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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>A relay-chain-powered Ciphertext-Policy Attribute-Based Encryption in Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2668529</link>
      <description><![CDATA[The rapid growth of Intelligent Transportation Systems (ITS) requires secure, efficient, and context-aware data sharing across heterogeneous and geographically distributed participants. We propose a relay-chain-driven architecture that couples a context-aware smart contract with a modified Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme to support dynamic access control under low-latency constraints. The relay-chain contract evaluates event metadata (type, time, region) and selects an appropriate policy strictness, On-Board Units (OBUs) then encrypt and store ciphertext on regional blockchains, while the relay chain maintains global attribute definitions, revocation state, and cross-region discovery. The model proposes a context-aware smart contract on a worldwide relay chain that checks data properties, including event type, time, and geographical region, to determine the appropriate encryption policy. From such relay-directed judgement, On-Board Units (OBUs) encrypt data using CP-ABE and store ciphertext on localised regional blockchains, thereby avoiding reliance on symmetric encryption or off-chain storage. Robust, multi-attribute access rules protect high-sensitivity events, whereas common updates use lighter policies to reduce processing burdens. The crypto system also adds traceability and low-latency revocation, with global enforcement managed through the relay chain. This distributed, scalable model strikes a proper balance between real-time responsiveness and security, making it highly suitable for next-generation vehicular networks that operate across multi-jurisdictional domains.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668529</guid>
    </item>
    <item>
      <title>Blueprint: Coordinated Vulnerability Disclosure (CVD) Adoption for Information Sharing and Analysis Center (ISAC)-Like Groups</title>
      <link>https://trid.trb.org/View/2694449</link>
      <description><![CDATA[A coordinated vulnerability disclosure (CVD) is a process for reporting vulnerabilities that are responsibly disclosed by a security researcher to a vendor of a product so that the vendor can patch the vulnerability that was discovered. Currently, this process varies among organizations, and it can often be complicated for an organization to conduct. The electric vehicle supply equipment (EVSE) industry includes diverse participants, such as EVSE manufacturers, charge network operators (CNOs), original equipment manufacturers, and others. With the potential for an information sharing and analysis center (ISAC) or an ISAC-like group, the EVSE industry requires guidance for performing multiparty CVDs for the group to succeed. This blueprint provides a template and guidance for EVSE industry stakeholders on conducting a multiparty CVD. It also formalizes what a multiparty CVD could look like in an ISAC-like group with multiple entities and vulnerability coordinators by noting who might be involved and which roles apply to each member. This blueprint leverages tools such as Vultron and the Vulnerability Information and Coordination Environment (VINCE) along with open resources, such as Carnegie Mellon University’s (CMU’s) Software Engineering Institute’s (SEI’s) Guide to Coordinated Vulnerability Disclosure, for EVSE industry stakeholders to start a CVD program of their own.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694449</guid>
    </item>
    <item>
      <title>A Survey of Cybersecurity Challenges and Mitigation Techniques for Connected and Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2659117</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) are emerging as the future of the automotive industry for secure, efficient and sustainable mobility. CAVs are being rapidly adapted for passenger transportation, delivery of cargo, disaster management and military reconnaissance missions. CAVs for urban transportation are in line with the evolution of “smart cities.” However, as CAVs belong to the family of cyber-physical systems (CPS), they inherit some of the generic cyber vulnerabilities of CPS. Due to the unique features of vehicular networks, establishing secure communication between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) has remained a challenge for vehicular ad-hoc network (VANET) service providers. The vulnerabilities associated with the multitude of sensors and internal connectivity modules increase the threat of cyber-attack on CAVs by many folds. This necessitates clear identification of the potential threats and classify them in terms of attack mechanisms. Such classification will help in mapping the existing security solutions and exploration of new defense strategies for CAVs. This article has conducted a comprehensive survey of the cyber-attacks on CAVs and the ongoing research on the defense mechanisms. Here, the cyber-attacks are organized based on the attacker strategies, attack surfaces, and attack vectors. The mitigation strategies are categorized based on the underlying defense approaches such as cryptography, intrusion detection, access control, and authentication in relation to their effectiveness in mitigating different attack categories. In conclusion this review delves into the current challenges and explores future research directions in the domain of cybersecurity of CAVs.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659117</guid>
    </item>
    <item>
      <title>Blockchain-Empowered Resource Allocation in HAPS-Assisted IoV Digital Twin Networks: A Federated DRL Approach</title>
      <link>https://trid.trb.org/View/2659115</link>
      <description><![CDATA[Vehicular edge computing (VEC) is a new paradigm in smart cities with the potential to provide and manage resources such as computing and storage closer to resource-constrained smart vehicles (SV) to support ultra-reliable low-latency communications and knowledge sharing. However, it is challenging to make optimal resource allocation and offloading decisions due to the mobility, ubiquitous communications, and diverse resource demands of SVs and the dynamic nature of the vehicular network topology. In this paper, we propose an adaptive resource allocation and task offloading scheme for HAPS-assisted Internet of Vehicle networks by exploiting the potential of digital twins (DT), blockchain, and federated multi-agent deep reinforcement learning (FMADRL) technologies to address these issues. The DT network is utilized to intelligently monitor and control the demand and supply of resources in the digital representation of the physical operating environment. We adopt a dynamic pricing-based double auction to model the supplies and demands of resource providers and requesters. This enables resource providers and SVs to make adaptive and optimal resource allocation and task offloading decisions. In addition, we deploy a consortium blockchain to enable distributed and secure resource allocation. The resource allocation and task offloading multi-objective optimization problem is formulated as a multi-agent extension of the Markov decision process and solved using an FMADRL-based multi-agent deep deterministic policy gradient (FMADDPG) algorithm. The numerical results show that the proposed scheme archives efficient resource allocation and maximizes the utility function while minimizing costs compared to the baseline schemes.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659115</guid>
    </item>
    <item>
      <title>Multi-Layer Resource Configuration and Safety Optimization for Integrated Modular Avionics with Resource Sharing and Isolation</title>
      <link>https://trid.trb.org/View/2691618</link>
      <description><![CDATA[The aviation industry extensively employs integrated modular avionics (IMA) to enhance system efficiency by sharing resources across various functions. Despite the benefits, the design of IMA systems is not without its challenges, particularly in achieving cost-effectiveness, ensuring availability, and addressing safety concerns. Optimizing resource utilization in IMA systems is increasingly complex due to the growing functionality and quantity of resources in aviation modules and technological flexibility. This paper presents a multi-layer resource configuration and optimal design method for IMA systems, with a dual focus on resource sharing and isolation mechanisms (which prevent fault propagation and enhance system safety). Our approach takes into account not only the strategic planning of resource sharing but also the intricacies of isolation scheme design, especially in the context of risk propagation. The architecture of the multi-layer resource configuration is meticulously structured and formalized to account for the unique dynamics of risk propagation across different configurations. The optimization process for the configuration scheme is formalized as a constrained multi-objective optimization problem. An optimal performance configuration scheme for the IMA systems can be identified that requires the minimum amount of resources. Finally, the effectiveness of the proposed method is demonstrated through the presentation of an illustrative example. The results show that the proposed approach effectively balances safety, efficiency, and cost in IMA resource management.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691618</guid>
    </item>
    <item>
      <title>Recommendations on Urban Logistics: Expert Group on Urban Mobility, Subgroup 4 — Data Sharing for Zero-Emission Urban Logistics</title>
      <link>https://trid.trb.org/View/2688621</link>
      <description><![CDATA[Data of the urban logistics can play a key role in decision making, measuring performance and short and long term urban planning. Data driven urban logistics policy making and transport operation can balance conflicting needs among various actors and support implementations of a set of policy objectives at the same time. However, how to enable data sharing from the private sector to the public sector has been a challenge. At the same time, data owned by the public sector or infrastructure operators, if being shared with the private sector, both sectors can also benefit. This report includes recommended actions for data sharing. In the Annex, examples of current practices on data sharing have been provided as they provide evidence to the recommendations.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:55:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688621</guid>
    </item>
    <item>
      <title>BTSCRP Research Report 5: Tracking Traffic Records Data: Challenges and Strategies for Success</title>
      <link>https://trid.trb.org/View/2678772</link>
      <description><![CDATA[To compare safety outcomes across jurisdictions, data must be consistently structured. Yet, in many parts of the United States, traffic records are collected and stored inconsistently, even within the same state, making cross-jurisdiction comparisons difficult or impossible. The ability of state and local highway officials to track traffic citations through the judicial process is critical to identifying high-risk drivers and supporting systemic safety improvement. The Transportation Research Board's (TRB’s) Behavioral Traffic Safety Cooperative Research Program (BTSCRP) Project BTS-04, published as BTSCRP Research Report 5: Strategies to Improve State Traffic Citation and Adjudication Outcomes, was launched to identify challenges and potential solutions in traffic records management and to produce a practical toolkit for agencies and states. This article summarizes the project’s background, methods, and key findings including strategies for transitioning to digital traffic-records systems.]]></description>
      <pubDate>Thu, 02 Apr 2026 15:16:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678772</guid>
    </item>
    <item>
      <title>Incentivizing Cooperative Sensing Sharing Ecosystem for Connected and Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2591293</link>
      <description><![CDATA[Connected and Autonomous Vehicles (CAVs) increasingly leverage sophisticated sensor systems integrated with emerging technologies like the sixth generation (6G) for enhancing driving safety and efficiency. Despite the potential of utilizing the advanced communication technology to enhance driving reliability, conficts between high-quality sensing needs of CAVs and insufficient sensing sharing wellness present significant challenges. To bridge the gaps, this paper proposes a novel cooperative sensing sharing framework that utilizes vehicle-to-vehicle (V2V) communication to extend the effective sensory range of CAVs, thereby reducing perception gaps and enhancing driving efficiency in complex driving environments. Aiming at incentivizing cooperative sensing sharing ecosystem within this decentralized framework, we first introduce a multi-tier blockchain architecture that ensures secure and transparent data sharing among CAVs. Concurrently, a custom-designed efficient consensus algorithm is proposed to minimize the overhead while guaranteeing transaction throughput. To tackle issues related to trust and motivate long-term cooperative behavior, we introduce a supervision-oriented model that utilizes evolutionary game to formulate incentive mechanisms discouraging malicious participation and promoting honest, active engagement. Finally, theoretical analysis and extensive simulations demonstrate that our system not only ensures healthy sensing sharing performance but also maintains system security.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:10:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591293</guid>
    </item>
    <item>
      <title>Unlocking lasting improvements in airport punctuality: The challenges facing airports and how they can be met</title>
      <link>https://trid.trb.org/View/2681229</link>
      <description><![CDATA[This paper explores the multifaceted challenges and strategic opportunities involved in improving on-time performance (OTP) at major airports, with particular reference to London Heathrow Airport’s recent initiatives and sector-wide trends. The primary aim is to highlight how sustained focus on punctuality can serve as a lever for operational efficiency, capacity optimisation and enhanced passenger experience, especially as global air travel demand continues to escalate. The paper demonstrates that OTP improvement is not a siloed pursuit. Instead, it depends on transparent stakeholder collaboration, data-driven decision making and integrated sustainability objectives. Readers are provided with practical insights into how airports can more effectively align expectations and incentives across airlines, ground handlers and third party suppliers. These include establishing joint key performance indicators, fostering community forums for real-time issue escalation and resolution, and leveraging shared operational data to target areas for process improvement. The paper also details Heathrow’s adoption of innovative technologies, such as turnaround management tools and ramp cameras, illustrating how accurate, real-time data can drive robust changes in performance. Furthermore, the paper tackles the environmental dimension by explaining how operational initiatives to reduce aircraft auxiliary power unit usage and transition towards alternative ground support not only cut emissions but also deliver cost and service benefits. By reading this paper, airport managers, policy makers and aviation professionals will gain a thorough understanding of how to collaboratively set trajectory-based OTP targets, adopt data and technology for continuous improvement and drive sustainable, resilient growth across the airport ecosystem. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.]]></description>
      <pubDate>Thu, 26 Mar 2026 09:06:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681229</guid>
    </item>
    <item>
      <title>Harnessing shared data and situational awareness: The role of AOS at Helsinki Airport</title>
      <link>https://trid.trb.org/View/2643160</link>
      <description><![CDATA[Helsinki Airport has made significant strides in operational efficiency and predictability by leveraging shared data and situational awareness. At the heart of this transformation is the Airport Operational Status (AOS) system, developed by Finavia, which provides a real-time, comprehensive view of airport processes, from passenger flows and baggage handling to turnaround operations, weather and security. AOS enables seamless information sharing and coordination among key stakeholders, including airlines, ground handlers and authorities, enhancing decision making and resource allocation. This paper explores the development and implementation of the AOS system, highlighting practical challenges, lessons learned and its role in Helsinki Airport’s journey toward achieving the Airport Operational Plan (AOP) in line with CP1 regulation by 2027. AOS was designed to address long-standing issues such as fragmented data, slow communication and lack of shared goals. Through close collaboration with a digitalisation partner, Finavia created a platform that integrates diverse data sources into a user-friendly interface, fostering transparency and operational alignment. Today, AOS supports real-time key performance indicators (KPIs), alerts and incident and crisis communication, accessible via desktop and mobile. It enables early detection of deviations from the operational plan, allowing timely interventions and cost-effective airport management. Helsinki Airport’s success with AOS showcases the power of digital transformation in creating a unified operational picture and driving continuous improvement in airport performance. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:40:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643160</guid>
    </item>
    <item>
      <title>Fine-Grained Access Control With Privacy-Preserving Data Retrieval for Cloud-Assisted IoV</title>
      <link>https://trid.trb.org/View/2674320</link>
      <description><![CDATA[In addition to the autonomous driving technology of single vehicles, the inter-group control algorithm serving the data sharing of multi-vehicle cooperative driving has also attracted widespread attention. To ensure secure communication, many encryption schemes have been proposed to protect the interaction data between vehicles. Nevertheless, traditional public key encryption schemes hinder the sharing of encrypted data. Based on the premise of ensuring the confidentiality of encrypted information, in order to facilitate efficient data sharing, conduct data searches across extensive cloud-based datasets, and authorize access under specified conditions, we introduce Fine-Grained Access Control with Privacy-Preserving Data Retrieval (FGAC-PPDR) for the Internet of Vehicles. This scheme offers a secure, flexible, and privacy-centric approach to data sharing for groups of vehicles in the IoV. Our proposed scheme enables encrypted data to be retrieved at the group level by the cloud server, and prevents vehicles outside the group from performing equality tests on the ciphertext. Furthermore, the data owner can create an authorization token with defined conditions to specify how the data is shared. During the process of data search and sharing, intensive computing tasks are undertaken by cloud servers with abundant computational resources. We also demonstrate that our scheme is secure against chosen ciphertext attacks (CCA). Finally, we provide security and performance analyses that verify the feasibility and effectiveness of our proposal.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674320</guid>
    </item>
    <item>
      <title>Task Offloading and Resource Allocation in Vehicular Cooperative Perception With Integrated Sensing, Communication, and Computation</title>
      <link>https://trid.trb.org/View/2561892</link>
      <description><![CDATA[Vehicular cooperative perception (VCP) facilitates the exchange of sensing data among vehicles through vehicle-to-everything (V2X) communication, significantly increasing the sensing range and precision of individual autonomous vehicles (AVs). However, efficiently managing the sharing and processing of large volumes of sensing data presents challenges due to restricted communication and computation resources. This study introduces an integrated sensing, communication, and computation (ISCC)-based task offloading and resource allocation (ITORA) framework, which optimizes cooperative perception by determining what data to share, which vehicles to involve, and how to process the data effectively. We develop an information value function to evaluate the data quality for each vehicle. Subsequently, we design strategies for sensing task allocation, task offloading, and resource allocation to enable value-driven data selection at a subregion level, facilitating collaborative computing among edge servers and vehicles. Additionally, we formulate an optimization problem aimed at maximizing information value while minimizing delay and energy consumption, subject to constraints on a full region of interest (RoI) coverage, delay, wireless bandwidth, and computational resources. We decompose the mixed-integer nonlinear programming (MINLP) problem into two subproblems, devising a sensing task allocation algorithm and a proximal policy optimization (PPO)-based task offloading and resource allocation (PTORA) algorithm to address them. Comprehensive simulations validate the effectiveness of the proposed PTORA in optimizing information value, reducing task execution delay, and minimizing energy consumption.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561892</guid>
    </item>
    <item>
      <title>MetaSSC: Enhancing 3D semantic scene completion for autonomous driving through meta-learning and long-sequence modeling</title>
      <link>https://trid.trb.org/View/2642466</link>
      <description><![CDATA[Semantic scene completion (SSC) plays a pivotal role in achieving comprehensive perceptions of autonomous driving systems. However, existing methods often neglect the high deployment costs of SSC in real-world applications, and traditional architectures such as three-dimensional (3D) convolutional neural networks (3D CNNs) and self-attention mechanisms struggle to efficiently capture long-range dependencies within 3D voxel grids, limiting their effectiveness. To address these challenges, we propose MetaSSC, a novel meta-learning-based framework for SSC that leverages deformable convolution, large-kernel attention, and the Mamba (D-LKA-M) model. Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, which is designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle's perception via the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model's ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin while also reducing deployment costs.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:01:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642466</guid>
    </item>
    <item>
      <title>The impacts of structured and unstructured information sharing on supply chain performance: the roles of information system connectivity, relationship commitment, and demand uncertainty</title>
      <link>https://trid.trb.org/View/2633375</link>
      <description><![CDATA[This research investigates the information sharing (IS) activities between firms and their customers and inquiries into their impacts on supply chain performance (SCP). Following the information processing theory, this study illuminates a less-explored yet crucial classification of IS activities, structured IS and unstructured IS, and investigates their impacts on SCP under different levels of demand uncertainty. We also probe into the influence of relationship commitment (a relational antecedent) and information system connectivity (an information system antecedent) on structured IS and unstructured IS. Using data of 410 Chinese manufacturers, our research finds that information system connectivity enables both structured and unstructured IS, whereas relationship commitment merely supports unstructured IS, which positively affects structured IS. Furthermore, structured IS improves SCP, while this impact can’t hold for unstructured IS. Finally, demand uncertainty negatively moderates the relationship between structured IS and SCP, while positively moderating the relationship between unstructured IS and SCP.]]></description>
      <pubDate>Wed, 25 Feb 2026 17:00:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633375</guid>
    </item>
    <item>
      <title>Standard-Driven Chinese Knowledge Extraction in Highway Domains Using Machine Learning and NLP Approach</title>
      <link>https://trid.trb.org/View/2640320</link>
      <description><![CDATA[Advancements in informatics have led to numerous data-driven strategies for improving transportation efficiency. However, data exchange between transportation systems is often hindered by complexity, ambiguous definitions, and varied data sets. To tackle these issues, this article introduces a method for creating an automatic knowledge extraction model for highway standards. Machine learning models like BiLSTM-CRF, TextCNN-BiLSTM-CRF, BERT, and BERT-CRF are utilized on small training sets for tasks such as Named Entity Recognition using ISO 12006-3 as upper-level ontology and relationship classification. Additionally, post-prediction and manual corrections refine training data sets for iterative learning. The best results are formatted into graphs and saved as OWL ontologies. This approach yielded 158 graphs from Chinese standards, linked via ISO 12006-3 referenced classes, and the outcome links highway domain concepts, enhancing data management and project collaboration. This method shows promise for better data management and interdisciplinary collaboration in highway projects, furthering data-driven progress in the field.]]></description>
      <pubDate>Wed, 25 Feb 2026 08:54:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640320</guid>
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