<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.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>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Learning to Predict Constraints: Hybrid Neural-MPC Control Architectures for Real-Time Vehicle Path Tracking</title>
      <link>https://trid.trb.org/View/2685835</link>
      <description><![CDATA[This paper proposes hybrid Neural Network-based Model Predictive Control (NN-MPC) architectures for real-time autonomous vehicle path tracking. To mitigate the computational burden of Model Predictive Control (MPC) in real-time operation and the absence of stability and safety guarantees in purely supervised approaches, we integrate supervised learning directly into the optimization process to accelerate solver convergence. Specifically, Multilayer perceptrons are trained to learn a constraint-correction term added to the closed-form unconstrained MPC solution, and to provide learning-assisted warm starts for an active-set QP solver by predicting the Lagrange multiplier vector and the active-constraint pattern. The first solution avoids online quadratic programming (QP) and achieves an approximately two orders of magnitude reduction in computation time, at the cost of approximate constraint enforcement in highly transient conditions, whereas the latter two solutions retain the original constrained optimization problem. The learning components are trained on a simplified design model and evaluated in the high-fidelity CarMaker environment to assess robustness under unmodeled dynamics and modeling errors. In CarMaker validation, the classification-based warm start reduces the number of QP iterations by approximately 30% and computation time by 23% relative to a classical shift-initialization warm start, supporting more predictable real-time operation in representative driving scenarios.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685835</guid>
    </item>
    <item>
      <title>Towards Comprehensive Safety Assurance of a DAL A AI/ML-based Runway Alignment System using Overarching Properties</title>
      <link>https://trid.trb.org/View/2732486</link>
      <description><![CDATA[This report details an Overarching Properties (OPs)-based approach for assuring the safety of Artificial Intelligence/Machine Learning (AI/ML)-based digital aerospace systems. To rigorously evaluate and enhance this approach, an AI-Assisted Autonomous Runway Alignment (AARA) system is introduced as a motivating use case, allowing for the identification and mitigation of potential safety risks through premise-based arguments. The study demonstrates how multi-level safety assessments can be conducted for AI/ML systems to pinpoint failure conditions inherent to AI/ML's nature. It also provides examples of how requirements can be formulated to address risks posed by black-box AI/ML components with unpredictable or uncontrollable behaviors. Comprehensive discussions cover various aspects of the OPs-based approach, including foreseeable operating conditions, development and training activities, evidence generation for premises, hybrid certification, design assurance, necessary assumptions, and a plan for OPs compliance. While currently focused on Artificial Neural Networks (ANNs) developed using supervised learning, the arguments may be adaptable to other AI techniques. The findings establish a strong foundation for applying OPs to AI/ML assurance, acknowledging the need for further work to ensure robustness and practicality across diverse criticality and autonomy spectrums.]]></description>
      <pubDate>Thu, 13 Aug 2026 15:07:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732486</guid>
    </item>
    <item>
      <title>Single-Vehicle Trajectory Prediction: A Review and Experimental Embedded Assessment</title>
      <link>https://trid.trb.org/View/2685822</link>
      <description><![CDATA[Due to technological advances in the automotive field, advanced driver assistance systems have attracted increasing interest from various research and development entities. Predicting road users' future trajectories remains an active research challenge for advanced driver assistance systems. Accurate Trajectory Prediction (TP) allows anticipation of surrounding road users' future motion, enabling timely safety-critical interventions such as speed regulation and emergency braking in unexpected driving situations. Recent advances in TP methods based on artificial intelligence have demonstrated remarkably accurate results compared to traditional methods. However, many of these models require a high computational burden, which makes their deployment on embedded architectures with constrained resources challenging. To overcome these constraints, TP models need to be lightweight and efficient to meet the real-time and power consumption requirements of advanced driver assistance systems. In other words, they must maintain high accuracy while guaranteeing low computational load and rapid inference. This paper presents a comparative and experimental review of state-of-the-art vehicle TP models. First, we propose a new taxonomy based on the operating environment, the trajectory output type, and the employed modeling approach to classify existing methods. Then, we evaluate representative approaches w.r.t the taxonomy in terms of accuracy, model complexity, computational performance, and real-time feasibility across a high-performance architecture and an embedded architecture. Finally, we discuss the evaluation results and present key conclusions and future directions.]]></description>
      <pubDate>Wed, 05 Aug 2026 09:14:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685822</guid>
    </item>
    <item>
      <title>Resource Allocation Schemes for Scalable Panel-Based LIS Surfaces</title>
      <link>https://trid.trb.org/View/2685815</link>
      <description><![CDATA[Panel-based large intelligent surface (LIS) systems, where each panel comprises a lower, but significant, number of antennas, and is equipped with several baseband outputs (generally much smaller than the number of terminals), allow for a direct dimensionality reduction. However, such dimensionality reduction may not be enough to simplify the overall system. This paper focuses on fully decentralized system architectures that allow for promising performances while requiring low, or even no, central data processing. This paper proposes panel-selection and panel-terminal association algorithms for decentralized panel-based LIS communication systems aiming at allocating a set of terminals to a given panel, which is limited to a given number of outputs, in a sequential or flooding manner, while maximising the minimum terminal rate. Performance results show that sequential and flooding distributed system architectures offer decent convergence rates in comparison with a centralized system architectures when selecting a proper combination of system parameters (number of panels, number of terminals, number of baseband outputs), in turn ensuring scalability. It is shown that, for a given combination of the system parameters, a max-min terminal rate achieved when considering sequential and flooding distributed system architectures can converge to the one achieved by the centralized system architecture, occurring after just a few iterations. Both the proposed sequential and flooding algorithms represent a less complex, but effective way to perform both panel selection and panel-terminal association, allowing for the deactivation of a significant portion of panels without substantially degrading the max-min terminal rate achieved by the centralized system architecture.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:10:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685815</guid>
    </item>
    <item>
      <title>Rampcast Phase II: Connected Vehicles Traffic Management Application on Indiana Highways</title>
      <link>https://trid.trb.org/View/2727584</link>
      <description><![CDATA[This project advances connected vehicle applications by developing and testing an enhanced RampCast system, a comprehensive traffic management system using Cellular Vehicle-to-Everything (C-V2X) technology for Indiana highways. The system features a dual-mode architecture integrating both short-range (PC5) and long-range cellular (Uu) C-V2X communication pathways, utilizing commercial-grade Cohda MK6 hardware and adhering to SAE J2735 standards to ensure interoperability. A key innovation is the integration of an Artificial Intelligence (AI)-based prioritization framework, which leverages a large language model to enhance the contextual relevance of traffic messages. This AI system introduces two intelligent agents: one to dynamically estimate the appropriate display distance for an event based on its severity, and another to prioritize the order of messages based on urgency and potential driver impact. Field tests conducted on I-65 and I-70 in Indianapolis validated the system's hybrid design. Results confirmed that the PC5 link provides very low latency (around 25 ms), ideal for time-critical alerts, while the Uu link ensures highly reliable coverage in complex environments, albeit with higher latency (around 45 ms). The AI framework was successfully shown to reorder and present messages based on real-time context, improving the clarity and usefulness of information provided to the driver. These findings support a hybrid C-V2X architecture as a robust model for future smart highway deployments.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:48:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727584</guid>
    </item>
    <item>
      <title>Phase 2 Interface Control Document (ICD) Heart of Iowa Regional Transit Agency ITS4US Deployment Project</title>
      <link>https://trid.trb.org/View/2724687</link>
      <description><![CDATA[Heart of Iowa Regional Transit Agency (HIRTA) is one of four awardees for Phase 2 of the ITS4US program for its proposed concept “Health Connector: Bridging the Gap Between Healthcare and Transportation” (Health Connector) by the United States Department of Transportation (USDOT). Per the goals of the program, Health Connector project is focused on improving transportation access to healthcare for underserved groups in Dallas County, Iowa. This document serves as the Interface Control Document (ICD) for HIRTA. The ICD serves as a companion document to the System Architecture Document (SAD) and provides a detailed description of the internal and external interfaces for the HIRTA ITS4US Deployment Project and the data, information and messages that are transcribed across those interfaces. It also details related hardware and software components for each interface. The ICD provides traceability of requirements tied to each interface from user needs through design for Phase 2 of the project. The ICD is supported by the companion HIRTA ITS4US Deployment Project System Design Document (SDD) which provides a detailed description of the overall system for Health Connector’s mobility on demand solution, followed by a detailed description of each of the system application components.]]></description>
      <pubDate>Tue, 14 Jul 2026 13:34:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724687</guid>
    </item>
    <item>
      <title>Software Patterns and Architectural Decisions for a Next-Generation Traffic Management System in the TANGENT Project</title>
      <link>https://trid.trb.org/View/2671029</link>
      <description><![CDATA[This paper introduces the TANGENT project and how it is exploring Dynamic Management of Multimodal Traffic in four European cities. It focuses on how specific software patterns and architectural decisions are contributing to the success in creating and trialling a Simulation powered Next-Generation Traffic Management System. It starts by exploring the motivation behind a TANGENT API where a REST API coexists with a Message Queue Broker and how they synergize to cover the different needs of distinct data producers and consumers within and around the TANGENT project. It then explores a generic approach for the different pieces of the TANGENT API. How incoming data handling from the message queue service is implemented generically in the TANGENT API, how the REST API is implemented with this same approach and how the OpenAPI documentation of the REST API is produced. Highlighting the central role of the JSON Schemas and meta-data stored in TANGENT Schema catalogue and how it is simple to add new data sources with this approach as opposed to custom handling the individual data sources.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671029</guid>
    </item>
    <item>
      <title>Collaborative Digitalisation and the Future of Networked Production: Exploring Decentralised Technical Intelligence in Supply Chains</title>
      <link>https://trid.trb.org/View/2671004</link>
      <description><![CDATA[Networked production, supported by advanced logistics and supply chain processes, is crucial for companies to stay competitive and foster cooperation and integration of production resources. It replaces sequential processes with dynamic arrangements, presenting challenges like managing product variants, short life cycles, and process optimisation. Agility is vital for adapting to changes and natural disasters. Decentralised Technical Intelligence (DTI) is an approach that manages complexity and incentivises integrating new technologies in planning and manufacturing.DTI involves distributed and autonomous intelligence embedded in interconnected systems, where humans and machines collaborate to achieve common goals. Humans bring unique skills like creativity and intuition, complementing AI’s capabilities. DTI relies on a multi-agent architecture, enabling trust, interoperability, and data sharing for better decision-making and efficiency. The EU knowlEdge project exemplifies this by providing AI solutions that are distributed, secure, standardised, and collaborative, integrating cognitive technologies, data analytics, IoT and more.DTI’s human-centric design fosters a different quality of intelligence, leading to greater autonomy within multi-agent systems. To realise advanced networked production, a roadmap must be implemented, focusing on a vision, value promise, and development pathway. Europe can maintain its leadership in future networked production through this approach.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671004</guid>
    </item>
    <item>
      <title>Conceptual Context-based Access Control Framework for Improving Privacy in Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2711019</link>
      <description><![CDATA[Autonomous vehicles (AVs) collect large amounts of raw data from various sensors. Often the data collection, processing and communication raises privacy concerns. These sensor data are integrated with contextual information. This transforms the data into usable metadata that enables real-time processing and analysis. Metadata is privacy sensitive, which requires General Data Protection Regulation (GDPR) compliance and comes with a set of compliance obligations for data controllers and processors. In this research, we propose a conceptual context-based access control framework that enhances the privacy of data collected and processed in autonomous vehicles. The proposed access control framework introduces a layered and modularized architecture. The use of a layered architecture allows the handling of difering privacy risk levels between real-time and historical data. As the potential risk of revealing a specific person in combined data is higher in historical data compared to real-time data, a containment method is proposed for real-time data and sandboxing is proposed for historical data. The data that are processed by AVs consists of personal and non-personal data. While personal data are already a privacy-breaching risk solely, there is also a risk of identifying a specific person by the combination of non-personal data. The proposed access control framework classifes the data, containerizes the data through sandboxing and containment methods, and applies specific access control according to the data context and its privacy implications.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711019</guid>
    </item>
    <item>
      <title>SMART METRO [supporting dataset]</title>
      <link>https://trid.trb.org/View/2717068</link>
      <description><![CDATA[SMART METRO is a regional initiative focused on enhancing planning efforts in Broward by leveraging advanced systems integration. This forward‐thinking program is designed to foster more comprehensive regional planning, bolster mobility, and strengthen the vital connections between jobs and housing for Broward’s residents as population levels grow. By leveraging data science, geospatial analytics, artificial intelligence (AI), and simulation, SMART METRO delivers actionable insights to address key regional challenges. Through its integrated digital twin platform, the program combines transportation, land use, demographic, and extreme weather data, enabling users to simulate real‐world processes and planning scenarios. This dataset includes the raw data (bronze), transformed data (diamond), transformation SQL logic, and PDF documents used by the BMPO SMART METRO application.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:11:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717068</guid>
    </item>
    <item>
      <title>SMART METRO: Data Management Plan</title>
      <link>https://trid.trb.org/View/2717067</link>
      <description><![CDATA[With support from the U.S. Department of Transportation (USDOT) SMART Grant Stage 1 Prototyping Grant, the Broward Metropolitan Planning Organization (BMPO) is developing SMART METRO, an innovative regional application of systems integration to create and implement an efficient foundational digital twin platform. Accelerating threats to infrastructure, housing costs, congestion impacts, and population growth pose significant risks to Broward County, Florida. SMART METRO will integrate data science, geospatial analytics, and simulation tools and models for efficient transportation, infrastructure hardening, economic development, and land-use analysis. This data management plan describes: the data collected or used during the project; data format and metadata standards employed; data access policies; re-use, redistribution, and derivatives products policies; and the archiving and preservation plan.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:11:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717067</guid>
    </item>
    <item>
      <title>SMART METRO</title>
      <link>https://trid.trb.org/View/2717171</link>
      <description><![CDATA[Broward County, Florida is experiencing rapid population growth, which is accompanied by challenges related to safety, traffic congestion, rising costs, and infrastructure demands. These factors complicate efficient planning and investment. The Broward Metropolitan Planning Organization (BMPO) and its regional partners face obstacles in coordination and modeling, reducing their effectiveness. Addressing these issues requires the integration of multiple regional systems and models. SMART METRO is a scalable prototype platform designed to tackle these challenges through advanced systems integration. The platform enables comprehensive transportation and land use planning by leveraging the following capabilities: (1) An Integrated Digital Twin Platform: SMART METRO serves as a scalable prototype that unifies transportation, land use, demographic, and extreme weather data within a single interface. (2) Advanced Analytics and AI: The platform utilizes data science, geospatial analytics, artificial intelligence (AI), and simulation tools to support detailed analysis and scenario modeling of infrastructure. (3) Data-Driven Decision Making: SMART METRO empowers stakeholders to evaluate and compare strategies, ensuring that resources are allocated to the most effective solutions. Key Partners include the City of Miramar, New York University (NYU) GovLab, University of Florida, Google, UrbanSim, and Deloitte.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:11:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717171</guid>
    </item>
    <item>
      <title>Connected Work Zones Implementation Guide and Standard v01.00: Guidance for Setting Up and Operating a Connected Work Zone</title>
      <link>https://trid.trb.org/View/2716613</link>
      <description><![CDATA[This document identifies the Connected Work Zone (CWZ) deployer needs, sets the requirements, and provides guidance for nationally interoperable connected work zones across the United States. The focus of this document is on system-to-system interfaces to enable interoperable CWZ applications. This document is envisioned as a living document. In addition to this Executive Summary, this document contains six main sections, as follows: General Information, Concept of Operations, System Interface Requirements, System Interface Design Details: Data Exchange Dialogs, System Interface Design Details: Data Concepts, and Connected Work Zones Testing.]]></description>
      <pubDate>Wed, 24 Jun 2026 17:03:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716613</guid>
    </item>
    <item>
      <title>A Real-Time Monitoring and Rapid Warning Method for Guardrail Collisions in Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2706218</link>
      <description><![CDATA[This article proposes a method for real-time monitoring and rapid alert for guardrail collisions based on Distributed Acoustic Sensing (DAS). The aim is to enhance traffic safety through continuous analysis of vibration signals. To achieve this, a system architecture that combines both hardware and software design has been developed, enabling the handling of the entire process from signal acquisition and decoding to intelligent event recognition and visualization. To improve signal reliability, an adaptive noise reduction algorithm and a multi-level feature extraction method are introduced, enabling accurate differentiation between collision events and environmental disturbances. Tests at various vehicle speeds show that the DAS-based system detects collisions with over 98% accuracy and cuts false alarms by more than 60% compared to traditional video and point-sensor monitoring. It can locate accidents with an average error of 4.2 meters and respond in under 1 second, demonstrating both its accuracy and speed. These results confirm the method’s effectiveness and reliability for enhancing transportation safety.]]></description>
      <pubDate>Mon, 22 Jun 2026 07:29:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706218</guid>
    </item>
    <item>
      <title>High Step-Up Dual Coupled-Inductors Interleaved Quadratic DC–DC Converter With Common Ground and Low Input Current Ripple for Fuel Cell Electric Vehicles</title>
      <link>https://trid.trb.org/View/2665606</link>
      <description><![CDATA[In this article, a novel interleaved quadratic dc–dc converter with dual coupled inductors has been proposed for fuel cell electric vehicles (FCEVs). The proposed converter is generated by cascading two boost converters and integrating a coupled-inductor multiplier cell and an output filter. The quadratic voltage gain is achieved and it can be increased by the turns ratio without the need of extreme duty cycle. The output filter capacitor is clamped at the input to achieve an input and output common ground. Furthermore, the two primary windings and the load are connected at the input to share the current, and very low input current ripple is achieved by using interleaved control. Diode-capacitor branch of the topology is multiplexed as the passive clamp branch to effectively reduce the voltage stress of the switches. The currents of the switching devices are limited by the leakage inductance during the mode conversions, which effectively alleviates the hard-switching problem. This article also gives the key performance of the proposed converter and compares it with other high step-up converters. Finally, a 400-W prototype is built to verify the theoretical analysis.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665606</guid>
    </item>
  </channel>
</rss>