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    <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" />
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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Performance-Based Management Approaches for Maintenance: A Guide</title>
      <link>https://trid.trb.org/View/2772578</link>
      <description><![CDATA[This report presents a guide for assessing the readiness of performance-based management approaches to support maintenance budgeting activities and personnel. The development of this guide was based on a methodology that incorporated stakeholder interviews, case studies, and a peer review workshop. This guide will be of immediate interest to highway maintenance engineers, asset managers, and performance managers seeking to address decision-making challenges associated with maintenance quality assurance programs.]]></description>
      <pubDate>Sat, 05 Sep 2026 11:31:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772578</guid>
    </item>
    <item>
      <title>Computational Intelligence and Automation for Resilient Bridge Infrastructure</title>
      <link>https://trid.trb.org/View/2705489</link>
      <description><![CDATA[Bridges are essential assets in transportation systems, and their failure or prolonged closure can disrupt emergency response, supply chains, and daily mobility. Aging bridge inventories face increasing demands from traffic growth, environmental exposure, and extreme events, making reliable in-service assessment and maintenance a structural engineering priority. This paper reviews how computational intelligence and automation are being used to support bridge condition evaluation and intervention decisions during the in-service phase by improving monitoring, inspection, damage assessment, and maintenance decision-making. A PRISMA-guided search was conducted in the Scopus database using title, abstract, and keyword terms related to bridges, computational intelligence, automation, and in-service operation. The initial search returned 109 records; after restricting the results to English-language journal articles published between 2000 and 2025 and screening for bridge-specific studies that applied computational intelligence and/or automation technologies to in-service monitoring, inspection, damage assessment, deterioration prediction, or maintenance decision-making, 37 studies were retained for combined scientometric and systematic analysis. The literature is organized into five application areas: structural health monitoring, damage detection and condition assessment, predictive maintenance and optimization, robotic and autonomous inspection, and post-event decision support. The review shows a clear rise in research activity since 2023, with deep learning leading advances in time-series monitoring and vision-based inspection, while unmanned and robotic platforms are making data collection safer and faster. However, most studies still rely on limited validation data, inconsistent performance reporting, and weak links between detected defects and decisions about service continuity, operability, and recovery. From a resilience perspective, these developments are most valuable when they reduce the time required to detect damage, classify safety and functionality, and prioritize interventions following disruptive events (e.g., floods, earthquakes, hurricanes, and climate-driven extremes). The review therefore interprets computational intelligence and automation not only as tools for routine condition assessment, but also as enablers of resilience-based management that supports continuity of transportation service, faster recovery, and risk-informed allocation of limited maintenance resources. Key future directions include uncertainty-aware models that translate detected defects into performance and functionality loss, validated workflows for rapid post-event screening, and integration with network-level decision-making for resilient cities and infrastructure systems.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:37:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705489</guid>
    </item>
    <item>
      <title>Pavement performance prediction via a tabular foundation model</title>
      <link>https://trid.trb.org/View/2705452</link>
      <description><![CDATA[This study investigates the applicability of tabular foundation models to pavement performance prediction under small, imbalanced, and partially missing infrastructure datasets. Using Japan’s National Road Facility Inspection Database, we analyze 189 pavement segments along a 24.48 km section of National Route 6. The task is to forecast a three-level categorical performance condition state (Sound/Monitor/Repair) at the second inspection conducted five years after the first inspection, using inventory attributes and first-inspection records only. We apply TabPFN (Tabular Prior-Data Fitted Network) in a zero-shot manner (no dataset-specific training or hyperparameter tuning) under an order-based fold design that reflects an operational scenario where only a subset of segments is inspected and the remainder is inferred. With two folds, TabPFN achieves high predictive performance (Accuracy=0.926; Macro-F1=0.876) while maintaining strong detection of the minority class (Repair). In contrast, five supervised baselines, logistic regression, random forests, histogram-based gradient boosting, XGBoost, and CatBoost, show weaker minority-class detection, with Accuracy ranging from 0.878 to 0.905 and Macro-F1 from 0.606 to 0.841. We also assess the reliability of TabPFN’s predictive probabilities and observe that misclassifications concentrate in low-confidence ranges, highlighting their utility as uncertainty-aware outputs. In addition, based on the high predictive performance results of TabPFN, we discuss the practical potential for a 25% inspection workload reduction under a partial-inspection deployment scenario. Finally, a missing-data experiment reveals a notable degradation trend in predictive performance as the missing rate increases. These findings highlight both the practical potential and key deployment considerations of tabular foundation models for pavement asset management.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:37:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705452</guid>
    </item>
    <item>
      <title>Prototyping Automated Framework for Asset Extraction and Characterization from Mobile Lidar Data</title>
      <link>https://trid.trb.org/View/2752293</link>
      <description><![CDATA[To be able to handle the statewide mobile lidar data ODOT collects efficiently, there is a need for a robust workflow with significant automation that can extract many types of features by effectively leveraging various feature extraction tools and algorithms to support applications. In particular, road characterization has been identified as a key application for information extraction from mobile lidar data. In this literature review, the team first provides a brief overview of the model inventory of roadway elements (MIRE 2.0) to help identify the attributes that are of interest as well as feasible to extract from mobile lidar data. Next, a review of the existing commercial software summarizes the common challenges in using these solutions for feature extraction and characterization tasks. Lastly, the existing work related to road characterization is reviewed covering the topics of ground filtering and road extraction, cross slopes and grades, and horizontal curve, followed by a summary of the benefits of using mobile lidar data as well as the challenges of the existing studies.]]></description>
      <pubDate>Wed, 02 Sep 2026 09:20:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752293</guid>
    </item>
    <item>
      <title>A Gated Transformer MADDPG Algorithm for Latency and Energy Aware Task Offloading in Digital Twinning Aerial Edge Computing</title>
      <link>https://trid.trb.org/View/2659615</link>
      <description><![CDATA[Unmanned aerial vehicles (UAVs) have seen breakthroughs in forming Aerial Edge Computing (AEC), which executes computationally intensive tasks generated by Internet of Things (IoT) devices, thanks to their ease of deployment, especially in scenarios where traditional terrestrial base stations are damaged and unable to process tasks due to natural disasters. However, an AEC faces significant challenges due to the limited battery capacity of UAVs and the need for efficient collaboration among them to execute tasks. Existing studies often overlook fine-grained task prioritization and balanced load distribution across UAVs, leading to inefficiencies in energy usage and service delay. In this paper, we have developed an optimization framework for efficiently offloading computationally intensive IoT tasks in a three-stage Digital Twin-enabled multi-UAV-based AEC network environment, which jointly minimizes service latency and energy consumption while ensuring the expected load distribution among the UAVs. The formulated framework is a Mixed-Integer Nonlinear Programming (MINLP) problem, which is inherently NP-hard. To address this, we design GLEMATO, a scalable GTrXL-assisted MADDPG framework that learns high-quality offloading policies through memory-aware task prioritization and cooperative multi-agent decision-making in dynamic AEC scenarios. In GLEMATO, while the GTrXL model ensures adaptive task prioritization by considering factors such as task generation time, energy budget, and application deadlines, while the MADDPG enables decentralized policy learning through sharing cooperative state–actions among UAVs. The experimental results, carried out on the OpenAI Gym simulator platform, demonstrate that the developed GLEMATO framework reduces average energy consumption and service latency by 21.8% and 23.3%, respectively, and increases the average task completion ratio by up to 20.1% for computationally intensive tasks compared to the state-of-the-art approaches.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659615</guid>
    </item>
    <item>
      <title>Task offloading based on lightweight identity authentication and genetic optimization for the internet of vehicles</title>
      <link>https://trid.trb.org/View/2667036</link>
      <description><![CDATA[Task offloading ensures low-latency responsiveness for computation-intensive Internet of Vehicles applications by dynamically distributing workloads across vehicle, edge, and cloud resources. However, due to the dynamicity of vehicle networking environment, open access characteristics, and complex interactions between vehicles and servers, existing offloading methods face dual challenges of security threats and insufficient optimization efficiency. To address this, a task offloading scheme based on lightweight identity authentication and genetic optimization is proposed in this paper. First, we design an anonymous authentication mechanism based on elliptic curves, combined with pseudo-identity generation, verifiable signatures, and timestamp technology. It ensures the privacy of vehicles while supporting malicious node tracking, thereby guaranteeing the trustworthiness of nodes participating in task offloading. After that, an improved genetic optimization model is proposed, integrating elite retention strategy, multi-point crossover-mutation operations, and resource allocation penalty functions to dynamically adapt to vehicle mobility and server resource states, achieving globally optimal offloading decisions. Finally, extensive experiments demonstrate that the proposed scheme significantly outperforms the baseline methods in terms of secure signature efficiency, authentication speed, and task processing performance. It reduces task latency by 6.29%-34.14%, and reduces energy consumption by 9.54%-35.36%.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667036</guid>
    </item>
    <item>
      <title>Seismic resilience assessment and multi‑phase optimization method of regional road networks for earthquake rescue</title>
      <link>https://trid.trb.org/View/2737027</link>
      <description><![CDATA[High-resilience road networks are critical for improving rescue efficiency and reducing seismic losses. Existing studies have proposed effective seismic resilience assessment and optimization methods for single phases (pre-earthquake, during-earthquake, post-earthquake). However, the improvement mechanism and effectiveness of multi‑phase resilience optimization framework remain understudied, which limits the construction of high‑resilience road networks. In this context, this study proposes a seismic resilience assessment and multi‑phase optimization method of regional road networks for earthquake rescue. Firstly, a multi–state damage model of road networks is developed by considering complex topological connected relations and multi–state damage characteristics of road components. Then, a probabilistic method is proposed for multi-state road network performance evaluation. This performance is quantified via total travel time for rescue demand. After that, a resilience assessment framework encompassing resistance, adaptation, and recovery capacities is established by linking the rescue process to network performance. Based on this framework, a multi–phase resilience optimization model is formulated to maximize expected resilience under seismic uncertainty by integrating pre–earthquake reinforcing, during–earthquake monitoring, and post–earthquake repairing strategies. Finally, the proposed method is demonstrated using a real regional road network and more than 300,000 simulated scenarios. Under consistent cost constraints, the resilience improvement effect of the multi–phase combined optimization strategy is significantly better than any single–phase strategies. Crucially, the resilience improvement of this combined optimization strategy proves robust across diverse uncertain scenarios. This system–level resilience improvement can be explained by complementary and distinct improvement mechanisms associated with each phase. This provides theoretical and methodological support for planning high–resilience road networks and multi–phase resilience optimization strategy.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737027</guid>
    </item>
    <item>
      <title>Physics-informed multi-agent deep reinforcement learning for dynamic emergency resource scheduling in marine oil spills</title>
      <link>https://trid.trb.org/View/2742978</link>
      <description><![CDATA[An efficient marine oil spill response is essential for mitigating environmental impacts. However, dynamic ocean conditions and uncertain spill evolution make timely and flexible emergency resource scheduling challenging. This study proposes a physics-informed multi-agent deep reinforcement learning framework for maritime oil spill response. The framework integrates oil evolution processes and environmental dynamics into the decision-making environment, enabling agents to adapt scheduling strategies under changing spill conditions. A multi-objective reward mechanism is developed to balance recovery performance, response time, and resource utilization. Simulation experiments demonstrate that the proposed MADDPG-based method consistently outperforms benchmark approaches across multiple objectives. Furthermore, the framework is validated using a real-world Bohai Sea oil spill scenario, demonstrating its applicability under realistic marine conditions. Multi-seed experiments show stable performance with a low coefficient of variation of 2.7% for the Overall Performance Index, confirming the robustness and reliability of the proposed approach for intelligent oil spill emergency response.]]></description>
      <pubDate>Tue, 01 Sep 2026 14:02:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742978</guid>
    </item>
    <item>
      <title>Strategic Resilience of Emergency Medical Transport Network Design in Public Health Crises</title>
      <link>https://trid.trb.org/View/2714329</link>
      <description><![CDATA[Recent frequent outbreaks of seasonal infectious diseases have surged emergency medical demand in large cities, causing hospital influxes and exacerbating road congestion. To address this, this study proposes a continuous emergency dedicated lane deployment strategy centered around urban hospitals to segregate rescue and civilian traffic. Adopting a macroscopic perspective, a bilevel programming model (lane reservation problem—complex emergency lane) was developed to minimize the total dedicated lane length and ambulance travel time. The lower-level model integrates lane counts with the Dijkstra algorithm to ascertain the shortest time path. Furthermore, a genetic algorithm with repair strategies (GA-R) was introduced to solve the model effectively. Case studies demonstrate that the GA-R algorithm reduced the average travel time from hospitals to emergency points by approximately 15%, verifying the utility of dedicated lanes in enhancing the efficiency of emergency medical response systems.]]></description>
      <pubDate>Tue, 01 Sep 2026 09:10:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714329</guid>
    </item>
    <item>
      <title>Secure State of Charge Estimation for Lithium-Ion Battery: An Impulsive-Driven-Based Observer Approach</title>
      <link>https://trid.trb.org/View/2685989</link>
      <description><![CDATA[The overcharge or overdischarge of a battery energy storage system (BESS) can be manipulated by cyber attacks, posing a severe threat to battery safety and reliability. Previous works have not adequately addressed the impact of such attacks on the state-of-charge (SOC) estimation, which motivates this study to develop a secure estimation method for lithium-ion batteries (LIBs). In this article, an impulsive-driven-based observer (IDBO) is proposed to improve communication efficiency by reducing data transmission requirements, enhancing resilience against cyber attacks, and ensuring reliable SOC estimation under nonideal scenarios. The Lyapunov function approach is employed to derive sufficient conditions that guarantee the uniform boundedness of the estimation error. In addition, a modified battery model is constructed to explicitly incorporate disturbances from measurement noise, parameter uncertainties, and cyber attacks. An extensive experiments are conducted under diverse driving cycles and cyber attack scenarios. The results show that the proposed method achieves an average RMSE of only 1.26% across three representative driving cycles, which is lower than that of conventional observer-based SOC estimation methods under the same attack scenarios, thereby highlighting strong estimation accuracy and robustness. The findings confirm that the proposed IDBO ensures accurate, robust, and efficient SOC estimation for secure battery management.]]></description>
      <pubDate>Mon, 31 Aug 2026 16:43:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685989</guid>
    </item>
    <item>
      <title>Advancing Climate Resilience in Transportation: The BC Ministry of Transportation &amp; Transit Resilience System: PIARC Prizes - Resilience</title>
      <link>https://trid.trb.org/View/2711623</link>
      <description><![CDATA[British Columbia’s transportation network is increasingly threatened by climate change, as shown by recent extreme weather events that have damaged infrastructure and disrupted key corridors. With limited funding and a vast inventory of roads, bridges, and culverts, the BC Ministry of Transportation and Transit (MoTT) recognizes that traditional, asset-by-asset adaptation is no longer viable.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711623</guid>
    </item>
    <item>
      <title>Smart Infrastructure-Based Anomaly Detection under Cyberattacks</title>
      <link>https://trid.trb.org/View/2709395</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) are increasingly exposed to cyberphysical attacks, yet most existing detection methods overlook the potential of infrastructure-based monitoring. In this article, we propose a novel infrastructure-based anomaly detection framework to identify cyberattacks on CAVs under time interference attacks and vehicle-to-everything communication attacks. The optimal attack strategies are generated using a Pareto optimization that jointly maximizes safety risk and stealthiness. To detect the attacks, a transformer-based trajectory prediction model is developed to predict normal driving behaviors. An XGBoost classifier is then developed to detect anomalies by comparing predicted and observed vehicle trajectories. We validate the proposed framework using real-world trajectory data. The results show that the proposed anomaly detection model achieves high accuracy with low false positive and false negative rates in both offline and online settings. These results demonstrate that smart infrastructure can significantly improve the safety of CAVs by enabling timely and accurate detection of cyberattacks.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709395</guid>
    </item>
    <item>
      <title>Privacy-Preserving Location-Based Service in Internet of Vehicles Via Oblivious Transfer in Zero-Trust Framework</title>
      <link>https://trid.trb.org/View/2761411</link>
      <description><![CDATA[The dynamism and heterogeneity inherent to the internet of vehicles necessitate its operation within a zero-trust environment. Serving as a vital element in location-based services (LBSs), geographic information is imperative to ensure the proper functioning of navigation, traffic updates, courier service, etc. Location data can potentially reveal extensive personal information of a user, such as social relationships, habits, and health conditions. To alleviate the conflicts amongst privacy, data property, and real-time performance for an LBS in the Internet of Vehicles, a secure point-of-interest information retrieval scheme is presented in this paper. We developed a lightweight fully homomorphic encryption algorithm based on the hardness of the discrete logarithm conjugacy search problem (DLCSP) and implemented it as an oblivious transfer (OT) protocol that not only conceals the query but also safeguards the data assets of the server. The effectiveness of our proposed method was validated experimentally. The results showed that our scheme outperformed state-of-the-art methods in terms of both computation and communication overhead, particularly on the server side. In addition, a simulation-based security validation demonstrated that our scheme is more robust when defined as an OT.]]></description>
      <pubDate>Mon, 31 Aug 2026 08:50:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761411</guid>
    </item>
    <item>
      <title>Maneuverable-Jamming-Aided Secure Communication and Sensing in A2G-ISAC Systems</title>
      <link>https://trid.trb.org/View/2761391</link>
      <description><![CDATA[In this paper, we propose a maneuverable-jamming-aided secure communication and sensing (SCS) scheme for an air-to-ground integrated sensing and communication (A2G-ISAC) system, where a dual-functional source UAV and a maneuverable jamming UAV operate collaboratively in a hybrid monostatic-bistatic radar configuration. The maneuverable jamming UAV emits artificial noise to assist the source UAV in detecting multiple ground targets while interfering with an eavesdropper. The effects of residual interference caused by imperfect successive interference cancellation on the received signal-to-interference-plus-noise ratio are considered, which degrades the system performance. To maximize the average secrecy rate (ASR) under transmit power budget, UAV maneuvering constraints, and sensing requirements, the dual-UAV trajectory and beamforming are jointly optimized. Given that secure communication and sensing fundamentally conflict in terms of resource allocation, making it difficult to achieve optimal performance for both simultaneously, we adopt a two-phase design to address this challenge. By dividing the mission into the secure communication (SC) phase and the SCS phase, the A2G-ISAC system can focus on optimizing distinct objectives separately. In the SC phase, a block coordinate descent algorithm employing the trust-region successive convex approximation and semidefinite relaxation iteratively optimizes dual-UAV trajectory and beamforming. For the SCS phase, a weighted distance minimization problem determines the suitable dual-UAV sensing positions by a greedy algorithm, followed by the joint optimization of source beamforming and jamming beamforming. Simulation results demonstrate that the proposed scheme achieves the highest ASR among benchmarks while maintaining robust sensing performance, and confirm the impact of the SIC residual interference on both secure communication and sensing.]]></description>
      <pubDate>Mon, 31 Aug 2026 08:50:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761391</guid>
    </item>
    <item>
      <title>Transparent and Trustworthy Blockchain-Based Scheme for the Protection of Vehicular Soft Integrity in Shared Mobility</title>
      <link>https://trid.trb.org/View/2685908</link>
      <description><![CDATA[The automotive industry is transforming from traditional private vehicle ownership to innovative shared mobility solutions, presenting unprecedented cybersecurity challenges. This transition introduces complex security vulnerabilities where malicious actors could exploit the access of a rental vehicle to manipulate the software systems on board. Unlike physical damage, which can be easily detected, software modifications represent an insidious threat that can compromise user safety and vehicle integrity. Our research proposes a blockchain-based approach to address these critical security challenges. We introduce a novel method for ensuring data authenticity and integrity within vehicle systems by leveraging blockchain’s immutable ledger and advanced encryption technologies. Our methodology utilizes the Trusted Platform Module (TPM) to securely archive vehicle data in the central gateway, creating a tamper-evident environment that fundamentally transforms traditional data management approaches. The key innovation lies in the blockchain-based data binding process: when a user possesses a vehicle, they bind application-retrieved data with the vehicle’s existing data and commit them to the blockchain. Upon vehicle return, any potential tampering can be immediately detected by comparing newly acquired data against pre-existing blockchain records. We develop a proof-of-concept implementation and demonstrate significant improvements in security architecture that offer a reliable alternative to conventional database-centric approaches. Comparative evaluations between database-centric and blockchain-centric architectures testify to the operational effectiveness and practical viability of our proposed solution. By addressing the inherent vulnerabilities in shared mobility ecosystems, this research contributes to a sophisticated technological intervention that enhances user safety, data integrity, and trust in emerging transportation paradigms.]]></description>
      <pubDate>Fri, 28 Aug 2026 16:25:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685908</guid>
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