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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>Advanced Sustainable Transportation Workforce Development Initiative in California’s Inland Empire</title>
      <link>https://rip.trb.org/View/2692313</link>
      <description><![CDATA[Spurred by significant government investments and regulatory landscape, advanced sustainable transportation (connected, automated, energy-efficient, and shared vehicles) and its supporting infrastructure is well underway in Inland Southern California. Not only are advanced vehicles becoming common among California’s Inland Empire residents, but the region is at the heart of medium- and heavy-duty vehicle programs associated with goods movement. As a result, many advanced transportation and infrastructure manufacturers are now locating to the Inland Empire due to its favorable economic landscape. What’s lacking is an advanced sustainable transportation workforce in the region that is needed for: (1) manufacturing, maintaining, repairing advanced vehicles; (2) setting up, deploying, and maintaining advanced vehicle infrastructure; and (3) responding to incidents associated with advanced vehicles and their supporting infrastructure. The project team will launch a comprehensive Advanced Sustainable Transportation Workforce Development Initiative for California’s Inland Empire, pulling together a variety of existing educational programs, developing these programs further into a cohesive vehicle/infrastructure training program, and creating a coalition of local manufacturers in this advanced vehicle space. This initiative seeks to position the Inland Empire as a national leader in advanced vehicle manufacturing and adoption. This bold vision positions the region as a model for sustainable growth, advancing the region’s goals while uplifting communities. The key goals of the initiative include: (1) Integrating workforce development, industry needs, and policy goals into a cohesive, impactful strategy. This project will deliver comprehensive training programs in advanced vehicle technology, associated infrastructure, and managing vehicle incidents across a wide range of technologies (light-, medium-, and heavy-duty vehicles, buses, trucks, rail, aircraft). (2) Creating high-quality jobs in the region. The team’s plan is to fill the expected thousands of advanced transportation jobs with locally sourced talent, emphasizing pathways that promote societal advancement.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:12:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692313</guid>
    </item>
    <item>
      <title>SMARTER Center CAV Testbed Digital Twin</title>
      <link>https://rip.trb.org/View/2676080</link>
      <description><![CDATA[This project advances transportation safety and mobility by developing a high-fidelity digital twin of the SMARTER Center’s Connected and Automated Vehicle (CAV) testbed at Morgan State University. The proposed platform synchronizes key infrastructure states, sensor observations, and traffic dynamics with a virtual environment in near real time, enabling safety and mobility interventions to be evaluated in a controlled, repeatable setting without exposing road users to risk. Currently, CAV safety validation faces a well-documented gap: physical testing is costly, slow, and may introduce safety concerns, while purely virtual simulations often lack real-world calibration. This project addresses that gap by integrating live testbed data—including LiDAR, CCTV cameras, roadside units, and V2X messages—with simulation-based scenario testing using CARLA, sensor fusion methods, and validated data pipelines. The system targets low latency and high spatial accuracy suitable for behavioral and safety analysis under representative traffic conditions. The platform demonstrates multi-modal capability through two application scenarios: (1) pedestrian crossing conflict analysis at signalized intersections under varying speeds, visibility, and occlusion conditions, and (2) transit signal priority evaluation using U.S. DOT bus trajectory data to assess potential operational impacts, including delay reduction. Validation is conducted using RTK-GPS probe vehicles and annotated video data, with trajectory similarity and time-to-collision metrics quantitatively assessed. Key outcomes include a functional digital twin system, evaluation of safety-critical scenarios with agreement between digital and physical testbed behavior on key performance indicators, a 5-hour annotated dataset with DCAT-US metadata, and three software modules released via GitHub. The extensible platform architecture supports future applications such as emergency vehicle preemption, freight operations, and micromobility, with documented APIs enabling replication across diverse testbeds and agencies.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:33:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676080</guid>
    </item>
    <item>
      <title> Simulating Accessibility from CAVs and ICTs (SACI)</title>
      <link>https://rip.trb.org/View/2680126</link>
      <description><![CDATA[Simulating Accessibility from CAVs and ICTs (SACI) develops a simulation tool that helps transportation agencies understand and plan for the transformative impacts of connected and automated vehicles (CAVs) and information and communication technologies (ICTs) on travel behavior and network demand. As CAVs and ICTs reshape how people choose destinations and routes, new models are needed to predict future demand and usage. The project develops a framework that captures cognitive, perceptual, and behavioral effects of CAVs and ICTs, implements it in an agent-based model using SILO, MITO, and MATSim simulation components, and packages the result as a software tool for use by state, regional, and local DOTs. The model uses multimodal transportation network data from the DC, Maryland, and Virginia region to assess how CAV-ICT deployment affects travel patterns and land use across the region.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:25:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2680126</guid>
    </item>
    <item>
      <title>Transportation Workshop: Streets of the Future</title>
      <link>https://rip.trb.org/View/2677682</link>
      <description><![CDATA[With connected and autonomous vehicles (C/AVs), drones, and delivery robots moving from research labs to urban streets, it will not be long before these technologies wind up on city streets. Unfortunately, public and private transportation stakeholders are generally not suited to keep up with technological change, especially when multiplier effects from various strands of innovation can disrupt urban life. Therefore, there is an urgent need to train a future-focused workforce that can adapt from today's best practices and standards, be creative and critical, and come up with innovative options for road safety in the future. Likewise, there is an urgent need to demonstrate to public/private sector stakeholders the most likely transportation changes and challenges over the next two decades. This project has two aims: (i) offer a new graduate-level course focused on using urban corridors as test beds to imagine reasonably accurate future scenarios that are based on state-of-the-art knowledge from the current times, and (ii) to assemble an exhibition where the lessons learned will be shared more broadly with the transportation community through a virtual environment (VR) and posters.]]></description>
      <pubDate>Wed, 04 Mar 2026 16:43:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677682</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>Data-Driven Policy Guidance for CAV Deployment in Rural Southeastern Communities</title>
      <link>https://rip.trb.org/View/2666840</link>
      <description><![CDATA[This project aims to recalibrate the connected and autonomous vehicle (CAV) benefit analysis tool with regional specific inputs, which will guide policy deployment for safe and effective CAV deployment. With the promise of improving transportation safety, mobility, and other transportation system challenges, CAV technologies may face unique deployment challenges in rural communities, including limited infrastructure, lagging adoption, sparse resources and reduced technological readiness. To address these issues, the project will collect quantitative and qualitative data through surveys and stakeholder engagement targeting residents and stakeholders across eight southeastern states. This region-specific data collection is necessary because previous CAV benefit analysis tools were calibrated using data from the Puget Sound area, which does not reflect the characteristics of the Southeast. Using non-regional inputs would skew safety benefit estimates and reduce the tool’s effectiveness for policy and planning. Collecting data regionally allows for statistically representative input for scenario testing, model calibration, and policy analysis. Further, it facilitates data-driven policy decision making.   ]]></description>
      <pubDate>Mon, 23 Feb 2026 14:30:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2666840</guid>
    </item>
    <item>
      <title>Enabling Mobility of Emergency Medical Service through Connected and Automated Vehicle Preemption</title>
      <link>https://rip.trb.org/View/2669655</link>
      <description><![CDATA[Emergency Medical Service (EMS) vehicles, typically ambulances, have time-critical transportation roles when responding to traffic incidents by bringing first medical responders and equipment from their bases to the incident scenes, and transferring injured persons from the scenes to medical facilities. Addressing the mobility of EMS vehicles supports but public health and safety goals, as well as those related to efficient mobility.     

The traditional way for EMS vehicles to reach their destinations faster is to use audible sirens to alert drivers of their presence. Upon hearing an EMS vehicle’s siren, drivers must yield the right of way to facilitate its passage. Previous research on traffic signal preemption for EMS vehicles has demonstrated its effectiveness in reducing delays at signalized intersections. With the advent of Connected and Automated Vehicle (CAV) technology, vehicles can now communicate directly with each other. EMS vehicles equipped as CAVs could leverage vehicle-to-vehicle (V2V) communication technology to transmit warning messages to the CAVs downstream along their routes, beyond the range of audible sirens. The CAVs that have received these messages can proactively move aside to create a clear lane for the EMS vehicle to pass. This “CAV preemption” concept has the potential to significantly improve EMS mobility, resulting in faster response times, earlier on-scene medical aid, and quicker patient transfer to hospitals. Furthermore, the proposed CAV preemption will accelerate incident clearance and the restoration of highway capacity.  

This research is based on an envisioned CAV preemption system in which an EMS vehicle broadcasts its impending arrival to downstream CAVs, while simultaneously sounding its siren and emitting high-intensity strobe light to request signal preemptions. All CAVs receiving this V2V message will automatically move to the right lane, while only a portion of the non-CAV drivers will comply and respond to the siren. The efficiency of this system depends the following factors: (1) The broadcast range of the warning messages to CAVs, (2) The market penetration rate of CAVs, (3) The move-aside compliance rate of non-CAV drivers, (4) The level of traffic congestion.  

This research will simulate and quantify the efficiency of the proposed CAV preemption system under varying operating conditions. An agent-based simulation model of the El Paso highway network will be used to assess the EMS vehicle’s travel time. Mobility efficiency is defined as the percentage reduction in the average travel time. The travel times of EMS vehicles from their bases (selected fire stations that house ambulances) to multiple incident sites (selected highway locations) will be simulated, extracted, and analyzed. The analyses will assess the impacts of broadcast range, CAV market penetration, non-CAV compliance rate, and traffic volume.   ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:34:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669655</guid>
    </item>
    <item>
      <title>Automating Statewide Seat Belt Monitoring </title>
      <link>https://rip.trb.org/View/2665665</link>
      <description><![CDATA[Despite some recent improvements, seat belt usage in Arkansas, Oklahoma, and Louisiana remains below the national average of 91.9%. These relatively low usage rates position three of the Southern Plains Transportation Center (SPTC) region states among the states with the lowest seat belt compliance, highlighting the need for enhanced safety initiatives and monitoring methods. The National Highway Traffic Safety Administration (NHTSA) requires annual statewide surveys of seat belt use to be eligible for federal funding for highway safety programs, funds that are used for traffic safety campaigns and enforcement programs.   

This project aims to enhance traffic safety programs aimed at education and enforcement of seat belt use. This project will automate statewide seat belt monitoring using Connected Vehicle (CV) and crash report data. CV and crash report data have the potential to reduce data collection burdens for seat belt use monitoring which is used for enforcement and education programs. The project tasks are to (1) implement a full-scale survey of seat belt engagement chain of event processes, and (2) develop mathematical models to estimate the relationship between observational seat belt use and belt use reported by crash reports. Replacing manual observations that are currently used to collect statewide seatbelt use data with continuous CV event and/or crash report data will enable more temporally and spatially continuous observations of seat belt usage. 

This project builds on SPTC Cycle 2 work which established a pilot project to evaluate seat belt engagement chain of events through naturalistic driving study and developed an exploratory model of observed seat belt use and seat belt use reported in crash records using historical data. The pilot project established survey protocols, equipment specifications for dashboard cameras, collected data for a small number of participants (<15 samples), and established a numerical relationship between observed and crash reported belt use.  The proposed work will expand the sample size for both the naturalistic driving experiments and modeling efforts. Cycle 2 outcomes directly support this next step, allowing the project to move from exploratory analysis toward a more operational modeling framework. ]]></description>
      <pubDate>Wed, 04 Feb 2026 15:21:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2665665</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[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. Recently, CV trajectory data providers in the U.S. started to “fuzz” the raw vehicle trajectory data for privacy reasons. These data-fuzzing algorithms may affect the feasibility, accuracy, and robustness of VDOT’s application of CV data for planning purposes. This project assesses the impact of data fuzzing algorithms used by two different CV trajectory data providers, Streetlight and Compass IoT, on potential VDOT application scenarios related to calibration and validation of transportation planning models. This research will further assess the potential of using Compass IoT 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>An AI-Based Reasoning Framework for Proactive Infrastructure Monitoring and Preservation Using Connected Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2655750</link>
      <description><![CDATA[This research proposes the development of a Connected Autonomous Vehicles (CAV)-based Proactive Infrastructure Preserving (CAV-PIP) system to enhance the safety, resilience, and operational efficiency of transportation infrastructure. The system leverages the sensing and communication capabilities of CAVs to enable continuous, real-time detection and reporting of roadway anomalies, such as pavement distress and damaged traffic signage. By fusing multi-modal sensor data and incorporating a retrieval-augmented generation (RAG) framework with large language models (LLMs), the system constructs a dynamic prior knowledge base to reason about infrastructure conditions and recommend context-aware maintenance actions. The project aims to transform current reactive maintenance practices into a data-driven, proactive framework that improves decision-making for transportation agencies. The system will be validated through simulation in the CARLA (Car Learning to Act) environment and supported by curated real-world datasets. Expected outcomes include an integrated detection and reasoning framework, structured maintenance reporting tools, and publicly shareable datasets and software packages. The project's broader impact lies in advancing intelligent infrastructure monitoring technologies, reducing long-term maintenance costs, and contributing to safer and more sustainable transportation systems.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:01:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655750</guid>
    </item>
    <item>
      <title>Understanding Risks and Opportunities for Ramp Metering Control in a Mixed-autonomy Future</title>
      <link>https://rip.trb.org/View/2651988</link>
      <description><![CDATA[Vehicle automation may change traffic flow dynamics. This will also impact the control of traffic flow via infrastructure-based systems such as ramp metering control. In this work the research team investigated the impact that different levels of automation and connectivity will have on ramp metering control, and proposed modifications to existing ramp metering algorithms to improve their performance under different automation scenarios. The team finds that low-level automation such as adaptive cruise control may decrease mainline throughput by up to 58% on average and increase travel time by 61%. However, full connectivity and automation may decrease travel time by up to 40%. Based on these potential impacts, modifications to the ramp metering algorithm settings were developed for each of the seven automation scenarios. These modifications are shown to improve operations in each scenario.]]></description>
      <pubDate>Thu, 08 Jan 2026 15:26:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2651988</guid>
    </item>
    <item>
      <title>Data-Driven Resilience Planning for Transportation Infrastructure: Pilot Study in Texas</title>
      <link>https://rip.trb.org/View/2646954</link>
      <description><![CDATA[This one-year pilot proposes marrying three rich but rarely combined data streams—high-resolution weather data (freeze/thaw, temperature, rainfall, snow/ice, etc.) supplied by the Southern Regional Climate Center (SRCC), Connected-Vehicle Data (movements, windshield wiper events, delay, etc.) that capture real-time operating conditions, and Texas Department of Transportation's (TxDOT’s) own asset and condition inventories (e.g.  pavement condition data) into a cohesive, decision-ready framework. The research team will begin by geolinking these datasets and mining them for hazard frequency, traffic exposure, and structural vulnerability signals. Machine-learning and stochastic life-cycle cost models will then translate those signals into corridor-level risk profiles and economic damage curves under three strategies: do-nothing, reactive repair, and proactive hardening.  

Over the course of twelve months, the research team will iterate through four tightly coupled phases: (1) data assembly and quality control; (2) vulnerability assessment that fuses hazard intensity with deterioration and delay models; (3) scenario-based economic analysis to identify the most cost-effective resilience options; and finally, (4) delivery of an interactive web geographic information system (GIS)-based platform that maps risks, ranks projects, and lets engineers explore “what-if” funding scenarios. The researchers will ensure that methods align with agency workflows and that results are immediately actionable.  

Tangible pilot products—open-source modeling code, corridor-level risk maps, and a web-based GIS platform with an implementation guide and training workshop—will give Texas a clear blueprint for maximizing every resilience dollar. These outputs will enable TxDOT to pursue proactive adaptation and pave the way for multi-state deployment in the future. Expected benefits include lower lifecycle costs, fewer weather-related disruptions, and safer travel for Texans. Equally important, the modular design allows the Southern Plains Transportation Center to extend the framework to other Region 6 states in a potential follow-on effort, furthering USDOT goals for safety and infrastructure durability. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:27:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646954</guid>
    </item>
    <item>
      <title>Connected Corridors Advancement Initiative</title>
      <link>https://rip.trb.org/View/2645418</link>
      <description><![CDATA[The I-80 Corridor Coalition and I-35 Advancement Alliance are spearheading the Connected Corridors Advancement Initiative (CCAI) to address evolving challenges and leverage opportunities in corridor management and transportation technology. As critical transcontinental arteries, these corridors underpin national commerce and mobility, fostering regional connectivity and economic growth.

Building upon AASHTO NCHRP 20-24(138) recommendations and inspired by successful models such as the I-95 Corridor Coalition and the Eastern Transportation Coalition, the initiative seeks to establish a framework for open data standards, infrastructure modernization, and multi-state collaboration. The Nevada DOT SMART Grant Enhancing Corridor Communication Roadmap will serve as a foundation model, showcasing enhanced inter-agency coordination and scalable technology deployment. International efforts, including Europe’s NAPCORE and Canada’s CAV Standards, highlight best practices in data interoperability and advanced infrastructure.

To sustain economic competitiveness, ensure national security, and foster technological leadership, the United States must develop integrated, multi-state corridor frameworks focused on data sharing, operational efficiency, and resilient infrastructure. Enhanced collaboration will bolster domestic mobility, supply chain reliability, and national emergency response capacity. Corridor coalitions have demonstrated the effectiveness of public-private partnerships in addressing infrastructure needs, facilitating transportation planning, and improving operational efficiency across multiple jurisdictions.

OBJECTIVES: The CCAI aims to modernize corridor operations, enhance safety, and optimize economic efficiency by aligning efforts across state, federal, and private sectors. Objectives include developing and implementing open data standards for Work Zone Data Exchange (WZDx), Truck Parking Information Monitoring Systems (TPIMS), and national interoperability of communication data feeds to enable seamless communication across jurisdictions. Additionally, the initiative seeks to prepare the corridor for connected and automated vehicle (CAV) technologies by supporting data interoperability between states, agencies, emergency services, industry partners and the traveling public.]]></description>
      <pubDate>Wed, 24 Dec 2025 15:04:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2645418</guid>
    </item>
    <item>
      <title>Enhancing Rural Freight Resilience in the Southeastern U.S.: Data-Driven Modeling and Decision Support for Supply Chain Efficiency.

</title>
      <link>https://rip.trb.org/View/2643108</link>
      <description><![CDATA[This research aims to address the issue of limited alternative routes in rural freight systems by modeling rural freight networks to identify critical vulnerabilities and evaluate potential recovery strategies. The study also proposes new methods for addressing truck parking shortages using models such as reservation and automated allocation for predicting demand and optimizing supply. The project leverages network science, emerging data sources, and simulation tools to develop methodologies for assessing the resilience of rural freight networks. Additionally, the study will explore the potential of connected and autonomous vehicles (CAVs) for improving operational efficiency and reducing parking demand, particularly for middle-mile delivery and short-range freight operations. This research directly addresses these issues by (1) Developing network-based modeling techniques to analyze rural freight resilience, (2) Identifying critical corridors and evaluating alternative routing strategies, and (3) Proposing innovative truck parking solutions to improve operational efficiency. This includes broader operational strategies such as parking reservations, staging areas near hubs or ports, route reservations, and quicker incident resolution for truckers.  ]]></description>
      <pubDate>Sat, 20 Dec 2025 17:04:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643108</guid>
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