <pubDate>Mon, 02 Mar 2026 19:15:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676001</guid>
    </item>
    <item>
      <title>Context Aware Optimal Information Selection for Reliable, Resilient, Secure, and Efficient
Cooperative Perception</title>
      <link>https://rip.trb.org/View/2676000</link>
      <description><![CDATA[Cooperative perception significantly enhances a vehicle's local field of view by leveraging shared information from nearby vehicles, thus improving overall situational awareness. However, in densely populated environments, cooperative perception can place substantial strain on both communication band-width and computational resources. Such scenarios often result in excessive redundant information, where multiple vehicles repeatedly report the same objects, provide data at unnecessarily high frequencies, or share information irrelevant to the ego vehicle's current context. These issues cumulatively increase computational overhead prior to data fusion and lead to prolonged decision-making times.
Therefore, an effective filtering mechanism is necessary to selectively retain only the most informative objects. Higuchi et al. proposed a value anticipation-based Vehicle-to-Vehicle (V2V) communication approach. In their method, the sender evaluates the potential informational value to receivers and, based on real-time network conditions, either defers or cancels transmissions. This ensures that primarily essential information is disseminated to neighboring vehicles. In another related study, Zhou et al. introduced the Augmented Informative Cooperative Perception (AICP) algorithm, which incorporates both a routing mechanism and message filtering at the receiver side. Their algorithm utilizes an informative-ness measure to assess and select messages, optimizing resource use while ensuring relevant data is received.

While redundant messaging is typically seen as a problem due to its computational demands, it can also provide significant benefits in enhancing security within V2X communications. Specifically, redundancy can enhance detection of malicious behavior through corroborative data from trustworthy vehicles, thereby improving the security of V2X communications. Lie et al. proposed Misbehavior Detection for Collective Perception Services in Vehicular Communications (MISO-V), which leverages redundancy from received V2X messages to validate incoming perception information. Upon verifying a new message against redundant data, the receiver updates the sender’s trust score based on whether the information is classified as benign or potentially malicious. This updated trust score subsequently guides down-stream tasks in determining whether to integrate or discard information provided by that sender.

Balancing redundancy is thus crucial - maintaining an optimal level of redundancy can simultaneously enhance security and sustain computational efficiency. A suitable approach involves dynamically adjusting redundancy based on multiple factors, including source reliability (assessed via trust mechanisms), the planned route of the ego vehicle, prevailing network conditions, and the Age of Information (AoI). This strategy ensures that cooperative perception remains robust, secure, and scalable, supporting accurate and timely decision-making within cooperative vehicle networks.

The aim is to establish a balance between purposeful and efficient redundancy and safety against potential attack scenarios, optimizing the use of communicated data and the reliability of data fusion necessary for downstream tasks such as planning and control. The research team will explore information redundancy, perception inconsistencies, context aware fusion, spoofing and other attack scenarios, and the detection of attack patterns and will employ optimization strategies and reinforcement learning techniques. The focus will include intersection scenarios with varying traffic densities and connectivity levels. In addition to using the VeReMi dataset, the team will explore extensions to more realistic collaborative perception message attach scenarios for evaluation and validation.
]]></description>
      <pubDate>Mon, 02 Mar 2026 19:08:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676000</guid>
    </item>
    <item>
      <title>Mixed Virtual Reality as an Aid in Advancing the Reliability and Robustness of Connected and Automated Vehicle Applications</title>
      <link>https://rip.trb.org/View/2675998</link>
      <description><![CDATA[The rigorous evaluation of safety critical Connected and Automated Vehicle (CAV) scenarios, faces some significant hurdles. Physical testing of scenarios (including edge-cases) presents risk and cost challenges as it is inherently dangerous, cost-prohibitive, and often non-reproducible. Additionally, purely virtual simulation lacks the real-world complexity of communication latency, interference, sensor noise profiles, and realistic representation of physical vehicle dynamics. To address this, the research team proposes using Mixed Reality (MR) co-simulation on a closed-course test track. This powerful alternative merges the real-world fidelity of a physical test platform (live sensor data, vehicle kinematics, real wireless communication channels) with the reproducible complexity of a virtual environment. This enables the safe and rigorous testing of otherwise impractical edge cases. The MR testbed facilitates comprehensive evaluation, addressing critical challenges for example: (1) Robustness and Reliability: It allows for precise injection of sensor degradation faults and failures and enables V2X reliability stress-testing in real-world communication and interference. (2) Cybersecurity and PNT Resilience: The platform safely simulates False Data Injection (FDI) and Denial of Service (DoS) attacks into the V2X communication channel, testing the Vehicle Under Test's Intrusion Detection Systems. Furthermore, it assesses system reliability when Position, Navigation, and Timing (PNT) data is compromised (e.g., via GNSS spoofing), evaluating the system's ability to use V2X data for positioning correction or safe mode transition. This framework leverages the validated utility of Hardware-in-the-Loop (HiL) platforms to rigorously evaluate the real-time performance and resilience of V2X protocols and sensor data fusion architectures on embedded edge computers. The project will leverage the existing highly-instrumented vehicle platform previously developed through the U.S. DOE ARPA-E NEXTCAR Program, which will serve as the Vehicle Under Test (VUT). Collaboration with TRC will be leveraged to facilitate the setup and validation of the MR testbed.]]></description>
      <pubDate>Mon, 02 Mar 2026 18:57:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675998</guid>
    </item>
    <item>
      <title>Improving Traveler Experience Via Alternatives to Roadway/Railway Grade Crossings  </title>
      <link>https://rip.trb.org/View/2646961</link>
      <description><![CDATA[There are more than 240,000 at-grade crossings between railroads and roadways in the U.S. and as the number of freight trains increases, the times of interface and blocked crossings also increases. USDOT reports numerous driver complaints about delays and frequent disruptions, and in some cases, there are delays to emergency vehicles due to excessive numbers of blocked trains. Work is underway to continue documentation and to consider strategies and address the frequent and repeated delays caused by long trains. The most requested remedy is grade separation. Grade separations are extremely expensive, and planning and construction lead times are long, so there is a need to identify other more short-term strategies that will offer travelers and emergency responders options to waiting on the long trains.  

The focus of this research will be Fort Bend County and Harris County, Texas, which include major freight corridors from Port Houston, the 3rd largest container port in the country. Between the two counties, there are at least 11,000 at grade crossings. Specifically, this work will assemble delay time data showing frequency and duration for the identified railroad crossings. The team will conduct literature review and on-line and in-person conversations to determine options and strategies underway by entities (e.g., railroad operators), municipalities, and others to address better traveler information and options to reduce and avoid delay time. Potential options include cameras noting delays and following with notifications to emergency services proximate to locations with frequent delays. The study team will examine whether this information distribution could be expanded to additional users. An additional option to be examined is message signs alerting travelers to blocked crossings in time to adjust their travel route. The expected research outcome is to provide an option to grade separations that will reduce delay time for travelers caused by blocked train crossings. ]]></description>
      <pubDate>Tue, 06 Jan 2026 17:10:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646961</guid>
    </item>
    <item>
      <title>Connected Vehicle Data</title>
      <link>https://rip.trb.org/View/2640696</link>
      <description><![CDATA[The Compass IoT company is performing a pilot project with the Missouri Department of Transportation (MoDOT) to provide data, both historical and over a four-month period, for the research team to build a proof of concept for the member states in the Original Equipment Manufacturers (OEM) Pooled Fund. This will allow the research team to show the member states the benefits that can be realized with this information. The data will be focused on work zone information, near miss data, and winter weather events.]]></description>
      <pubDate>Tue, 16 Dec 2025 09:56:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640696</guid>
    </item>
    <item>
      <title>Leveraging Emerging Technologies to Support Accessible Communication in Transit for Riders Who Are Deaf/Hard-of-Hearing</title>
      <link>https://rip.trb.org/View/2636144</link>
      <description><![CDATA[Public transportation systems increasingly rely on real-time information, digital alerts, and audible public announcements to support safe and reliable travel. Yet riders who are deaf or hard of hearing continue to face significant communication barriers across bus, rail, and station environments. These barriers affect not only convenience but also personal safety, particularly during emergencies or service disruptions when timely, actionable information is essential. Although the U.S. population of older adults is growing—and age-related hearing loss is becoming more prevalent—current transit communication systems have not kept pace with the range of hearing-related, cognitive, and hidden disabilities that affect navigation and comprehension in transit settings.

Little research exists that specifically addresses communication accessibility for deaf and hard-of-hearing riders. Most existing disability research and accessibility investments center on physical or mobility impairments, and current transportation data systems do not consistently capture the experiences or needs of riders with hearing loss or communication-related disabilities. As a result, important gaps remain in understanding how these riders experience the system, how they respond to information, and how emerging technologies could improve communication and reduce barriers. These gaps affect urban, suburban, rural, and small agencies alike—many of whom lack the technical staff or funding to test new accessibility solutions.

At the same time, rapid advances in communication technology, wayfinding tools, and real-time interpretation services offer promising opportunities. For example, the New York MTA’s pilot deployment of instant, on-demand ASL interpretation via QR code access demonstrates the potential of low-cost, scalable solutions to enhance communication, increase trust, and improve rider experience. Other agencies may be testing similar tools, but documentation is sparse and lessons are not widely shared. A clearer understanding of what technologies are available, how they perform, and how they might be deployed in systems of varying sizes is essential to modernizing access for riders who are deaf/hard of hearing.

This topic also intersects with other disabilities—such as cognitive disabilities, sensory processing conditions, or neurodivergence—where improved communication design could benefit multiple rider populations. While some TCRP research examine broader disability access questions, there is a compelling need for a focused investigation into hearing-related communication needs within transit, while also identifying where alignment or shared standards could support cross-disability improvements.]]></description>
      <pubDate>Mon, 08 Dec 2025 19:52:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636144</guid>
    </item>
    <item>
      <title>Rural Omnichannel Healthcare: A Demand-Centric Approach in Transportation System Design
</title>
      <link>https://rip.trb.org/View/2625868</link>
      <description><![CDATA[Entering its second year, the Rural Omnichannel Healthcare Project aims to enhance rural healthcare equity and accessibility by refining the deployment strategy of Telehealth Kiosks/Booths (TKBs). Grounded in Year 1's evidence, this phase deepens the understanding of the complex underlying demand dynamics for the use of TKBs. In particular, Year 2 aims to better understand both the currently unmet healthcare needs in rural areas and how these needs drive the demand for TKBs. Additionally, it investigates how the use of TKBs correlates with the demand for services at existing healthcare facilities. This phase also examines the geographical factors that influence the adoption and utilization of TKBs, exploring how location affects the effectiveness and popularity of these kiosks in enhancing rural healthcare access. Through rigorous empirical research, we aim to refine distance decay functions that describe how these factors impact healthcare access. These insights will inform the enhancement of our mathematical models, guiding the strategic deployment of TKBs to address the unmet needs and preferences of rural communities effectively. The project is set to offer innovative solutions that realistically improve healthcare access and quality, addressing rural healthcare disparities with precision. Through this comprehensive approach, Year 2 of the Rural Omnichannel Healthcare Project stands to significantly advance our understanding of the complexities of healthcare access in rural areas, bridging critical gaps and fostering a more equitable healthcare landscape.
]]></description>
      <pubDate>Tue, 18 Nov 2025 13:56:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625868</guid>
    </item>
    <item>
      <title>Evaluating V2X Network Performance and Enhancing Safety and Security in Sensor Data Sharing for Connected and Automated Driving
</title>
      <link>https://rip.trb.org/View/2625308</link>
      <description><![CDATA[This project will investigate the sensor data sharing mechanism with C-V2X and networked vehicle-to-everything (V2X) communication technology in terms of safety, cybersecurity, and network performance with current bandwidth allocations. The research team will (1) develop a comprehensive evaluation framework of the V2X network performance (e.g., latency, throughput) under real – world complexities; (2) develop a data fusion model that fuses Sensor Data Sharing Messages (SDSMs) from multiple sources considering uncertainties in real-world V2X communication networks, errors in sensor-based object detection; and (3) develop a misbehavior detection model that can detect anomaly in SDSMs and evaluate the trustworthiness of the message within a short time.]]></description>
      <pubDate>Thu, 13 Nov 2025 15:28:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625308</guid>
    </item>
    <item>
      <title>A Low-cost Roadside Device System for Cooperative Automated Driving
Phase 2: Work Zone Safety Applications</title>
      <link>https://rip.trb.org/View/2625304</link>
      <description><![CDATA[Despite the significant progress in automated driving, technical challenges still exist, especially for complex Operational Design Domains (ODDs). A low-cost roadside device system, the Connected Reference Marker (CRM) System, was developed to facilitate connected and automated vehicle (CAV) localization. A project was funded by Center for Connected and Automated Transportation (CCAT) FY2024 to build a prototype system and evaluate its performance. The initial results show that the CRM System is capable of maintaining low positional errors and, therefore, has the potential to be a reliable solution for vehicle localization for cooperative driving automation (CDA). In this project phase, the research team aims to develop a deployable work zone safety system built upon the prototype CRM system from the previous project phase. Specifically, the work zone safety system can track and predict the trajectories of individual vehicles, estimate the vehicle’s collision risk, and send customized warning messages if the risk is elevated. This work zone safety system will also incorporate modules from the CARMA CDA platform to ensure system interoperability.]]></description>
      <pubDate>Thu, 13 Nov 2025 14:51:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625304</guid>
    </item>
    <item>
      <title>Urban Network Speed Optimization for Connected Automated Vehicles: Development and Testing</title>
      <link>https://rip.trb.org/View/2606410</link>
      <description><![CDATA[This research develops and evaluates optimal speed control strategies for Connected and Automated Vehicles (CAVs) at the network level, addressing critical gaps in existing research by incorporating multiple powertrain technologies including internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and hydrogen fuel cell vehicles (HFCVs). The study addresses real-world challenges such as communication delays, data transmission errors, and vehicle actuation complexities that are often overlooked in idealized research conditions. Using the INTEGRATION microscopic traffic simulation software, the research will implement advanced communication modules for vehicle-to-vehicle and vehicle-to-infrastructure interactions alongside vehicle speed control modules. The methodology involves formulating speed trajectory optimization as a constrained problem incorporating vehicle dynamics, fuel consumption models for different powertrains, and signal phase and timing data. Dynamic programming methods including A-star search algorithms will ensure real-time computational efficiency. The research includes extensive testing across varied traffic networks with different congestion levels and CAV market penetration rates, culminating in a scalable framework for generalizing results to large-scale networks including the entire U.S. roadway system through collaboration with Saudi Aramco.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:21:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606410</guid>
    </item>
    <item>
      <title>Roadmap for Innovative Application of GDOT's Digital Information Assets in Support of Developing the Digital Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2596466</link>
      <description><![CDATA[
This project proposes the development of a comprehensive roadmap for digital infrastructure in transportation which will provide the necessary guidance and insights to support the implementation of digital technologies and drive innovation in the transportation sector. ]]></description>
      <pubDate>Fri, 05 Sep 2025 13:03:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596466</guid>
    </item>
    <item>
      <title>Instrumentation And Monitoring For G-Beam/Stillwater Avenue Bridge Replacement</title>
      <link>https://rip.trb.org/View/2582413</link>
      <description><![CDATA[In the proposed project, the research team plans to deploy an extensive instrumentation and communication system that will be embedded in the G-Beam girders proposed for the Stillwater Avenue bridge in Orono/Old Town.  Some of the details of the specific monitoring plan will need to be deferred to coincide with girder design.
The study will include the following. First, an array of fiber optic cabling will be installed along the longitudinal beam axis at different locations relative to the neutral axis.  Each cable will include discrete sensors at different locations along the beam axis to capture strain at those points.  Second, an array of accelerometers will be located it key locations in order to capture frequencies and modes of vibration during service.  Both the accelerometers and the fiber optic system will be connected to a communications network that both collects data from the sensor array and broadcasts the data over a wireless network to a server at University of Maine (UMaine).  Depending on collection rates, the data will either be transmitted over a conventional 5G cellular network, or more likely via a closed network that sends the data through a series of discrete repeaters in between the bridge site and the server.  Third, the team proposes a system of digital cameras that will be used both to trigger the acquisition and transmission system, but also through machine vision, be able to identify the vehicle type (e.g. number of axles.)  Once triggered, the array of strain gages and accelerometers, will preprocess data and send to the UMaine server.  In this way, resulting strain and vibration data can be tied to load types.  Fourth, a weather station will monitor current temperature, sunlight, and relative humidity data to complement the acquired structural data.  Depending on design issues, additional on-site sensors can monitor water level, ice status, and other environmental conditions that may be relevant. Finally, we will conduct diagnostic live load tests on the completed structure immediately before it is opened to traffic and approximately one year after its completion]]></description>
      <pubDate>Thu, 31 Jul 2025 14:23:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2582413</guid>
    </item>
    <item>
      <title>CyberTrans-AI Development of Transportation Cybersecurity Certificate Program for Transportation</title>
      <link>https://rip.trb.org/View/2559305</link>
      <description><![CDATA[Transportation systems have evolved in the last decades and the modern system heavily relies on digital technologies from traffic signals to communication portals. Ensuring the security of these systems is imperative to safeguard public safety, protect sensitive data, and maintain the smooth operation of transportation services. The failure to protect the security of transportation networks could lead to disruptions in traffic flow, potential crashes, and even threats to national security. The purpose of this project is to develop a certificate program in transportation cybersecurity for practitioners with the necessary skills and knowledge to effectively protect transportation systems from cyber threats. There are five major objectives listed as follows: (1) Understanding Cybersecurity Fundamentals: the proposed certificated program is to provide participants with a fundamental understanding of cybersecurity concepts relevant to transportation system engineering. (2) Knowing Industry-Practice Knowledges: the proposed program is to offer specialized cybersecurity issues on intelligent transportation system (ITS), such as vehicle-to-vehicle (V2V) communications, vehicle-to-infrastructure (V2I) communications, to ensure and protect the transportation infrastructure and data. (3) Conducting Risk Assessment and Management: the proposed certificated program is to train practitioners to identify and assess cybersecurity risks within transportation systems and develop risk mitigation strategies to the unique characteristics of transportation infrastructure. (4) Increasing Security Awareness and Training: the proposed certificated program is to promote a culture of cybersecurity awareness among transportation practitioners, to identify potential threats, and thus to follow best practices to mitigate risks. (5) Providing Continuous Professional Development: the proposed certificated program is to support ongoing education and professional development for transportation practitioners in cybersecurity, providing opportunities for further learning, skill enhancement, and staying abreast of emerging threats and technologies.

By achieving these objectives, a transportation cybersecurity certificate program can help build a workforce of knowledgeable and skilled practitioners capable of effectively safeguarding transportation infrastructure and ensuring the safety, security, and reliability of transportation systems.]]></description>
      <pubDate>Thu, 29 May 2025 21:37:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2559305</guid>
    </item>
    <item>
      <title>Advancing Cyber Resilience of Transportation System Management and Operation Programs </title>
      <link>https://rip.trb.org/View/2558388</link>
      <description><![CDATA[Transportation systems and technology across the United States are becoming increasingly interconnected, spanning local, state, and national borders. This integration supports safe and efficient transportation networks but introduces cybersecurity challenges. Many transportation system management and operation (TSMO) strategies rely on real-time data sharing, integrated traffic management centers (TMC), and interoperable technologies, all of which create new threat vectors for cyber incidents. The potential for severe injuries and fatalities, data loss, service disruptions, and other failures necessitate a more resilient approach to cybersecurity, shifting the focus from merely preventing attacks to ensuring the ability to recover and maintain essential functions during an incident.

Research is needed to develop coordinated, cross-jurisdictional frameworks and methodologies to assess, manage, and enhance cyber resilience within integrated transportation systems. 

OBJECTIVE: The objective of this research is to develop a guide for incorporating cyber resiliency into state and local TSMO programs.]]></description>
      <pubDate>Wed, 28 May 2025 13:35:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558388</guid>
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