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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>An Active RS-449/RS-232C Adapter for RMMS</title>
      <link>https://trid.trb.org/View/2717070</link>
      <description><![CDATA[The purpose of this activity was to develop an active adapter to interface RS-449 devices to RS-232C devices. This active adapter will be used in Remote Maintenance Monitoring System (RMMS) activities at the Technical Center and as a functional prototype for possible future procurement.]]></description>
      <pubDate>Wed, 08 Jul 2026 10:02:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717070</guid>
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    <item>
      <title>Remote Maintenance Monitoring System Concentrator</title>
      <link>https://trid.trb.org/View/2714053</link>
      <description><![CDATA[A Remote Maintenance Monitoring System (RMMS) concentrator has been designed, developed, and tested at the Federal Aviation Administration (FAA) Technical Center. The concentrator is a microcomputer-based device that collects, stores, displays, and retransmits to a Maintenance Processor Subsystem, performance information obtained from many remote navigational aid monitors. The concentrator consists of a communications subsystem and a data subsystem. Due to its design features, the concentrator may be reconfigured to handle several RMMS tasks. By incorporating operating system software into the data subsystem, the concentrator becomes a low-cost, general-purpose minicomputer.]]></description>
      <pubDate>Tue, 07 Jul 2026 17:29:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714053</guid>
    </item>
    <item>
      <title>Centimeter-Level GNSS Positioning Using C-ITS for Correction Data Delivery: An Experimental Study</title>
      <link>https://trid.trb.org/View/2671776</link>
      <description><![CDATA[High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use correction data. This data is typically delivered via 4G mobile internet. In this paper, we present a novel method to deliver the data via C-ITS (Cooperative Intelligent Transport Systems and Service). We compare its performance against using 4G and analyze the impact on the accuracy during data gaps, all using data collected in test drives from a real deployment in a small segment of a motorway. The results show that with C-ITS, a comparable performance can be achieved. The observed 2D position errors in our tests were below 3.1 cm for 95% and below 10 cm for more than 99% of the time.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671776</guid>
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    <item>
      <title>EALU-AER: Enhanced Automation for U-Space/ATM Integration</title>
      <link>https://trid.trb.org/View/2671772</link>
      <description><![CDATA[EALU-AER is a proposed scalable U-Space ecosystem technology infrastructure integration and demonstration project to establish Ireland’s first Digital Sky Demonstrator (DSD), enabling the smooth transition towards smart cities and is centered at Future Mobility Campus Ireland’s (FMCI) recently established vertiport site, in the vicinity of Shannon Airport, Ireland, therefore inside controlled airspace. The project focusses on deploying a reliable infrastructure, catering U1 and U2 services with higher levels of performance, refinement, and integration to enhanced levels of automated interface with the ATM/ATC, aiming to drive and support U-space regulations and standards development along with global interoperability between Uspace and ATM in a cross-border dimension. This allows for a safe and co-operative integration of zero-emission drones into the airspace that will perform different Urban Air Mobility (UAM) missions. The infrastructure will be facilitated by a mature USpace Services Provider (USSP) platform (WebUAS), a backhaul network for secure data exchange within the ecosystem (ARINC Ground Network (AGN)), Command and Non-payload Communication (CNPC) ground solution (CNPC 5000) and advanced three-dimensional ground surveillance RADAR. The project builds on the reliable integration of these technologies, to provide an autonomous, connected, and collaborative ecosystem ready for U3 and U4 services provision. In this paper, we build the case for the need of such efforts, provide a deep dive into the solution structure the project proposes and describe the operations planned, to validate the system architecture and infrastructure put in place for the same.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671772</guid>
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    <item>
      <title>6G-Enabled Intelligent Healthcare Transport Systems: Framework and Resource Allocation Strategy</title>
      <link>https://trid.trb.org/View/2617912</link>
      <description><![CDATA[Integrating 6G networks with emergency medical services (EMS) transportation aims to transform patient care during transit, yet maintaining reliable communication for mobile medical units poses technical hurdles. This paper introduces an intelligent reconfigurable surface-assisted healthcare transport system for the 6G (IRIS-HT6G) framework. We leverage reconfigurable intelligent surfaces to enhance communication links between healthcare vehicles, roadside units, and remote medical facilities. Our framework jointly optimizes RIS phase shifts, power allocation, and spectrum sharing to maximize system capacity while ensuring the reliability of critical healthcare data transmission. Simulation results demonstrate the superiority of IRIS-HT6G over state-of-the-art baseline methods. The proposed framework achieves up to 30% higher link capacity, 50% lower latency, and significantly improved reliability in non-line-of-sight scenarios. Further, based on a 30-day trial involving 15 ambulances serving an urban population of 500000, IRIS-HT6G achieved tangible improvements in EMS operations. Emergency response times decreased from 12.5 to 10.0 minutes through better fleet coordination. Patient care continuity during transport improved from 76% to 98% uptime, enabling uninterrupted vital sign monitoring and ECG transmission.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617912</guid>
    </item>
    <item>
      <title>Socially-Inspired Semantic Communication Codec Updating for NTN-Enabled Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2617905</link>
      <description><![CDATA[In navigating the challenges of real-time semantic communication (SC) codec updates in the 6G-era non-terrestrial network (NTN)-assisted vehicular networks (NTN-VNs), a crucial component of intelligent transportation systems (ITS), this article introduces a novel approach inspired by human society. Facing complexities like 3-dimensional updating, network dynamism, and updating costs, NTN-VNs are treated as social networks. The proposed NTN-VN federated learning (NTN-VN-FL) framework asynchronously addresses challenges such as uplink and downlink SC codec updates, device decentralization, and asynchronous updating. By viewing device behaviors during updating as social behaviors with economic costs, an NTN-VN social management system ensures the proper functioning of the social network in the context of NTN-VN-FL. An economical social behavior selection mechanism, based on the reverse auction game for NTN-VN-FL, minimizes training delay and device energy costs, considering social relationships. The article also presents a two-stage Stackelberg game with the Vickrey auction rule to maximize social welfare in the auction. Simulation results highlight the superiority of NTN-VN-FL over existing potential application algorithms, effectively addressing the unique challenges of SC codec updating in NTN-VN. The efficacy of the social management system and social behavior selection mechanism is demonstrated in achieving optimal outcomes.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617905</guid>
    </item>
    <item>
      <title>Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks</title>
      <link>https://trid.trb.org/View/2617901</link>
      <description><![CDATA[The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocation. This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges on the way of realizing autonomous driving in vehicular edge networks. U-SFL is able to enhance privacy protection by keeping both raw data and labels on the vehicular user (VU) side while enabling parallel processing across multiple vehicles. To optimize communication efficiency, we introduce a semantic-aware auto-encoder (SAE) that significantly reduces the dimensionality of transmitted data while preserving essential semantic information. Furthermore, we develop a deep reinforcement learning (DRL) based algorithm to solve the NP-hard problem of dynamic resource allocation and split point selection. Our comprehensive evaluation demonstrates that U-SFL achieves comparable classification performance to traditional split learning (SL) while substantially reducing data transmission volume and communication latency. The proposed DRL-based optimization algorithm shows good convergence in balancing latency, energy consumption, and learning performance.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617901</guid>
    </item>
    <item>
      <title>Fly, Sense, Compress, and Transmit: Satellite-Aided Airborne Secure Data Acquisition in Harsh Remote Area for Intelligent Transportations</title>
      <link>https://trid.trb.org/View/2617899</link>
      <description><![CDATA[Satellite-aided airborne systems can enable data acquisition in remote areas for the intelligent transportation systems (ITS), leveraging the satellite coverage alongside the mobility and multifunctional capabilities of autonomous aerial vehicles (AAVs). However, due to the harsh environment, ensuring secure and timely task execution is complicated by uncertainties related to both channel conditions and eavesdropping threats. This paper proposes a two-stage optimization method to fully exploit AAVs’ flying, sensing, compressing, and transmitting capabilities for secure data acquisition under dual uncertainties. In the first stage, a deep reinforcement learning strategy is employed to optimize sensing and trajectory planning to explore the eavesdropping environment and balance computational and transmission demands. Building on the sensed information about the eavesdropping environment, the second stage focuses on minimizing task completion time through optimal resource allocation and hierarchical A* path planning, with the channel uncertainty addressed by incorporating an outage probability constraint. Simulations demonstrate that the proposed method can reduce data completion time by 35.3%, validating its effectiveness in uncertain environments.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617899</guid>
    </item>
    <item>
      <title>A Multi-Agent Federated DRL Model for Vehicular Task Offloading in WPT-Aided eROAD Environment</title>
      <link>https://trid.trb.org/View/2617891</link>
      <description><![CDATA[This paper introduces a novel multi-agent federated deep reinforcement learning (MA-FDRL) framework designed to minimize vehicular task offloading latency in electrified road (eROAD) environments. The solution integrates inductive coil-based wireless power transfer (WPT) systems with full duplex multiple input and multiple output (MIMO) vehicular networks, enabling continuous charging and reducing computational delays for electric vehicles (EVs) on eROADs. The MA-FDRL framework optimizes the offloading of vehicular tasks, with support from base stations (BS), unmanned aerial vehicles (UAVs), and satellites, while ensuring data privacy through differential privacy techniques. By intelligently distributing resources across these supporting entities, the framework enhances the efficiency of task processing. Key challenges such as dynamic wireless charging, intermittent BS coverage, and privacy-preserving task offloading are addressed using a comprehensive WPT framework, a mobility model, and a differential privacy-enhanced MA-FDRL algorithm. The proposed MA-FDRL solution effectively reduces the latency of vehicle task offloading by 17.05% over proximal policy optimization (PPO) algorithm, ensures balanced task distribution between edge servers, and offers a scalable and privacy-preserving approach for future autonomous electric vehicles and connected wireless environments.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617891</guid>
    </item>
    <item>
      <title>Accelerating Resource-Constrained Swarm Robotics With Cone-Based Loop Closure and 6G Communication</title>
      <link>https://trid.trb.org/View/2617887</link>
      <description><![CDATA[Loop closure detection, a critical component of Simultaneous Localization and Mapping (SLAM) systems, can be computationally intensive, particularly in swarm robotics where coordination among multiple agents is essential. Traditional SLAM methods often involve comparing each frame with all previous frames, leading to performance bottlenecks, especially on battery-operated or resource-constrained devices. This leaves little room for other critical tasks, such as continuous coordination and information sharing among swarm robots, which require swift execution to perform effectively. This paper introduces a novel cone-based approach to streamline loop closure detection, significantly reducing the computational burden and improving system efficiency. By limiting frame comparisons to a predefined region, our method accelerates SLAM algorithms, enabling more efficient coordination and exploration among swarm robots. The approach is particularly advantageous for resource-constrained devices and battery-powered platforms operating in dynamic environments. The need for millisecond-level response times in swarm robotic tasks require the integration of 6G networks for seamless communication and coordination. Experimental results demonstrate the effectiveness of the cone-based method in enhancing loop closure accuracy while minimizing computational overhead. This makes it a valuable tool for advancing swarm robotic applications, particularly in 6G-enabled environments where real-time coordination and efficiency are paramount.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617887</guid>
    </item>
    <item>
      <title>Reinforcement Learning Based Edge-End Collaboration for Multi-Task Scheduling in 6G Enabled Intelligent Autonomous Transport Systems</title>
      <link>https://trid.trb.org/View/2617883</link>
      <description><![CDATA[As communication and computing technologies advance, vehicular edge computing emerges as a promising paradigm for delivering a wide array of intelligent services in 6G enabled Intelligent Autonomous Transport Systems. These service requests, are safety-oriented and typically require the fusion of processing results from multiple independent computation tasks generated by various onboard sensors, in which the computation tasks are delay-sensitive and computation-intensive. Consequently, the allocation of multiple tasks within a single service request while efficiently reducing request completion time and energy consumption presents a substantial challenge. In order to address the problem of multi-task simultaneous scheduling, this paper proposed to employ deep reinforcement learning and edge computing architecture to make task scheduling decisions for vehicles. Firstly, the Vehicle-Infrastructure Network (VINET) is designed, in which the vehicles can assign multiple tasks to the edge servers and other idle vehicles, thus extending the task processing capabilities for vehicles. Secondly, Fully-decentralized Multi-agent Proximal Policy Optimization (FMPPO) algorithm is proposed to make task scheduling decisions for autonomous driving, the large model trained via FMPPO is adaptable to different scenarios with various numbers of vehicles. Thirdly, by taking into account task characteristic, environmental status, and vehicle mobility, the proposed method can make task scheduling decisions in real-time and then dynamically distributes tasks based on the decisions. Finally, experimental results demonstrate that the designed method outperforms benchmark methods in terms of both completion time and energy consumption of computation tasks.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617883</guid>
    </item>
    <item>
      <title>Enhanced Security Index Modulation for STAR-RIS Aided Intelligent Autonomous Transport Networks</title>
      <link>https://trid.trb.org/View/2617879</link>
      <description><![CDATA[As a promising technology, simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed to improve the transmission quality and coverage, which is considered to be widely applied to intelligent automated transportation (IAT) systems. However, due to the open environment and 360° omnidirectional transmission, security issues have always been a major difficulty hindering the implementation of STAR-RIS aided IAT systems. To tackle this challenge, we propose an enhanced security index modulation (ESIM) scheme in this paper, which combines well-known IM and higher-order linear decoding quasi-orthogonal space-time block coding (LD-QO-STBC) techniques to improve the system security performance. Specifically, the information bits and transmit antennas are organized into four distinct sets, with each subset of antennas activated by individual IM. Subsequently, the STAR-RIS is employed to meticulously craft the LD-QO-STBC scheme, which involves determining the phase of the unmodulated carrier. In a strategic move to thwart eavesdropping attempts, the eavesdroppers are subjected to continuously varying and disruptive continuous artificial noise (CAN) and is thus unable to reliably decode the transmitted information, which collectively achieves secure communications amidst potential eavesdropping threats. In addition, the theoretical analysis of both the bit error rate (BER) and secrecy capacity are derived to explore the potential of ESIM-LD-QO-STBC. Numerical results reveal that our proposed method excels in achieving a substantial diversity gain while maintaining low computational complexity. Furthermore, our proposed scheme enhances secrecy capacity by at least 50%, offering a marked improvement over conventional physical layer security (PLS) schemes.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617879</guid>
    </item>
    <item>
      <title>Graph Convolutional Reinforcement Learning-Guided Joint Trajectory Optimization and Task Offloading for Aerial Edge Computing</title>
      <link>https://trid.trb.org/View/2617872</link>
      <description><![CDATA[The unique capabilities of Unmanned Aerial Vehicles (UAVs), including their superior mobility, flexibility, and line-of-sight transmission, have made them well-suited for facilitating Aerial Edge Computing (AEC). This computing paradigm is particularly beneficial for meeting the computing demands of User Equipments (UEs) in emergency situations, as it offers efficient support for task offloading. Considering the service requirements of UEs, it is essential to minimize the processing delay experienced by UEs in AEC systems. This is accomplished through the joint optimization of the UAV trajectory, flight speed, and task offloading ratio allocation for UEs. Due to the non-convex nature and the continuous action space of the problem, recent studies have turned to the Deep Deterministic Policy Gradient (DDPG) to tackle similar challenges. However, Deep Neural Networks (DNNs) employed in DDPG are limited to extracting latent information solely from Euclidean data, and are similarly constrained by the highly dynamic changes in channel states within AEC networks, thereby disregarding the valuable features inherent in the structural information. In order to alleviate the task offloading problem in AEC systems, we propose a novel Graph Convolutional Pooling-DDPG (GCP-DDPG) algorithm by exploiting the graph-based multi-relational derivation capability of the multi-Relational Graph Convolutional Network (R-GCN) and employing the reinforcement learning technique. Extensive simulation experiments are conducted to evaluate the superiority and effectiveness of the GCP-DDPG algorithm. The results demonstrate a remarkable performance improvement of 34.6% compared to state-of-the-art approaches.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617872</guid>
    </item>
    <item>
      <title>Joint Resource Allocation for V2X Communications With Multi-Type Mean-Field Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2617870</link>
      <description><![CDATA[Vehicle-and-infrastructure cooperation is emerging in vehicle-to-everything (V2X) communication to increase traffic efficiency and road safety with advanced services. Static infrastructures like roadside units (RSUs) have the potential to provide stable and high-quality communication services, but suffer overload problems caused by uneven spatiotemporal distribution of vehicles. Unmanned aerial vehicles (UAVs) with high flexibility can establish the line-of-sight (LoS) links but require extra scheduling overheads. Furthermore, the scarce spectrum resources, complex interference, limited energy budgets, and the mobility of automobiles also pose significant challenges. In this paper, we combine mean-field game (MFG) theory with multi-agent reinforcement learning (MARL) to allocate resources for heterogeneous infrastructures in non-orthogonal multiple access (NOMA) V2X communication networks. First, a joint sub-band scheduling, transmit power allocation, and UAV deployment problem is addressed, aiming to jointly optimize communication resources for heterogeneous infrastructures under power and QoS constraints. Subsequently, considering the differences among nodes, multi-type agents are designed and applied MARL to get self-learning ability and collaboration. Moreover, MFG theory is employed to tackle the tremendous overhead caused by agent interactions in MARL. Finally, simulation results demonstrate that our proposed method outperforms two state-of-the-art resource allocation schemes in both average energy efficiency and probability of failure.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617870</guid>
    </item>
    <item>
      <title>An Adaptive Computing Offloading and Resource Allocation Strategy for Internet of Vehicles Based on Cloud-Edge Collaboration</title>
      <link>https://trid.trb.org/View/2617862</link>
      <description><![CDATA[With the development of the Internet of Vehicles (IoV) industry, the introduction of cloud-edge collaboration has greatly enhanced the computing capabilities of vehicle networks. However, optimizing computing offloading and resource allocation strategies in IoV to reduce latency and energy consumption at the vehicle terminals remains a challenge. This paper proposes an Adaptive Computing Offloading and Resource Allocation Strategy (ACORAS) for IoV based on cloud-edge collaboration. Firstly, a Vehicles-Collaborative Road Side Units-Cloud (VCRSUC) system architecture is constructed by considering the use of idle resources on edge servers at remote Road Side Unit (RSU) to reduce the total cost at the vehicle terminals. Secondly, the discrete particle swarm optimization algorithm is combined with chaotic mapping and Cauchy mutation, and dynamically adjusts weights and learning factors based on variable updates. Finally, our proposed ACORAS gradually approaches the optimization of the computing offloading decisions and resource allocation decisions through iterative calculations. Simulation results show that our proposed ACORAS can effectively reduce the total cost while considering latency and energy consumption, demonstrating superior performance compared to traditional algorithms.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617862</guid>
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