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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" />
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    <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>
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    <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>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>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>
    </item>
    <item>
      <title>Framework for Transportation Systems Management and Operations Curricula
</title>
      <link>https://rip.trb.org/View/2558371</link>
      <description><![CDATA[Transportation systems management and operations (TSMO) is a set of strategies focused on operational improvements that maintain and restore the performance of existing transportation systems before additional capacity is required. A wide range of careers falls under TSMO, including intelligent transportation system (ITS) engineers, traffic management control operators, and data scientists. Growing and developing the TSMO workforce requires multiple approaches and strategies, from educating students about TSMO careers to preparing existing professionals from other fields to transition into TSMO roles.

Several resources exist to advance the knowledge, skills, and abilities of TSMO practitioners. For example, the American Association of State Highway and Transportation Officials (AASHTO) Transportation Operations Manual serves as a resource for transportation agencies to develop and sustain the operational capabilities and strategies needed to preserve and optimize system performance. The Operations Academy is a training program designed for mid- to high-level managers whose current or future responsibilities include TSMO. The National Operations Center of Excellence (NOCoE) TSMO Workforce Development website provides a variety of additional resources. However, few initiatives focus on individuals who have not yet entered, or are new to, the TSMO workforce.

Research is needed to help educational organizations develop and align curricula that introduce TSMO concepts, build relevant skills, and connect students with career opportunities in the field.

The objective of this research is to develop a framework for educators to establish recruitment pipelines that expand the talent pool and enhance the competencies of candidates entering the TSMO workforce.]]></description>
      <pubDate>Thu, 29 May 2025 13:03:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558371</guid>
    </item>
    <item>
      <title>Crash Modification Factors for Intelligent Transportation Systems Infrastructure and Applications</title>
      <link>https://rip.trb.org/View/2558374</link>
      <description><![CDATA[Intelligent transportation systems (ITS) applications—including dynamic message signs, variable speed limits, closed-circuit televisions (CCTVs), traffic monitoring stations, ramp meters, and road weather information systems (RWIS)—are used to manage traffic, improve incident response and traffic operations, and enhance roadway safety. However, despite widespread ITS deployment, existing research provides limited crash reduction evidence for many ITS applications. 

While ITS can be a cost-effective strategy to achieve mobility and safety goals, state and local agencies also need credible estimates of safety benefits for use in safety performance analyses and investment decisions. Research is needed to develop more reliable crash modification factors (CMFs) for ITS applications to strengthen these analyses and support data-driven decision-making.

OBJECTIVES: The objectives of this research are to (1) develop CMFs for ITS infrastructure and applications on roadways and (2) develop a method to incorporate the CMFs into safety performance and investment analyses. 

]]></description>
      <pubDate>Thu, 29 May 2025 12:58:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558374</guid>
    </item>
    <item>
      <title>Vehicle Edge Computing for Travel Behavior and Demand in Future Intelligent Transportation Systems (ITS)</title>
      <link>https://rip.trb.org/View/2553151</link>
      <description><![CDATA[Meeting the diverse needs of stakeholders such as passengers, drivers, and service providers is imperative. Modern travelers seek real-time updates and personalized journey experiences. Drivers need consolidated data for safety and punctuality (Chen et al., 2021), while service providers rely on data analytics to optimize resources and enhance reliability (Wang et al., 2020). Traditional centralized computing infrastructures struggle with the agility and responsiveness needed in the dynamic transportation landscape (Li et al., 2017). Edge computing emerges as a transformative solution by offloading computational tasks to roadside units. This enables swift processing for real-time applications, facilitating dynamic route optimization, congestion management, and resource allocation, thereby enhancing operational efficiency and reducing travel times. The project will investigate how edge computing impacts travel behavior. Field studies and simulations will measure travelers’ responsiveness to real-time data and how it influences their travel choices and demand patterns. This ensures the research is relevant to travel behavior studies. 

Edge computing not only enhances current transportation operations but is also crucial for autonomous vehicles. It allows real-time data processing and analysis for navigation, hazard detection, and collision avoidance. By leveraging edge computing, autonomous vehicles can offload computational tasks, alleviating the burden on onboard systems and ensuring seamless, responsive data processing without compromising safety or performance. The collaborative framework between autonomous vehicles and roadside units facilitates continuous learning and adaptation. Real-time access to advanced computing enables autonomous vehicles to use machine learning for predictive analysis, enhancing their ability to anticipate and respond to changing road conditions and traffic patterns. Integrating edge computing with autonomous vehicles creates a symbiotic relationship that enhances autonomous driving systems and accelerates the development of safer, more efficient transportation systems. This aligns the project with the theme of improving the mobility of people and goods, fitting the TBD center’s priorities. ]]></description>
      <pubDate>Tue, 13 May 2025 19:05:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553151</guid>
    </item>
    <item>
      <title>Utilizing Digital Twining Technologies for Addressing FDOT Priorities</title>
      <link>https://rip.trb.org/View/2550951</link>
      <description><![CDATA[This project has the following objectives: • The primary objective of this project is to establish a comprehensive understanding of digital twin (DT) technology in the context of transportation. This involves providing an overview, conducting a thorough literature review, and exploring practical use cases that create a foundation for further applications. • Proposing a prototype DT framework tailored to the specific needs of the Florida Department of Transportation (FDOT). This involves addressing technical considerations such as data integration, interoperability, and scalability to lay the groundwork for practical implementation. • Developing comprehensive and practical DT frameworks for selective sub-domain in transportation and transportation systems management and operations (TSMO) applications, such as utilizing the SunTrax testing facility as a real-world testing ground. This involves simulating scenarios, integrating diverse data sources, and showcasing the potential of DT technology in enhancing decision-making, infrastructure planning, and testing various intelligent transportation systems (ITS) applications.]]></description>
      <pubDate>Thu, 08 May 2025 12:29:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2550951</guid>
    </item>
    <item>
      <title>Smart Transportation Digital Infrastructure: Advancing System Equity, Resilience, and Safety through Multi-Source Open-Standard Data Integration</title>
      <link>https://rip.trb.org/View/2549292</link>
      <description><![CDATA[The recently emerging trend of sensor technology, ubiquitous and high-performance computing is creating a revolutionary paradigm shift in the coming years. Through data and feedback, both simulated and real, a Digital Infrastructure (DI) for smart cities has received increasing attention. The pandemic, in many cases, is accelerating this need, as there are critical needs for analyzing the health and safety of citizens. With the rise of the digital infrastructure, cities have many adoptions in transportation, utilities, buildings, and citizen services. For community mobility applications, the pairing of the virtual and physical world allows analysis of data and monitoring of systems, evaluating different improvement strategies, and planning the future by using simulations. Smart Transportation Digital Infrastructure (STDI) is to create sustainable urban systems that benefit the citizens and societies at large. It represents a fundamentally new approach for close-loop large-scale system modeling, ubiquitous communication, and diverse data synthesis and can provide an integrated solution for data, simulation, connection, and human interaction, which are the four key elements of achieving the paradigm’s main functions for smart community applications. 

There are three critical challenges for STDI: digital at scale, decision intelligence in data-intensive systems, and consistency between objectives, decisions, and execution. Open-STDI could not only dramatically reduce the cost and complexity of managing computers and simulation models but also redefines what is tractable regarding dispersed bi-directional intra-system communication between different community stakeholders and citizens. Therefore, connected and smart communities represent an ideal DI application, but one that requires transformative advances both within the traditional domains of city planning, community policy analysis, network behavior, and demand forecasting but also within the emerging field of DI itself.

This project aims to develop an Open data hub and Open-source data analysis platform for transportation-focused Open-STDI applications. That is, the proposed framework Open-STDI will deliver rapid prototyping of STDI and enable smarter multimodal policy decisions for transforming the livability, sustainability, and resilience of the community. A successful STDI in the project will enable both: (1) integration of a variety of legacy and emerging transportation data sources, covering supply, demand, resilience, safety, and security aspects, etc.; and (2) integration of data analysis, data visualization, traffic estimation on a unified platform. Designers, faculties, and engineers can use the integrated platform for quick, inexpensive prototyping of new ideas, which further provides a potential for creating new forms of citizen engagement by communities and new approaches to city operations and management by city planners. This project, in collaboration with the IEEE Department of Global Sales & Customer Operations, will primarily utilize data from IEEE National Performance Management Research Data Set (NPMRDS) and OpenStreetMap data to understand mobility characteristics, and use Google mobility data to discover resilience features of transportation system.
]]></description>
      <pubDate>Mon, 05 May 2025 16:01:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2549292</guid>
    </item>
    <item>
      <title>Application of Digital Twins for Testing Connectivity, Automation and Cooperation Applications</title>
      <link>https://rip.trb.org/View/2543435</link>
      <description><![CDATA[The goal of this project is to demonstrate the value of a digital twin for one Transportation Systems Management and Operations (TSM&O), connected vehicles (CV), and/or cooperative driving automation (CDA) application. The specific objectives: (1) develop a concept of operations of using digital twin for the selected case study; and (2) develop, demonstrate, and evaluate the case study digital twin based on the developed concept.]]></description>
      <pubDate>Fri, 25 Apr 2025 08:55:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543435</guid>
    </item>
    <item>
      <title>RES2023-30: I-24 Smart Corridor</title>
      <link>https://rip.trb.org/View/2539923</link>
      <description><![CDATA[The I-24 SMART Corridor takes a comprehensive approach to improving the safety and travel time reliability along the corridor utilizing existing infrastructure and emerging technology. Vehicle-to-Everything (V2X) technologies are a key initiative of 
Tennessee Department of Transportation (TDOT) by aligning with several strategic goals of TDOT including safety, mobility, sustainability, and consistent customer experience. To achieve the benefits of successfully applied V2X
technologies along the I-24 SMART Corridor, a clearly defined direction of V2X deployments needs to be established. The path towards applying V2X technologies throughout the I-24 SMART Corridor is described within the I-24 SMART Corridor V2X Roadmap. The I-24 SMART Corridor Roadmap provides an evaluation of
the existing Intelligent Transportation Systems (ITS) infrastructure along the corridor as well as an implementation plan for V2X applications that meet the goals of the I-24 SMART Corridor. The initial deployment locations for V2X applications were based on several safety factors including existing traffic volumes, crash history, and reoccurring
congestion. These safety factor hotspots led to the specific V2X application needs along the I-24 SMART Corridor. Along with the hotspots, geometric factors were included in determining which specific V2X applications were most applicable at each hotspot location. In addition to identifying and locating where specific V2X applications should be provided along the I-24 SMART Corridor, the Roadmap provides the costs associated
with implementing these applications. These costs include software, physical integration, vehicular integration, and annual operations and maintenance costs.]]></description>
      <pubDate>Thu, 17 Apr 2025 13:50:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2539923</guid>
    </item>
    <item>
      <title>Freight Route Management Application for the Port of Anchorage</title>
      <link>https://rip.trb.org/View/2512620</link>
      <description><![CDATA[The objective of this research is to develop and evaluate an intelligent transportation management application for improving the efficiency, safety, reliability, and cost-effectiveness of freight and fuel truck movement to/from the Port of Alaska located in Anchorage, Alaska. This is a partnership project between the City of Anchorage’s Port of Alaska and Alaska Department of Transportation and Public Facilities (DOT&PF).  Truck transportation network located at the port will be able to better route and stage cargo transport within the Port of Alaska footprint. The application could be used outside the port by truck drivers, Alaska 511, and traffic operations centers.]]></description>
      <pubDate>Fri, 21 Feb 2025 21:42:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2512620</guid>
    </item>
    <item>
      <title>Transition to Cellular V2X for Georgia's Connected Vehicles
</title>
      <link>https://rip.trb.org/View/2511181</link>
      <description><![CDATA[This research aims to provide technical guidance for the Georgia Department of Transportation (GDOT) on DSRC-C-V2X transition, ensuring the seamless operation of Georgia's connected vehicles and ITS infrastructure. By addressing this interference issue, the research team aims to pave the way for broader acceptance and implementation of intelligent transportation systems. The significance of this research stems from the limited number of studies addressing in-band interference from Wi-Fi in the 5.9 GHz band due to the Federal Communication Commission's (FCC's) recent order. Notably, these studies that were conducted by relevant organizations such as U.S. Department of Transportation (USDOT) and FCC lack technical details in regard to the bandwidth reduction to the ""upper 30 MHz."
]]></description>
      <pubDate>Tue, 18 Feb 2025 15:08:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2511181</guid>
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