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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Connected and Automated Vehicle (CAV) Readiness Survey: Are MDOT Roads Machine Readable</title>
      <link>https://rip.trb.org/View/2731918</link>
      <description><![CDATA[Considering Michigan Department of Transportation (MDOT) Mission, Values, and Vision, with the evolving landscape of technologies within the connected and automated
vehicle (CAV) industry, there is a pressing need to investigate the requisite support from DOTs to enable seamless integration of
CAVs with infrastructure. As core sensors and systems defining these technologies become more established, understanding the
precise infrastructure requirements becomes paramount. Therefore, the research aims to address the question: "What specific
support and infrastructure enhancements are necessary from MDOT to facilitate effective detection and interaction of connected and
automated vehicles with the surrounding infrastructure?]]></description>
      <pubDate>Fri, 17 Jul 2026 11:47:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731918</guid>
    </item>
    <item>
      <title>Leveraging Telematics Data for Enhanced Traffic Safety: Unveiling Crash-Prone Hotspots and Mitigating Incidents - Phase 2</title>
      <link>https://rip.trb.org/View/2727388</link>
      <description><![CDATA[Cutting-edge connected-vehicle (CV) telematics now stream billions of instantaneous speed, heading, and hard- maneuver records across Texas roadways—an untapped resource for proactive safety management. Phase I of Project 0-7200 capitalized on this opportunity by (1) surveying and vetting statewide CV data sources, (2) building rigorous preprocessing pipelines and a strategic data-archiving scheme with the Receiving Agency, (3) defining data-driven “near-crash” events, and (4) creating proof-of-concept analytics that locate and rank high-risk corridors. Two single-user prototype web tools—TTI’s near-crash explorer and UTA’s multi-criteria hotspot-ranking dashboard—proved the approach valid, with results aligning closely with the Crash Records Information System (CRIS). Phase II will transform those prototypes into a secure, cloud-based, multi-user platform capable of statewide, high-volume ingestion and real-time analytics—advancing the solution to TRL 8 (actual system completed and “TxDOT-pilot ready”). The research teams will, optimize the data-processing engine for scalability, integrate interactive visualizations with enterprise authentication, automate continuous data refresh and long-term archiving, and embed crash- prediction models that fuse telematics with CRIS and roadway inventory. The research teams will develop a decision-support tool that lets TxDOT’s districts quickly pinpoint emerging crash-prone hotspots and deploy targeted countermeasures.]]></description>
      <pubDate>Fri, 10 Jul 2026 17:07:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727388</guid>
    </item>
    <item>
      <title>Applying Telematics Data to Support Traffic Enforcement</title>
      <link>https://rip.trb.org/View/2720301</link>
      <description><![CDATA[Telematics is increasingly used to help traffic enforcement shift toward data-driven, risk-based, and preventive safety interventions. Telematics systems collect data such as vehicle speed, acceleration, braking, cornering, location, time of day, and sometimes phone distraction indicators. In a traffic enforcement context, this data can support hotspot identification and targeted enforcement deployment. Relevant traffic enforcement applications include: 1. Speed management - telematics can reveal where speeding is routine, not just where crashes have already occurred. This is especially useful on arterials, rural roads, school zones, and work zones. 2. Distracted driving risk - mobile-device telematics can indicate patterns of phone interaction while driving. This is better suited for network-level risk mapping than individual citation issuance. 3. Commercial fleet compliance - fleet telematics can support internal safety management, identify repeat risky driving behavior, and guide employer-based interventions. 4. Work-zone safety - telematics can help detect excessive speeds near work zones and support placement of speed safety cameras or police presence.

The Governors Highway Safety Association has recently promoted a shift toward using anonymized, aggregate telematics insights to identify risky conditions before crashes occur, such as repeated high-speed driving through school zones or distraction on rural roads.

Research is needed to develop a better understanding of the application of telematics data to support traffic enforcement.

OBJECTIVES: The objectives of this research are to: Document the existing state of knowledge regarding the application of telematics data to support traffic enforcement; Assess pilot projects underway in various states; Assess primary issues and unintended consequences, and propose measures states can take to manage risks; Develop a guide for state highway safety and other state agencies indicating (1) how they can use this technology to promote traffic safety and (2) recommendations for standardizing the use of telematics data to meet user needs; Propose a study design for use in potential future BTSCRP research to address knowledge gaps.]]></description>
      <pubDate>Thu, 02 Jul 2026 20:00:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2720301</guid>
    </item>
    <item>
      <title>Connected Corridor Advancement Initiative</title>
      <link>https://rip.trb.org/View/2712040</link>
      <description><![CDATA[The Connected Corridor Advancement Initiative (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>Mon, 08 Jun 2026 11:10:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712040</guid>
    </item>
    <item>
      <title>Phase II: After Study Evaluation of Interstate 4 (I-4) Florida's Regional Advanced Mobility Elements (FRAME) Project (After Analysis)</title>
      <link>https://rip.trb.org/View/2706007</link>
      <description><![CDATA[Restart of BED26-977-08. The objective of this research project is to develop the evaluation plan for the after conditions of the I-4 FRAME project. Then, before/after study for the evaluation metrics will be conducted to identify the degree of improvement (or not) for every metric from the before to the after observations. The study findings will be analyzed and documented. To conduct the task, the research team will perform the following activities: (1)  determine the evaluation criteria tailored to the I-4 FRAME project objectives; (2) describe the data collection procedures tailored to these criteria that are needed to report on the achievement of project objectives; and (3) document how the I-4 FRAME project addressed the safety challenges on the project corridors compared with Phase ? (before).]]></description>
      <pubDate>Fri, 22 May 2026 09:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706007</guid>
    </item>
    <item>
      <title>Enhancing Safety in Mixed-Autonomy Traffic via Prediction-Based Connected Autonomous Vehicle Control</title>
      <link>https://rip.trb.org/View/2703928</link>
      <description><![CDATA[This project proposes a new framework for prediction-based connected autonomous vehicle (CAV) control to enhance safety in mixed-autonomy traffic where CAVs and human-driven vehicles (HVs) coexist. Specifically, by predicting future traffic conditions behind a target CAV, the vehicle can be proactively controlled to improve the safety and efficiency of the overall traffic stream. This approach is motivated by the fact that a controlled CAV directly influences the behavior, safety, and performance of following HVs through car-following interactions. Accordingly, the proposed method jointly considers a CAV and its following HVs in the design of a safety-aware driving strategy. Although HVs do not communicate with CAVs, traffic states related to HVs can be estimated using partial traffic measurements collected by CAVs. Leveraging these predictions, the proposed control strategy will be formulated within a model predictive control (MPC) framework to improve safety and traffic efficiency for HVs following a CAV. Extensive simulation studies will be conducted under a range of traffic scenarios and HV driving styles to demonstrate the effectiveness of the proposed approach. In addition, multiple CAV penetration rates will be evaluated to examine scalability and deployment potential. 
This project is highly aligned with the Mid-America Transportation Center's (MATC’s)  mission to advance transportation safety through technology development, technology transfer, and deployment. It addresses a timely safety challenge: near-term traffic will be mixed-autonomy, where early-generation autonomous vehicles (e.g., ACC-equipped vehicles and emerging CAVs) operate alongside the majority of HVs. In this environment, safety risks arise not only from individual vehicle performance, but also from interactions between automated and human drivers—an issue that is often overlooked in existing CAV control design.
While this project will leverage an existing dataset collected in Minnesota and high-fidelity simulation data generated in Simulation of Urban MObility (SUMO) for numerical investigation and validation, the team anticipates extending the proposed methodology in future work using connected-vehicle and SPaT data to be collected by Dr. Li Zhao’s team at UNL, in collaboration with the Nebraska DOT. 
]]></description>
      <pubDate>Thu, 21 May 2026 22:41:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703928</guid>
    </item>
    <item>
      <title>Health-Aware Edge Computing for Durable Autonomous Transportation</title>
      <link>https://rip.trb.org/View/2696026</link>
      <description><![CDATA[Across global markets, transportation systems are rapidly evolving toward automation, pervasive sensing, and intelligent decision-making capabilities. These advancements are often designed primarily around traditional metrics, such as safety, throughput, and cost. Modern autonomous and semi-autonomous systems introduce new types of human exposures (e.g., fatigues, cognitive stress, motion discomfort) and new system constraints (e.g., battery degradation, vibration-induced wear, thermal loads). If left unmanaged, these exposures degrade long-term system performance, reduce user trust and adoption, and impose hidden lifecycle and health costs. This project proposes a new research paradigm for Health-Aware and Durable Transportation Systems, enabling through advanced technologies in autonomous driving, edge computing, and optimized machine learning. We envision that transportation systems can be engineered to actively sense, model, and mitigate human and mechanical exposures, turning transportation into a joint human-machine health ecosystem. The research objectives include: 1) develop joint occupant/vehicle exposure models that quantify health and mechanical burdens, 2) enable adaptive autonomy strategies that mitigate cognitive stress, fatigue, and mechanical wear, and 3) build edge computing framework for efficient inference and control.  ]]></description>
      <pubDate>Thu, 23 Apr 2026 17:32:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696026</guid>
    </item>
    <item>
      <title>Generating reliable freight disruption measures with freight telematics data</title>
      <link>https://rip.trb.org/View/2684220</link>
      <description><![CDATA[Freight network resilience is critical for economic stability, especially during disasters and infrastructure failures. This study refines disruption measures using Robinsight, COMPASS IOT, and Robinsight telematics data, alongside WAZE crowdsourced data and infrastructure-based instrumentation (TN RDS). Building on prior research, we analyzed freight mobility impacts from events like the Oregon Durkee Fire (2024), Hurricane Helene, and major bridge closures (I-40, I-55, I-84).

Year 3 focuses on validating key disruption indicators, enhancing predictive models, and integrating emerging data sources to assess infrastructure failures and safety risks from freight detours. Aligned with US Department of Transportation priorities, this research provides transportation agencies with actionable insights to improve freight mobility, inform infrastructure investments, and strengthen supply chain resilience. The findings will support data-driven decision-making, ensuring a more adaptive and robust freight transportation system.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:27:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684220</guid>
    </item>
    <item>
      <title>Evaluation of Vehicle Telematics and Infrastructure-based Connected Vehicle Data for Real-Time Safety and Mobility Application
</title>
      <link>https://rip.trb.org/View/2625309</link>
      <description><![CDATA[The emergence of connected vehicle (CV) data has provided unprecedented opportunities for developing real-time, proactive applications to enhance safety and mobility. This project utilizes and compares telematics and infrastructure-based CV data to determine optimal applications for each and explore integration strategies for safety and mobility solutions. Specifically, telematics CV data provide the location, speed, and other key information on approximately 5-10% of vehicles on the road. In contrast, infrastructure-based CV data from the connected corridor in the City of Madison contain information about traffic signals, vehicles, and road geometry. By comparing and integrating these data sources, this project proposes physics models and neural network algorithms to detect real-time safety issues such as crashes. The detection results can be used to issue immediate warnings to drivers, traffic managers, and automated vehicle systems. To disseminate these warnings, the research team proposes utilizing roadside variable message signs and in-app notifications via platforms like HAAS, Google Maps, and Waze. The proposed applications can be piloted through field tests in the University of Wisconsin-Madison’s Level 3 CAV testbed and possibly later at Mcity.]]></description>
      <pubDate>Thu, 13 Nov 2025 15:31:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625309</guid>
    </item>
    <item>
      <title>IVHS Study (ENTERPRISE)</title>
      <link>https://rip.trb.org/View/2616149</link>
      <description><![CDATA[The objective is to investigate and promote Intelligent Vehicle Highway Systems (IVHS) approaches and technologies that are compatible with other national IVHS initiatives.]]></description>
      <pubDate>Tue, 28 Oct 2025 19:27:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2616149</guid>
    </item>
    <item>
      <title>AI-Driven Telematics Solutions for Detecting Near-Crash Events and Safety Hotspots in Texas Transportation Networks</title>
      <link>https://rip.trb.org/View/2606398</link>
      <description><![CDATA[The research team will leverage telematics data to proactively identify near-crash events and hotspots, strengthening transportation safety management across Texas. The work will advance analytical methodologies in four areas: (1) trajectory-based analysis of vehicle movement patterns; (2) event-based analysis of critical driving behaviours such as hard braking and abrupt manoeuvres; (3) multi-criteria analysis integrating mobility, safety, and environmental performance; and (4) data fusion techniques that combine telematics with other sources, including traffic sensors and historical crash records. Building on this foundation, the research team will apply spatial-temporal analyses and machine learning predictive models to detect current and forecast future near-crash hotspots. An interactive artificial intelligence (AI)-powered decision-support system will be developed to provide transportation agencies with actionable insights for targeted safety interventions. Rural and urban case studies will demonstrate the platform’s applicability and validate its effectiveness through comparisons with historical crash data. An implementation roadmap will guide integration into agency safety management practices. The project will deliver robust analytical tools and evidence-based recommendations that can be seamlessly integrated with existing platforms—such as geographic information system (GIS) systems, roadway networks, and performance dashboards— ensuring compatibility and significantly enhancing proactive traffic safety measures statewide.]]></description>
      <pubDate>Thu, 02 Oct 2025 09:44:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606398</guid>
    </item>
    <item>
      <title>Intelligent Aerial Drones for Railroad Track Traversability Assessment, Intrusion Detection 
and Integrity Evaluation</title>
      <link>https://rip.trb.org/View/2573854</link>
      <description><![CDATA[Aerial drones have been increasingly used in railroad operations as they offer an effective low-cost solution that can be easily deployed and efficiently support the human efforts in inspection and monitoring activities. This proposal outlines the development of an advanced system leveraging intelligent aerial drones for comprehensive railroad track monitoring and evaluation. The project serves as the integration phase (phase 3) of two University Transportation Center for Railway Safety (UTCRS) projects that in the previous two phases developed related technology: (i) a project on the development of Intelligent Aerial Drones for Traversability Assessment of Railroad Tracks, and (ii) a project on the development of AI-enabled system for Track Intrusion Detection and Track Integrity Evaluation. Through this integration, an intelligent aerial drone will be developed able to carry equipment for the autonomous inspection of railroad tracks with the following capabilities: (i) Visual-based identification and autonomous following of the track; the system will be able to work even in GPS-degraded environments (tunnels, dense forests); (ii) Collision avoidance capability where the drone senses and avoids obstacles; (iii) Track centering capability where the drone follows the same line regardless of the number of tracks in the field of view; (iv) Identification and mapping of any obstacles identified blocking the line; (v)Intrusion\Trespassing detection; and (vi) AI-based Detection, Classification, Tracking, and Situational Evaluation. This innovative solution promises to improve operational efficiency, safety, and cost-effectiveness in the management of railroad networks, while minimizing downtime and enhancing system reliability.]]></description>
      <pubDate>Mon, 14 Jul 2025 19:12:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573854</guid>
    </item>
    <item>
      <title>Spun Concrete Poles: Guidelines for Fabrication, Condition Assessment, Repair, and Replacement</title>
      <link>https://rip.trb.org/View/2562259</link>
      <description><![CDATA[As per the Appendix A of the Michigan Ancillary Structure Inspection Manual (MiASIM), Spun Concrete Poles (SCPs) are "high
mast prestressed precast concrete poles used to support ITS [Intelligent Transportations System] infrastructure such as
cameras and radar detectors." The Michigan Department of Transportation (MDOT) is managing more than 300 poles with ITS
infrastructure. Cracking and deterioration documented during field inspections highlight the need for developing guidelines and
recommendations for fabrication quality improvement, condition assessment, and supporting repair and replacement
decisions.]]></description>
      <pubDate>Fri, 06 Jun 2025 14:29:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2562259</guid>
    </item>
    <item>
      <title>Explainable Machine Learning for Data Efficient Attack Detection in Intelligent Transportation Systems</title>
      <link>https://rip.trb.org/View/2559167</link>
      <description><![CDATA[The rise of Intelligent Transportation Systems (ITS) and connected autonomous vehicles (CAVs) has revolutionized transportation but has also introduced significant cybersecurity risks. This project focuses on developing an explainable anomaly detection framework that leverages normal operational data to identify cyber-attacks, addressing the challenge of limited labeled attack data in early ITS deployment stages. By framing the problem as open-set recognition, the system integrates explainable artificial intelligence (AI) techniques, such as Occlusion Sensitivity Maps and a zero-bias deep learning framework, to enhance transparency and trust. Incremental learning algorithms will enable data-efficient adaptation to evolve cyber-attack scenarios, ensuring robust protection against threats like compromised nodes and system exploits.]]></description>
      <pubDate>Thu, 29 May 2025 22:25:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2559167</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>
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