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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>Evaluating User Acceptance and Effectiveness of Cognitive Measurements and Intervention for Shared Autonomy</title>
      <link>https://rip.trb.org/View/2690985</link>
      <description><![CDATA[Vehicles equipped with automated driving systems (ADS) have become more widespread in the trucking industry. On the one hand, ADS are known to be susceptible to occasional errors in environment perception, but on the other, ADS can demonstrate safer and more efficient behavior in situations where the driver is cognitively impaired. Shared autonomy systems thus have the potential to combine the best of both paradigms. Some early instantiations of such shared autonomy ADS use measurements of the human cognitive state to perform interventions, either in the form of sensory feedback, and/or by actively taking over the driving task. The main objective of this project is to address the gap in research on the effectiveness and acceptance of cognition-aware shared-autonomy methods with respect to the overall system safety. Qualitative data will be collected through semi-structured interviews with truck drivers and systematically encoded into operational design requirements and hypothesis-driven performance metrics that directly inform the design of cognition-aware shared autonomy systems. The research team will perform a driving simulator study that enables a controlled evaluation of adaptive cognition-aware intervention policies, including rule-based and data-driven triggering mechanisms that dynamically adjust system behavior based on real-time cognitive interventions. Researchers will study how specific design choices in cognition-aware intervention policies (e.g., trigger thresholds, modality selection, and intervention persistence) influence system acceptance, misuse, and compliance, enabling actionable design guidance beyond descriptive acceptance analysis. The datasets collected inform policy on the use of ADS in both drayage and long-haul trucking. This project will develop a methodology for designing and evaluating cognition-aware behavioral interventions that couple driver monitoring outputs with explicit control and feedback policies, enabling reproducible comparison across intervention strategies and deployment contexts.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:23:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2690985</guid>
    </item>
    <item>
      <title>From perception to preparedness: Virtual reality simulations of flooded roadways in coastal communities (UPRM)</title>
      <link>https://rip.trb.org/View/2663232</link>
      <description><![CDATA[Project Description: Coastal flooding regularly disrupts transportation networks, damages infrastructure, and limits access to essential services through storm surge, tidal inundation, and extreme precipitation. These events result in vehicle failures, stranded motorists, pavement damage, and delays in emergency response and daily mobility. Communities with aging infrastructure, limited resources, or constrained evacuation options face heightened vulnerability. The total annual economic burden of flooding in the U.S. ranges from $179.8 to $496.0 billion (US Congress JEC, 2024). In addition, the National Weather Service and the Centers for Disease Control and Prevention report that over half of all flood-related drownings occur when a vehicle is driven into hazardous floodwater. Understanding how drivers decide whether to cross or avoid flooded roads is essential for designing warnings, signage, and roadway treatments that reduce risky behavior and improve outcomes. The use of virtual reality (VR) and immersive 360° scenarios can let residents experience rising water, blocked routes, and mitigation measures without real-world risk, increasing realism and emotional stimulus. Scenario-based VR visualizations can help translate technical flood data into intuitive, actionable information for nontechnical audiences. Local resilience depends not only on infrastructure but also on household-level preparedness and decision-making, including how individuals interpret alerts and respond to flood risks. Chacon-Hurtado (2013) advocates for embedding community preferences and preparedness considerations directly into transportation decision-making frameworks, arguing that investments should be evaluated not only on engineering metrics but also on how they advance local capacity to act under hazard conditions. 
This project will employ virtual reality (VR) simulations of flooded highways that are being developed by the University of Puerto Rico at Mayagüez (UPRM) team to study human behavior and perception in flood scenarios, with three main goals: (1) Enhance public understanding of flood risks by immersing participants in realistic coastal flooding scenarios, (2) Evaluate driver decision-making when encountering flooded roadways, analyzing how variables such as water depth, roadway conditions, and alert systems (e.g., signage, ADAS, in-vehicle alerts) influence choices, and 
(3) Assess community preferences for flood mitigation strategies, using immersive experiences to gather feedback on potential interventions. 
Two VR approaches will be implemented. The first involves a driver simulator with 24–36 participants navigating flooded roadway scenarios to assess behavioral responses under controlled conditions. The second approach will engage community members from coastal municipalities like Isabela, Puerto Rico, in immersive 360° simulations to explore perceptions of flood risk and mitigation strategies. Pre- and post-tests will measure changes in knowledge, perception, and behavioral intent. Insights from both simulations will inform the design of more effective alert systems and flood mitigation strategies that reflect community preferences and improve safety. The findings will support transportation and emergency planning professionals in developing human-centered solutions for flood-prone coastal areas.

]]></description>
      <pubDate>Sat, 31 Jan 2026 12:03:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663232</guid>
    </item>
    <item>
      <title>Exploring AI-Driven Approaches to Quantify and Mitigate Driver Distraction</title>
      <link>https://rip.trb.org/View/2655701</link>
      <description><![CDATA[As automated systems and assistive driver features continue to advance, driver distraction is increasing in both manual and semi-autonomous modes. Automated vehicles (AVs) offer promising technology solutions to reduce distraction risk. However, automation can create out-of-the-loop periods, encouraging non-driving tasks and masking gradual disengagement. Current AVs are semi-autonomous, so drivers must be ready to retake control for issues like missing lane markers. The takeover process is cognitively and physically demanding, unfolding within seconds, which is especially challenging when drivers are distracted. Traditionally, distractions in vehicles have mainly been due to phone use or infotainment systems. New technologies have expanded the sources of distraction. These distractions include visual distractions where drivers’ eyes are diverted from the road (e.g., using a GPS), manual distractions where drivers remove their hands from the wheel (e.g., using a phone or eating while driving), and cognitive distractions where a driver’s mind is not focused on driving (e.g., mind wandering). Emotions and fatigue that pull attention from driving also reduce engagement. Social pressures and smartphone habits further contribute to distraction. This emphasizes the need to broaden the definition of distraction to include driver engagement levels to better reflect real-time mental states and driving performance.
Given the many types of distractions, there is a need to systematically quantify their effects using both driving and physiological measures acquired by various sensors under a more universal paradigm (e.g., engagement level). With the rapid advancements in artificial intelligence (AI), particularly in the domain of deep learning, sophisticated multimodal models have emerged as powerful tools for integrating diverse data sources. These models offer an unparalleled ability to combine acquired features from various sensors into a cohesive understanding of driver states. Leveraging such techniques enables us to capture nuanced indicators of distraction, which can ultimately enhance the system’s ability to predict and respond to varying levels of engagement. The research team proposes using federated learning with multimodal sensor fusion, where each sensor model is trained locally on-device to maintain privacy and adapt to specific driving conditions. Federated learning enables these models to share knowledge without centralizing data, creating a global model that can recognize diverse distractions across different driving contexts.
Therefore, the goals of this project have two parts: (1) Identify and quantify the effects of traditional and emerging distractions, and (2) Build an AI-driven framework that categorizes and quantifies all types of driver distractions based on multisensory data. Two phases of studies will be conducted to achieve the two goals, respectively. Phase I will identify and quantify both traditional and emerging forms of driver distraction. Specifically, the team will first conduct a national survey to better assess sentiments, attitudes, and key themes regarding driver distraction as well as correlations and trends among demographics, distraction types, and self-reported behaviors. With the knowledge gained, the team will then quantify the effects of distractions on driving performance and
physiological measurements. The team will categorize types of distractions based on established classifications such as visual, manual, and cognitive distractions, as well as emerging types identified in the survey. Then the team will conduct an in-lab experiment using a high-resolution driving simulator. Participants’ driving and physiological measures. All data collected herein will then be used in Phase II, the development of an AI-driven framework.
Phase II will develop an AI-driven framework to categorize distraction types using
multisensory data. The team will integrate three sensors, eye tracking, a depth sensor, and face video, to enable a complete view of distraction. Multimodal models will fuse these signals to improve accuracy and robustness. The team will aggregate insights from each modality to achieve a more reliable categorization of distraction types. Federated learning will build a global model while keeping data on the device, which supports privacy and adaptation to different platforms and roads. The global model will serve as the core engine in Phase II, classifying distraction types based on combined sensory inputs. This layered AI approach will strengthen the understanding of driver distraction and support real-time interventions.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:14:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655701</guid>
    </item>
    <item>
      <title>Impact of Double Centerline Rumble Strips (CLRS) on Driver Behavior</title>
      <link>https://rip.trb.org/View/2655579</link>
      <description><![CDATA[According to the NCHRP report 641 (2005) Centerline rumble strips (CLRS) effectively reduce head-on, sideswipe, and crossing-the-centerline crashes on two-lane rural highways by 44-46%. Benefits of using CLRS include alerting the inattentive drivers, offering visibility of lane markings, and providing a low-cost treatment to avoid crashes. Previous research suggests that a separation of 6” between the double rumble strips along the center of the road can prevent joint deterioration. In addition, the gap may alert the driver to allow sufficient time to correct lane encroachment. However, the effectiveness of such a gap between double CLRS to enforce safety has not been investigated. Furthermore, other factors may affect the effectiveness of double CLRS on surrogate safety measures, such as traffic density, visibility conditions, geometric design (crest curves), presence of shoulders (paved/unpaved), passing zones, etc. Therefore, the objective of this project is to investigate the impact of different double CLRS installation patterns on the behavior and safety of drivers. To achieve this objective, a driving simulator-based study will be designed to investigate the effect of the pattern of double CLRS on driving behavior using various surrogate safety measures such as lane position, speed, post lane encroachment time, and time-to-collision (TTC). These measures will be collected across segments installed with and without the double CLRS in a control condition without any distraction/inattentiveness and compared with the test conditions including a distracted driver using a driving simulator experiment. Based on the study findings, the final report will include recommendations for the systematic installation of double CLRS for safety enhancement.]]></description>
      <pubDate>Thu, 15 Jan 2026 12:52:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655579</guid>
    </item>
    <item>
      <title>Deskilling or Redefining? Drivers’ Hazard Anticipation with Automation</title>
      <link>https://rip.trb.org/View/2643018</link>
      <description><![CDATA[Hazard anticipation is a critical skill for crash avoidance, requiring drivers to detect, predict, and respond to potential roadway threats. As Advanced Driver Assistance Systems (ADAS) become more common, drivers increasingly shift from active vehicle control to supervisory roles, which may change visual scanning patterns, situational awareness, and response strategies. These changes are not yet fully understood and may have important safety implications.

This project investigates how automation affects hazard anticipation by examining driver gaze behavior, responses to traditional roadway hazards, and reactions to system-related cues in ADAS-equipped vehicles. The research integrates secondary data analysis, development of a hazard anticipation taxonomy, and experimental testing using a high-fidelity driving simulator. Results will be used to identify automation-related changes in hazard anticipation and to develop targeted training approaches aimed at maintaining effective driver oversight. Findings from this work will support safer human interaction with automated vehicle technologies as their use continues to expand.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:13:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643018</guid>
    </item>
    <item>
      <title>Improving Teen Driver Safety, Evaluating Educational Interventions to Reduce Overreliance on Advanced Driver Assistance Systems</title>
      <link>https://rip.trb.org/View/2640187</link>
      <description><![CDATA[Advanced Driver Assistance Systems (ADAS) can provide important safety benefits, but they may also encourage drivers to respond more slowly when the system turns control back to the driver. Teen drivers, who are still developing driving skills and awareness, may be more likely to misunderstand system limits or become inattentive when ADAS features are active. This project investigates whether targeted educational programs can reduce this type of overreliance and improve driver performance when automation disengages. The study will compare a traditional classroom style training module with an interactive simulator based program to determine how each affects driver attention and reaction time.

A group of licensed teen drivers from Connecticut and Massachusetts will complete simulator sessions that include planned ADAS disengagements under both distracted and non distracted conditions. The simulators will collect data on braking, steering, lane position, reaction time, and gaze behavior. The results will show how training influences driver behavior during sudden transitions from automated to manual control. The findings will inform improvements to teen driver education materials and provide guidance for programs that teach responsible and informed use of ADAS technologies.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:41:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640187</guid>
    </item>
    <item>
      <title>ELDER-3: Empowering Lifelong Driving Experiences with SAE Level 3 Automation
</title>
      <link>https://rip.trb.org/View/2628204</link>
      <description><![CDATA[Automated vehicles (AVs) are heralded as the future of transportation. The Society of Automotive Engineers categorizes six levels of AV, with levels 1 and 2 already in operation, and level 3 undergoing mass production testing in North America. Level 3 marks a significant advancement, as drivers can engage in non-driving related tasks (NDRT), but must be prepared to take over control of the vehicle at all times. However, level 3 AVs pose significant cognitive and motor demands during take-over requests (TOR) in older drivers with intact cognition, suggesting that take-over maneuvers may be even more challenging for older adults with cognitive impairment (CI).
This study aims to determine the impact of CI on take-over performance in level 3 AVs. Participants (30 older drivers with intact cognition, and 30 older drivers with CI) will engage in level 3 AV driving using a high-fidelity driving simulator while their eyes are tracked for attention and cognitive workload. During the drive, the TOR will require participants to quickly transition from an NDRT to taking over control of the vehicle. Additionally, participants will complete a clinical battery of cognitive, visual, and motor tests.
We hypothesize that older adults with CI will exhibit: (1) slower response to TOR; (2) reduced attention (glances on screen) and increased drowsiness (eyelid closure) before TOR; and (3) heightened cognitive workload (pupillary response) during and after the TOR. In hypothesis (4), we expect that a combination of clinical tests including reaction time, processing speed, and working will predict take-over performance.
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:20:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2628204</guid>
    </item>
    <item>
      <title>Development of a VR Transportation Simulator</title>
      <link>https://rip.trb.org/View/2625582</link>
      <description><![CDATA[As cities strive to promote sustainable and active transportation, bicycles are becoming an increasingly popular mode of travel. However, cyclists remain among the most vulnerable road users due to unsafe road conditions. According to the National Highway Traffic Safety Administration (NHTSA, 2023), bicyclist fatalities in the U.S. have been increasing, with 1,105 reported in 2022, the highest annual total since 1975. These alarming statistics emphasize the urgency of improving cycling safety through informed policy decisions.  The development of VR-based bicycle simulators has gained traction in recent years, with various research institutions integrating immersive environments, physiological monitoring, and behavioral tracking into their systems. Studies such as the ORCLSim project at the University of Virginia (Guo et al., 2022) and research by Nazemi et al. (2021) have demonstrated the potential of VR-based platforms in assessing cycling safety. Additionally, Friel et al. (2023) explored how cyclists perceive safety at intersections and roundabouts using a bicycle simulator, highlighting the effectiveness of immersive simulations in transportation research. However, many existing bicycle simulators vary in hardware configurations, software frameworks, and data integration capabilities, making it difficult to establish a standardized platform for transportation research. Moreover, while several studies have used VR bicycle simulators for analyzing cyclist behavior, the process of building a fully functional, data-driven simulator remains an open challenge.  This project focuses on the development of a VR-based bicycle simulator that integrates real-time performance and physiological data collection. The primary goal is to design, build, and calibrate a simulator that can be used for future transportation safety research. This includes selecting the appropriate hardware and software components, ensuring compatibility between different system elements, and validating data acquisition methods. A critical aspect of this study is identifying what types of data can be collected and how they can be effectively integrated into the simulator’s framework. By addressing these technical and methodological challenges, this project aims to establish a foundation for future research using VR-based bicycle simulators.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:38:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625582</guid>
    </item>
    <item>
      <title>Assessing Quick Builds and Safe Streets for Non-Motorized Safety Using Simulations and Portable Sensing Technology</title>
      <link>https://rip.trb.org/View/2606402</link>
      <description><![CDATA[This research establishes a comprehensive evaluation framework for Quick Build interventions using portable LiDAR technology, driving simulation, and field data to quantify safety impacts for non-motorized road users including pedestrians and cyclists. Building on Phase 1 simulation-based foundations, the study addresses the challenge of limited long-term effectiveness data for quick-build street treatments such as curb extensions, protected bike lanes, and temporary traffic calming features. The methodology combines portable detection systems including LiDAR sensors, edge computing, and 5G connectivity to capture high-resolution vehicle and non-motorized transport trajectories, speeds, conflict points, and crossing patterns before and after intervention implementation. UC Win Roads driving simulator environments will replicate selected corridors to analyze road user behavior and interactions under controlled conditions, while survey questionnaires assess community perceptions of safety improvements. The research develops multimodal safety performance metrics tailored to temporary installations including Post Encroachment Time, Time to Collision, and crash modification factors for non-motorized transport modes. The study produces Standard Operating Procedures enabling Maryland Department of Transportation staff to implement evaluation methods independently for future Quick Build projects, supporting evidence-based decision-making for permanent infrastructure investments.]]></description>
      <pubDate>Thu, 02 Oct 2025 14:57:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606402</guid>
    </item>
    <item>
      <title>SPR-5009: Phase II of SPR-4628: Validation of Innovative Work Zone Countermeasures by Field Tests</title>
      <link>https://rip.trb.org/View/2601508</link>
      <description><![CDATA[This project aims to validate the effectiveness of countermeasures identified in Phase I (SPR-4628) using field tests in INDOT work zones. The field tests include two parts: (1) apply sensors in work zones with geofenced surveys and (2) perform real-world driving experiments with post-surveys. Project deliverables include identified work zones and factors for field tests, effectiveness of countermeasures in INDOT work zones, differences and consistencies between field tests and driving simulation experiments, and the final report. ]]></description>
      <pubDate>Thu, 18 Sep 2025 16:06:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601508</guid>
    </item>
    <item>
      <title>SPR-5023: Understanding and Shaping Driver Behavior and Public Perception at Reduced Conflict Intersections (RCls)</title>
      <link>https://rip.trb.org/View/2577104</link>
      <description><![CDATA[This project addresses public acceptance and driver safety at Reduced Conflict Intersections (RCIs). Through statewide driver surveys and field observations, the research identifies specific driver challenges and perceptions at RCIs. Utilizing Purdue’s portable driving simulator, the project evaluates design interventions and signage improvements to clarify driver navigation. Final deliverables include behavioral insights, validated simulation scenarios for RCIs, and educational outreach tools. ]]></description>
      <pubDate>Thu, 17 Jul 2025 16:00:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2577104</guid>
    </item>
    <item>
      <title>SPR-5008: Strategic Expansion of Driving Simulators in INDOT Driver Training Programs</title>
      <link>https://rip.trb.org/View/2577100</link>
      <description><![CDATA[This project aims to enhance the effectiveness of simulator-based training and contribute to transportation safety and efficiency in Indiana. This study will refine simulator scenario selections tailored to Indiana’s unique operational conditions and drivers’ skill levels, develop instructional tutorials for the consistent and effective use of driving simulators, assess the impacts of simulator-based training, identify and quantify resources saved through driving simulator programs, and develop efficient strategies for utilizing simulator resources statewide. ]]></description>
      <pubDate>Thu, 17 Jul 2025 15:33:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2577100</guid>
    </item>
    <item>
      <title>Statewide augmented information in the driver environment study</title>
      <link>https://rip.trb.org/View/2570738</link>
      <description><![CDATA[The purpose of this project is to increase lifelong independence and safe vehicle operation by improving access to environmental information while driving, particularly among older adults and people with visual impairments. By simulating a new framework for computer-vision assisted augmented reality (CVAR) in the driving environment, the research team intends to study how augmenting roadway elements (e.g., lines on the road, lane markers, and obstructions) improves overall operational performance. It is predicted that the universal design approach used in this work will not only substantially benefit older adult drivers but will also benefit all drivers. This is because the University of Maine (UMaine) CVAR solution can be used to improve access to roadway elements during situations of reduced visibility (e.g., at night or during inclement weather) while also highlighting an eventual suite of potential hazards (e.g., downed limbs, wildlife, and pedestrians).  

The work will expand UMaine's Virtual Environments and Multimodal Interaction Laboratory (VEMI Lab)’s current autonomous vehicle simulator (MOISIN: Multimodal Omnidirectional Immersive Simulator for Inclusive Navigation) to include a manual driving operational mode. Related software will also be developed to simulate new inclusive CVAR approaches that combine multisensory feedback with augmented visual information to expand access to a wide range of potential drivers. The resulting UIs will be tested in a series of user studies examining the impact on driving performance across various driving scenarios.]]></description>
      <pubDate>Wed, 02 Jul 2025 13:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2570738</guid>
    </item>
    <item>
      <title>Graphical Changeable Message Signs: Strategies, Impacts, and Best Practices</title>
      <link>https://rip.trb.org/View/2563027</link>
      <description><![CDATA[The Virginia Department of Transportation (VDOT) is exploring the potential of graphical changeable message signs (CMS) to improve driver comprehension and response to roadway information. While traditional CMS rely on text-only messages, advances in display technology now allow the inclusion of graphic symbols and photographic elements. However, limited research exists on how Virginia drivers interpret these graphical formats and whether they offer measurable benefits over standard text-based messages.

This study will evaluate the effectiveness of graphical CMS designs in supporting driver comprehension and safe decision-making through a combination of carefully designed surveys and driving simulator experiments. An early focus of the project will be the evaluation of CMS messages developed for the Targeted Over-Height Vehicle Warning CMS system at the Hampton Roads Bridge-Tunnel (HRBT), which includes the novel feature of displaying photo of the detected over-height vehicles. This effort supports VDOT’s Request to Experiment submitted to the Federal Highway Administration (FHWA).

The study will use interstate incident management as its primary case study. Results will provide VDOT with actionable guidance on the design and deployment of graphical CMS, support potential updates to statewide messaging policies, and inform future pilot projects or experimentation initiatives.   
]]></description>
      <pubDate>Tue, 10 Jun 2025 09:49:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563027</guid>
    </item>
    <item>
      <title>Building a Calibrated Microsimulation Platform for Modeling System-wide Cyberattack Consequence</title>
      <link>https://rip.trb.org/View/2548668</link>
      <description><![CDATA[In today’s increasingly interconnected transportation landscape, cyberattacks pose substantial risks to individual vehicles and
broader traffic systems. This project introduces a comprehensive framework that models vehicle-human complex behaviors under
cyberattacks by incorporating perception-reaction time (PRT), situationally dependent braking intensity, and adaptive velocity
adjustments influenced by intersection geometry and signal phases. Central to the framework is a trajectory reconstruction model
(CTRM) that merges vehicle kinematics, driver Perception-Identification-Emotion-Volition (PIEV) processes, and cyber-psychological
perspectives into a unified system.
Validation was conducted using data from the miniSim driving simulator under designed spoofed Red-Light Countdown (RLCD)
conditions, allowing comparison with the physics-based Intelligent Driver Model (IDM) and the machine learning-based Long Short-Term Memory (LSTM) techniques. Results show that CTRM more accurately replicates real-world vehicle trajectories under both
normal and cyberattack scenarios, demonstrating superior explainability and fidelity. Factors such as driver age, experience, and
trust in connected vehicle technology influence responses to cyber intrusion events. By embedding our model in SUMO, the
simulation results indicate that well-coordinated signal timing can significantly mitigate cyber-induced disruptions.]]></description>
      <pubDate>Wed, 30 Apr 2025 16:09:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548668</guid>
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