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    <title>Transport Research International Documentation (TRID)</title>
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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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    <item>
      <title>Secure Estimation Using Partially Homomorphic Encryption for Unmanned Aerial Systems in the Presence of Eavesdroppers</title>
      <link>https://trid.trb.org/View/2598834</link>
      <description><![CDATA[Unmanned aerial systems (UASs) are attracting increasing attention thanks to the great mobility and flexibility of unmanned aerial vehicles (UAVs). This paper considers a typical UAS, which consists of a UAV, a sensing device that provides some sensed data to the UAV, and an end-user that operates the UAV. However, the information exchanged between these parties is vulnerable to eavesdropping attacks, emphasizing the need to develop privacy-preserving approaches. The cryptographic methods are undoubtedly effective, but their high computational overhead may adversely impact the normal operations of UASs. Additionally, the dynamic of a UAV has a high dimension, which is disadvantageous for both estimation and encryption. Therefore, this paper proposes a secure distributed estimation protocol with partially homomorphic encryption by encrypting the transmitted measurements and estimates. Attribute to distributed structure and partial homomorphism, the computation amount for secure estimation is greatly reduced. At the same time, the raw data that needs to be encrypted is transferred into the space of plaintexts by a uniform quantizer and a mapping strategy. Finally, the effectiveness of the proposed method is verified by computer simulation.]]></description>
      <pubDate>Wed, 10 Dec 2025 11:10:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598834</guid>
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    <item>
      <title>Monitoring cyberthreats in railway systems: A hybrid framework for detecting stealthy data tampering attacks</title>
      <link>https://trid.trb.org/View/2605095</link>
      <description><![CDATA[Railway cybersecurity has become a critical concern as the integration of advanced monitoring systems increases reliance on technology. Cyberattacks targeting railway systems can disrupt operations, compromise data integrity, and mislead maintenance decisions, jeopardizing safety and efficiency. Despite these risks, existing detection methods often struggle to address stealthy data tampering attacks designed to either mask failures or trigger unnecessary maintenance. To remedy this gap, this article proposes a novel framework combining Turnout Lifecycle Analysis (TLA) and Expected Behavior Analysis (EBA), complemented by a weighted, modified Dempster–Shafer theory to integrate threat estimations from both approaches. The proposed framework supports the detection of stealthy cyberattacks and the diagnosis of turnout faults, while enabling resilient decision-making under uncertainty. The framework is validated on simulated cyberattack scenarios, successfully identifying six out of seven attacks while reducing false positives. The results highlight the potential of this framework to give railway maintenance operators more accurate insights, help improve decision-making, and help enhance the safety and resilience of railway operations against cyberthreats.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:25:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2605095</guid>
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    <item>
      <title>Infoscraps: Enforcing Information Security by Mechanism</title>
      <link>https://trid.trb.org/View/2604451</link>
      <description><![CDATA[Several information security problems currently require the vigilance of the defender to prevent exploitation or misclassification of information, specifically code injection vulnerabilities and enforcement of Security Classification Guides. This paper discusses a potential solution that can enforce some of these rules by computer mechanism, reducing the potential for security problems. The solution is to replace using simple text strings with data structures containing both a string and a key-value data store. This metadata allows the computer to apply automated rules to enforce data sanitization and classification.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:24:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604451</guid>
    </item>
    <item>
      <title>Safety Evaluation of Dilemma Zone Protection System for Rural, High-Speed Signalized Intersections Using Empirical Bayes Method</title>
      <link>https://trid.trb.org/View/2612383</link>
      <description><![CDATA[This study employed the empirical Bayes before-and-after crash analysis for the safety assessment of the continuous wide area (CWA) sensor-based dilemma zone protection (DZP) system deployed at multiple rural, high-speed signalized intersections in Alabama. Six intersections treated with the DZP system were selected as the treatment group, while thirty-three untreated intersections with similar characteristics with regard to traffic, geometry, and speed limit were selected as the reference group. Safety performance functions and crash modification factors (CMFs) were developed using police-reported crash data. Red-light running (RLR) crashes were identified from the crash database by categorizing crashes into two groups (strict definition [SD-RLR] and extended definition [ED-RLR] crashes) for a comprehensive safety analysis. The analysis results showed that SD-RLR and ED-RLR crashes would decrease by 35% (CMF = 0.65, SE = 0.06) and 24% (CMF = 0.76, SE = 0.05), respectively, if a rural high-speed intersection is treated with the CWA sensor-based DZP system.]]></description>
      <pubDate>Thu, 23 Oct 2025 17:02:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612383</guid>
    </item>
    <item>
      <title>Protecting the Copyright of Intelligent Transportation Systems Based on Zernike Moments</title>
      <link>https://trid.trb.org/View/2553406</link>
      <description><![CDATA[Intelligent transportation systems are at risk of data misuse. Watermarking data that needs to be opened and shared can mitigate this problem, i.e., embedding signals into generated images, which are imperceptible to humans. Regardless of the shape of the image or any potential attacks it may undergo, the watermark can be detected by algorithms when needed. However, many watermarking schemes fail to resist attacks like cropping and translation, limiting their applicability in the intelligent transportation domain. To tackle these issues, the authors propose a dual watermarking framework based on Zernike moments for intelligent transportation systems, where a robust watermark and a periodic watermark are embedded in different planes of the cover image. Specifically, the authors propose a single-circle model (SCM) where Zernike moments are locally computed based on a circle centered at the image center with a radius proportional to image size for embedding the robust watermark. Since SCM is determined by measuring its center and radius, SCM is applicable to images of various sizes and shapes. Then the authors employ a robust combination of discrete wavelet transform (DWT) and discrete cosine transform (DCT) watermarking algorithms to embed the periodic watermark. For watermark extraction, the authors propose an efficient adaptive correction mechanism (AC) to recognize attack types and automatically relocate the embedding position of the watermark. By combining the above strategies, the proposed scheme can adaptively resist various attacks (e.g., random cropping, translation), which addresses the shortcomings of most existing watermarking schemes. The authors test the watermark using over 300 images of different sizes and shapes, and the experimental results prove that the proposed scheme achieves stronger robustness against various distortions with better invisibility, outperforming the state-of-the-art (SOTA) methods.]]></description>
      <pubDate>Fri, 26 Sep 2025 09:06:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553406</guid>
    </item>
    <item>
      <title>Data governance and data for governance : a circular and regulatory perspective on data</title>
      <link>https://trid.trb.org/View/2598631</link>
      <description><![CDATA[The Smart Urban Traffic Zones1 Project aims to create smart solutions in cities that contribute to increased flexibility in the use of urban space, more efficient transportation, and improved traffic safety. This report is a partial deliverable within the project, where we have explored how a municipality can collect and work with data to achieve better and more efficient solutions, based on policies and regulations. During this work, we have identified four possible approaches to data sharing. In the first approach, the city handles all data-related tasks itself, from start to finish. In the second approach, the city allows private actors to collect data in the city infrastructure. These companies then aggregate and analyse the data, and the municipality procures the results through a public procurement process. The third approach is based on voluntary data sharing. The municipality procures a data-sharing platform, which both public and private entities can use to exchange data. In the fourth and final approach, the municipality makes its data publicly available, for example, via the National Access Point. The expectation is that the market will identify possible use cases for the municipal data, combine it with their own data, and develop services for citizens.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598631</guid>
    </item>
    <item>
      <title>Privacy-preserved authentication &amp; communication in vehicular ad-hoc networks</title>
      <link>https://trid.trb.org/View/2598601</link>
      <description><![CDATA[As a key component of Intelligent Transportation Systems (ITS), Vehicular Ad Hoc Networks (VANETs) enable real-time data exchange, traffic optimization, and smarter mobility. However, large-scale deployment raises critical security and privacy concerns, including message integrity, user anonymity, and protection against unauthorized access. This thesis proposes lightweight cryptographic protocols for secure and privacy-preserving authentication in both centralized and decentralized VANETs. The solutions are designed for real-time efficiency, scalability, and strong security. A primary contribution is the development of a localized task management system that significantly reduces authentication latency in centralized VANETs, achieving vehicle verification within a fraction of a millisecond. In decentralized settings, the proposed protocols employ advanced cryptographic mechanisms, including elliptic curve digital signatures (ECDSA) and non-interactive zero-knowledge proofs (NIZKPs), to establish distributed trust without incurring high computational overhead. These techniques provide strong, provable security while preserving user anonymity during authentication and message exchange. To enhance group communication in VANETs, the thesis introduces efficient group key-sharing schemes that support secure, direct interactions among vehicles. Furthermore, a novel localized revocation mechanism immediately removes malicious vehicles from the network, addressing a key limitation in existing frameworks. This ensures fast, secure authentication for time-sensitive message transfers while limiting the propagation of malicious data. The thesis also investigates the proposed protocol's performance under dynamic conditions, such as high traffic density, large-scale decentralized deployments, and remote authentication scenarios.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:18:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598601</guid>
    </item>
    <item>
      <title>Emerging Artificial Intelligence-Induced Risks in Highway Construction Quality Assurance


</title>
      <link>https://trid.trb.org/View/2558387</link>
      <description><![CDATA[The rapid advancement of artificial intelligence (AI) is reshaping the highway construction industry and quality assurance (QA) practices. However, there are limited studies on how AI tools can be misused to falsify and manipulate construction data, particularly in infrastructure projects where safety, quality, and compliance are critical. While foundational to ensuring quality and safety, QA practices are increasingly susceptible to manipulation through AI. Research is needed to provide a comprehensive framework for detecting and mitigating against AI-driven data manipulation in the highway construction industry. 

OBJECTIVE: The objective of this research is to develop a framework to mitigate unexplored and emerging risks that are induced by generative artificial intelligence (GenAI) and related tools in highway construction QA practices.]]></description>
      <pubDate>Wed, 28 May 2025 14:00:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2558387</guid>
    </item>
    <item>
      <title>Information Security Incidents in the Last 5 Years and Vulnerabilities of Automated Information Systems in the Fleet</title>
      <link>https://trid.trb.org/View/2407771</link>
      <description><![CDATA[The trend towards the introduction of unmanned ships has been outlined for a long time: the number of crews on river and sea ships has been decreasing for several decades, and this decline is associated with the desire to reduce operating costs, increase the capacity of ships and improve the safety of navigation by reducing the influence of the “human factor”. Since the authors are talking about improving safety and automation, it is necessary to consider the transition to unmanned navigation from the point of view of ensuring information security. In this work, at the first stage, examples of vulnerabilities of ship’s automated systems, discovered by information security experts in 2017, will be considered, then an introduction to information security incidents in water transport caused by infection with ransomware viruses will be made. After that, in the second part of the work, an incident with theft of data from a US Navy contractor will be considered, and then an acquaintance with incidents caused by hacker attacks on ship navigation systems will be made.]]></description>
      <pubDate>Fri, 21 Mar 2025 09:36:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407771</guid>
    </item>
    <item>
      <title>Research on Risk Analysis and Testing Method of the Data in Intelligent and Connected Vehicles</title>
      <link>https://trid.trb.org/View/2475390</link>
      <description><![CDATA[With the increasing information interaction in intelligent and connected vehicles (ICVs), there are more and more security vulnerabilities in the exploitable data. These vulnerabilities may cause personal privacy leaks, property damage, etc. This paper analyzes the data security risks in the four basic functional areas of ICVs and introduces a novel desensitized data testing framework. This work will provide support for security testing methods for desensitized data in ICVs and provide effective security protection for sensitive personal information in ICVs.]]></description>
      <pubDate>Mon, 13 Jan 2025 10:24:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475390</guid>
    </item>
    <item>
      <title>Research on Data Security Risk Analysis Method of Intelligent and Connected Vehicles Based on Data Asset</title>
      <link>https://trid.trb.org/View/2475385</link>
      <description><![CDATA[The diversity and complexity of typical scenarios of intelligent and connected vehicles (ICVs) bring serious risks to the data security of vehicles. To avoid the potential data security risks of ICVs effectively, this paper proposes a data risk analysis method for vehicles based on the characteristics of data assets. Considering the similarity between the internet of things (IoT) system and the internet of vehicles system, this method organizes the data assets of the vehicles combined with the layered architecture of the IoT system. The data security risks of vehicles are analyzed from the aspects of threat analysis, vulnerability analysis, and risk quantification. The risk points of vehicles are analyzed, so as to provide model design and verification methods for data security risk analysis of ICVs.]]></description>
      <pubDate>Mon, 13 Jan 2025 10:24:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475385</guid>
    </item>
    <item>
      <title>Liability of Transportation Entity for the Unintentional Release of Secure Data or the Intentional Release of Monitoring Data on Movements or Activities of the Public</title>
      <link>https://trid.trb.org/View/2487295</link>
      <description><![CDATA[Transportation entities collect various amounts of data for transportation related purposes. Without debating the legitimacy of the purpose for the specific data collected, what liability exists for the accidental release of data that was to be securely held by the entity for a transportation related purpose? Similarly, what liability exists for the intentional release of data generated from the monitoring of the movements or activities of the public?  The main objective of this research is to review what statutes, regulations or common law exist regarding the release of data collected for transportation purposes. Included in this research are questions concerning the application of public records laws and the application of any constitutional, statutory or common law privacy rights. ]]></description>
      <pubDate>Wed, 08 Jan 2025 16:11:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487295</guid>
    </item>
    <item>
      <title>A Novel Network Forensic Framework for Advanced Persistent Threat Attack Attribution Through Deep Learning</title>
      <link>https://trid.trb.org/View/2425348</link>
      <description><![CDATA[The Internet now plays a pivotal role in the social and economic land space, providing individuals and businesses with access to essential daily services and tasks. However, it has also become a breeding ground for conflicts. Advanced Persistent Threats (APTs) pose a formidable challenge when directed at organizations and governments, exposing the entire network to substantial security risks. Employing network forensics for attributing cyber-attacks and acquiring timely, credible forensic results is a fundamental challenge in maintaining cyber security. This paper introduces a Deep Learning-based network forensics framework for digitally identifying and tracking network attacks, providing a comprehensive overview of the network forensics process. Specifically, the authors extract network traffic and employ encryption to ensure the integrity and security of data. Subsequently, the authors apply feature filtering techniques to retain essential traceability information, and Deep Learning model parameters are automatically optimized using hyperparameter optimization techniques. Lastly, the authors develop a Multi-Layer Perceptual Deep Neural Network (MLP DNN) model with perceptual capabilities for detecting anomalous events within the network. The authors evaluated the framework’s effectiveness using the UNSW-NB15 dataset. The experiments demonstrate that the proposed framework is applicable to APT attack forensics scenarios. In comparison to other AI methods, the framework excels in discovering and tracking network attack events with high performance.]]></description>
      <pubDate>Fri, 06 Dec 2024 09:19:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2425348</guid>
    </item>
    <item>
      <title>Vehicular Edge Computing Meets Cache: An Access Control Scheme With Fair Incentives for Privacy-Aware Content Delivery</title>
      <link>https://trid.trb.org/View/2414064</link>
      <description><![CDATA[Vehicular Edge Computing (VEC) integrates mobile edge computing with traditional vehicular networks, which shifts the majority of computation and storage workload of resource-constrained vehicles to the edge nodes. The high mobility of vehicles usually leads to frequent network changes and connection interruptions, making data sharing more challenging in such dynamic and unstable environments. To address this issue, cache-based content delivery is considered a promising solution for efficient data sharing in VEC. However, access control and fair incentive distribution in privacy-aware data sharing are rarely taken into account in prior VEC-oriented studies. In this paper, we propose RFIP-VEC, a Revocable access control scheme with Fair Incentive for Privacy-aware content delivery in VEC. Specifically, to enable anonymous authentication and conditional revocation, the authors construct a secure group signature scheme with formally proved security guarantees. Subsequently, based on their group signature scheme, the authors design a two-layer access control framework by employing proxy re-encryption. The authors also establish an evolutionary game theory model to analyze the effectiveness and fairness of the fair incentive in therr scheme. Thus, the authors’ scheme can achieve flexible access control and fair incentive distribution with the assistance of edge nodes. Security analysis and experimental results demonstrate that the proposed scheme can achieve security goals with affordable cost in terms of network performance in VEC.]]></description>
      <pubDate>Fri, 01 Nov 2024 08:51:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2414064</guid>
    </item>
    <item>
      <title>Securing oil port logistics: A blockchain framework for efficient and trustworthy trade documents</title>
      <link>https://trid.trb.org/View/2445145</link>
      <description><![CDATA[The oil port logistics involves multiple parties including oil tanker owners, port authorities, customs, oil suppliers, and shipping companies. These parties need to exchange a significant amount of data and documentation related to cargo, such as bills of lading, customs declarations, and cargo manifests. This huge amount of data and documentation provides ample opportunities for data manipulation and corruption. Moreover, physical documentation is slow and prone to errors and manipulation. This data can be securely stored and shared between different parties in a tamper-proof and transparent manner using blockchain. Blockchain is a decentralized technology that employs secure hashing and consensus algorithms that can detect any data modification. Hence, this work proposes a blockchain-enabled immutable, and efficient framework for trade documentation in oil port logistics. The proposed framework provides timely processing of oil trade documents and ensures immutability while increasing trust among the trade entities. In addition, this work implements a private blockchain for the execution of smart contracts, which can ensure that all parties involved in the logistics process comply with pre-agreed rules and regulations. Simulation results validate the effectiveness of the proposed framework in terms of transparency, immutability, network latency, throughput, and resource utilization.]]></description>
      <pubDate>Sat, 26 Oct 2024 17:36:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2445145</guid>
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