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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>CarTraC: A Lightweight IoT-Based System for Post-Crash Accountability in Hit-and-Run Incidents</title>
      <link>https://trid.trb.org/View/2685846</link>
      <description><![CDATA[CarTraC is a post-crash evidence-sharing proof-of-concept system for hit-and-run incidents, to cooperatively exchange crash data through direct V2V communication. The proposed architecture focuses on an overlooked aspect of V2V systems, post-crash evidence sharing, while prior research focuses on pre-crash safety. CarTraC detects collisions by monitoring motion data and upon detection automatically broadcasts encrypted and signed messages to nearby vehicles. These messages contain crash-related and vehicle-specific information retrievable by trusted authorities. The system operates without cellular networks or roadside infrastructure and supports two modes: Self-CarTraC, for direct data exchange, and Cooperative-CarTraC, where uninvolved vehicles assist in rebroadcasting. The security properties of the CarTraC protocol were formally verified using ProVerif. A proof-of-concept prototype was implemented on a custom ESP32-based embedded platform equipped with inertial sensors and a 2.4 GHz transceiver. Experimental validation was conducted using two stationary vehicles in an outdoor semi-urban environment, demonstrating reliable post-crash data exchange without external connectivity. The implementation achieved delivery rates of up to 100% at 15 m and 70% at 85 m, with end-to-end latencies below 34 ms.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:05:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685846</guid>
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    <item>
      <title>RLC³-LPI: A Reinforcement Learning-Based Multi-UAV Cooperative Communication Coverage Algorithm With Low Interception Probability</title>
      <link>https://trid.trb.org/View/2685844</link>
      <description><![CDATA[In low-altitude applications, Unmanned Aerial Vehicles (UAVs) have gained widespread adoption, expanding their roles across diverse domains such as search and rescue, communication coverage, and security surveillance. In complex and remote environments, UAVs are frequently deployed as communication relays to support ground search units (GUs). However, a critical challenge persists in ensuring effective communication coverage for GUs during penetration missions while simultaneously minimizing interception risk. To address this challenge, we propose a multi-agent reinforcement learning-based cooperative communication coverage algorithm with low probability of interception (RLC³-LPI) for multi-UAV trajectory planning. Our approach employs a centralized training and decentralized execution paradigm, eliminating the UAV swarm's reliance on global environmental awareness and enhancing scalability in real-world scenarios. Within RLC³-LPI, the low-intercept coverage problem is formulated as a partially observable Markov decision process, and a federation-enhanced deep reinforcement learning algorithm is developed, incorporating meticulously designed reward functions to optimize connectivity, coverage efficiency, and interception avoidance. Extensive experimental results demonstrate that RLC³-LPI outperforms existing benchmarks, achieving higher coverage and connectivity rates while significantly reducing interception probability.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:05:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685844</guid>
    </item>
    <item>
      <title>Modern gasoline direct-injection engines: Key challenges and mitigation pathways: A critical review</title>
      <link>https://trid.trb.org/View/2706082</link>
      <description><![CDATA[Gasoline Direct Injection (GDI) engines have become a cornerstone of modern automotive technology due to their significant advancements in fuel efficiency, emissions reduction, and combustion optimization. However, challenges remain in managing particulate emissions, improving engine performance, and addressing knocking phenomena. This review explores the latest developments in GDI engine technologies, focusing on pathways for enhancing combustion stability, reducing emissions, and optimizing engine performance across various operating conditions. Key technologies such as Exhaust Gas Recirculation (EGR), Gasoline Particulate Filters (GPFs), have been instrumental in addressing the emission challenges. Moreover, alternative fuels like ethanol, methanol, and hydrogen have been explored for their potential to improve combustion efficiency and reduce emissions. The review also discusses various injection strategies, including single, double, dual-fuel, and water injection, as well as the promising role of flash boiling in improving fuel atomization and mixture homogeneity. Despite these advancements, the review identifies several ongoing research gaps, particularly in optimizing injection systems and improving the fuel blends. The paper concludes by highlighting the need for continued research into hybrid systems and advanced modeling techniques to further improve GDI engine performance while meeting increasingly stringent emissions regulations.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:05:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706082</guid>
    </item>
    <item>
      <title>Aerodynamic performance and sail-trim characteristics of a dual-tail self-trimming wingsail</title>
      <link>https://trid.trb.org/View/2729100</link>
      <description><![CDATA[Autonomous sailboats depend on sails as the primary component for capturing wind energy. The self-trimming wingsail incorporates a rotational joint between sail and hull, allowing decoupled motion. This reduces power consumption and improves adaptability to varying wind conditions. This study uses 2D CFD to examine the aerodynamic and trimming characteristics of a NACA0015-based dual-tail self-trimming wingsail, emphasizing tail position, chord ratio, and rotation mode. Most multi-element airfoil studies concentrate on aerodynamic interactions while giving limited attention to coordinated rotation that achieves moment balance; therefore, their applicability to self-trimming wingsails remains restricted. Results show that simultaneous rotation of both tails in the same direction expands the mainsail angle-of-attack adjustment range to 2.17 times that obtained with opposite rotation. Here, the angle first increases and then decreases as tail rotation angle grows. The maximum angle of attack declines with increasing tail-to-mainsail distance and decreasing inter-tail distance because of wake interference. At zero relative tail rotation, the mainsail angle of attack is effectively zero. Additionally, asymmetric wake effects produce distinct aerodynamic performance between the two tails. Greater tail distance weakens wake influence, whereas larger inter-tail distance and chord ratio strengthen it. These findings support the design and application of self-trimming wingsails.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2729100</guid>
    </item>
    <item>
      <title>AISFusionNet: A dual-branch fusion network for robust and intelligent ship type classification</title>
      <link>https://trid.trb.org/View/2729074</link>
      <description><![CDATA[With the increasing frequency of maritime transportation, fishing activities, and marine resource development, accurate vessel identification and classification have become essential for maritime safety supervision, combating illegal fishing, and enhancing maritime situational awareness. However, existing methods often rely on a single data source, such as AIS or trajectory images, limiting robustness under complex sea conditions. This problem is further exacerbated when illegal vessels manipulate MMSI information, leading to misclassification. To address these challenges, we propose AISFusionNet, a dual-branch framework that integrates AIS time-series data with trajectory images. The model combines a CNN-Transformer module to capture temporal patterns and an EfficientNet branch to extract spatial features, followed by a deep feature fusion mechanism. To handle class imbalance, Poly Focal Loss is adopted to enhance the learning of minority and hard samples. Extensive experiments demonstrate that AISFusionNet outperforms existing methods across multiple benchmarks, achieving 95.60% accuracy and 95.34% F1-score. Furthermore, the model shows strong generalization ability on public datasets and cross-region scenarios. This study provides an effective and reliable solution for fine-grained vessel classification in real-world maritime monitoring applications. The source code is available at: https://github.com/zjze/AISFusionNet/tree/master.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2729074</guid>
    </item>
    <item>
      <title>Predefined-time global prescribed performance tracking of ASV via adaptive event-triggered control</title>
      <link>https://trid.trb.org/View/2733008</link>
      <description><![CDATA[This paper addresses the predefined-time global performance tracking control (PTGPTC) problem for an autonomous surface vehicle (ASV) in the presence of model uncertainties and external disturbances. A shift function and a novel performance function are designed to eliminate the conventional restrictive requirement for prior knowledge of the initial conditions. This ensures that the tracking errors converges within a predefined time and remains confined to a predefined region, regardless of initial conditions. Furthermore, a predefined-time sliding mode control framework incorporating a singularity avoidance function is developed to prevent control singularities while guaranteeing convergence within the predefined time. Neural-network-based reinforcement learning (RL) is employed to approximate and compensate for lumped uncertainties online, thereby enhancing tracking accuracy and robustness without requiring exact knowledge of the uncertain ASV dynamics. An adaptive event-triggering mechanism is introduced to reduce communication events. Stability analysis proves that all closed-loop signals are uniformly bounded. Numerical simulations demonstrate that the proposed method drives the tracking errors into the prescribed-performance bounds within the predefined time. Moreover, under bounded uncertainties and external disturbances, the event-triggered mechanism maintain high-accuracy tracking while reducing communication and computational resource consumption by 40.46% compared with continuous control.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2733008</guid>
    </item>
    <item>
      <title>EAC-based ensemble clustering with four-dimensional features for upbound and downbound route extraction in inland waterways</title>
      <link>https://trid.trb.org/View/2733007</link>
      <description><![CDATA[Confined inland waterways are frequently characterized by dense vessel traffic and highly overlapping bidirectional trajectories, presenting significant challenges for maritime surveillance. To address these challenges, the primary objective of this study is to develop a robust, data-driven framework capable of identifying the common navigation corridors and key navigation-state points repeatedly selected by different types of vessels from massive, unlabeled Automatic Identification System (AIS) observations. Unlike conventional single-method approaches, the proposed methodology synergistically integrates a four-dimensional spatial-heading feature space with an evidence accumulation clustering ensemble and k-dimensional tree (KD-tree) consistency filtering. The extracted consensus route templates can support waterway managers in delineating feasible navigation areas and recommended routes, while also providing vessel operators with data-driven references for route selection and key-point speed and heading settings. A case study using data from a navigable reach of the Yangtze River demonstrates the robust quantitative superiority of the proposed framework. Experimental evaluations reveal that the ensemble approach achieves an outstanding average comprehensive clustering performance metric score of 6.0096 across diverse navigational regions. This result significantly outperforms traditional single-method clustering baselines whose average scores merely range from 4.2126 to 5.4673, as well as all dual-model combinations.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2733007</guid>
    </item>
    <item>
      <title>A spatio-temporal diagnostic framework for anchorage utilization: Integrating static efficiency and dynamic stability from a time-geography perspective</title>
      <link>https://trid.trb.org/View/2732992</link>
      <description><![CDATA[Anchorage utilization assessment is critical for port management, but existing studies treat anchorages as homogeneous black boxes, neglecting internal dynamics. To bridge this gap, this paper introduces the “state transition” concept from time geography and proposes a spatiotemporal diagnostic framework. First, a density-based stay point algorithm extracted anchorage data from AIS, and the Getis-Ord Gi∗ statistic identified “Preferred Berths” as stable micro-spatial units. A multidimensional evaluation index system was then constructed, integrating static efficiency (anchorage occupancy rate, spatial Gini coefficient, average anchorage duration) with dynamic stability (average state transition rate, spatio-temporal efficiency heterogeneity). Finally, clustering of these indicators developed a “typological” profile of anchorages for spatiotemporal diagnostics under varying traffic conditions. Using empirical AIS data from ten outer-harbor anchorages in Tianjin Port, the results demonstrated that the framework accurately identifies Preferred Berths, generates typological profiles, and performs detailed diagnostics. A key finding is that high anchorage occupancy does not necessarily correspond to low dynamic activity. By extending time geography's dynamic paradigm to the micro-scale of anchorage research, this work provides a directly applicable typological decision map for differentiated and precise anchorage management.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732992</guid>
    </item>
    <item>
      <title>Modelling driver behaviour as a continuum between defensive and neutral states</title>
      <link>https://trid.trb.org/View/2716713</link>
      <description><![CDATA[Improving road safety requires more than identifying collisions or near misses. It calls for understanding how drivers perceive, interpret, and adapt to risk in real time. The authors introduce a behavioral dimension of conflict severity: the probability a driver is in a defensive state in response to perceived risk, contrasted with a neutral state reflecting routine, goal directed driving. Using a dual state latent discrete choice model, driving is represented as a probabilistic blend of these latent modes rather than a binary safe/unsafe label. The framework is grounded in the affect–behavior–cognition (ABC) triad in psychology. It assumes drivers interpret a dynamic spatial risk field in which proximity, motion, and relative direction guide adaptive shifts in state. The model is applied to naturalistic road user trajectories and yields interpretable state membership probabilities. The defensive state shows stronger sensitivity to spatial and temporal risk indicators. The neutral state captures context appropriate but less reactive patterns. The authors also propose a diagnostic to assess the quality of the estimated defensive state probabilities. Due to the duality of states, consistency in one implies reliability in the other. A multi-step validation across five driving contexts, including free flow and diverging scenarios, tests generalizability beyond the estimation sample. Results indicate behaviorally consistent and reasonably stable estimates across contexts. From a theoretical perspective, this work draws on behavioral science to inform traffic safety analysis by modelling a dimension of conflict severity that has largely remained implicit. From a practical standpoint, the continuous defensive state probability offers a richer diagnostic than binary conflict classifications, with potential applications in real time safety monitoring and infrastructure design. These findings frame probabilistic behavioral states as proactive safety indicators and offer a path to integrate perception, attention, and adaptive control into quantitative traffic safety analysis.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716713</guid>
    </item>
    <item>
      <title>Impact of dispositional and situational factors on willingness to use shared autonomous shuttles</title>
      <link>https://trid.trb.org/View/2716693</link>
      <description><![CDATA[The integration of shared autonomous shuttles (SASs) into existing transportation networks is crucial for fostering user acceptance. Optimizing public adoption of SASs requires public trust in their ability to provide safe, efficient, and effective transportation. While it is evident that trust is a key factor that influences adoption, certain inherent dispositional traits may moderate individuals' baseline trust and interact with situational factors to affect their willingness to adopt SASs. This study advances both the transportation technology acceptance and trust literature by using structural equation modeling (SEM) to identify key dispositional and situational factors that have an impact on individuals' trust in, attitudes towards, and intention to use SASs. Data was collected via online survey to adults residing in multiple cities within the United States of America and Australia. The results of the study confirm trust is a significant predictor of both attitude towards and intention to use SASs. The model also shows dispositional traits, such as the propensity to trust and the plasticity Big Five meta-trait (which consists of the openness and extroversion personality traits), significantly influence perceptions of and willingness to use SASs. Trust and plasticity had mediating effects that influence perceived risk and perceived usefulness - factors that in turn shaped both attitude towards and intention to use SASs. The results from this study are vital for informing strategies aimed at increasing acceptance and usage of SASs, ultimately contributing to the development of more effective and user-friendly autonomous mobility solutions.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716693</guid>
    </item>
    <item>
      <title>Residents’ preferences regarding shared mobility in car-free zones: The case of Delft’s inner city</title>
      <link>https://trid.trb.org/View/2725115</link>
      <description><![CDATA[Growing car dependence intensifies congestion and reduces urban livability. In response, many cities are introducing car-free zones supported by shared mobility to promote sustainable and accessible transport. However, residents’ preferences for shared modes in actively transitioning car-free contexts remain underexplored. This study examines these preferences in the inner city of Delft, a medium-sized Dutch city transforming toward a car-free area under the “Mobility Plan 2040.” A stated choice experiment examines how travel cost, walking time, socio-demographic characteristics, and trip purposes influence the adoption of four shared electric modes, (e-) bikes, scooters, cargo bikes, and cars, while an “opt-out” option captured avoidance behavior. A Mixed Logit model estimated the Value of Time and quantified preference heterogeneity. Travel cost and walking time are the most significant determinants. The estimated VoT (€0.20 per minute) highlights the importance of reducing access distances. Socio-demographic variation was significant: younger, digitally literate residents prefer micromobility, while those aged 50 + are more likely to opt out. Gender, income, education, and trip purpose further shape preferences, with commuting trips showing lower adoption due to time and reliability constraints. Rather than forecasting demand, the authors use the experiment as a behavioral diagnostic of an urban transition in progress: the 64% of choices selecting the opt-out indicate that the offered shared modes were frequently not acceptable substitutes for the recalled trip—most strongly when that trip was car-based—highlighting that car-free strategies must combine service design (pricing, fleet proximity) with car-ownership targeted measures for the groups least ready to substitute.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725115</guid>
    </item>
    <item>
      <title>Relevance and predictive role of human values in pedestrian and cyclist interactions with automated vehicles</title>
      <link>https://trid.trb.org/View/2735231</link>
      <description><![CDATA[As vehicle automation increases, vulnerable road users (VRUs) will need to negotiate right-of-way with level 4 and level 5 automated vehicles (AVs). Designing safe and comfortable interactions, therefore, requires a clear understanding of which human values are relevant for VRUs in such encounters. However, existing research lacks a comprehensive overview of relevant values, clear methods for quantifying them in concrete interaction scenarios, and evidence of their predictive role. To address these gaps, the present video-based laboratory study investigated VRU–vehicle interactions in a bottleneck scenario. Participants took the role of a pedestrian (nₚ = 60) or a cyclist (nc = 65; between-subjects factor) while interacting with an automated or a manual vehicle (within-subjects factor) and with or without another VRU (within-subjects factor). Value relevance was assessed quantitatively through ratings of value definitions and qualitatively by mapping values to participants' explanations of their behavioral intentions. The results show that a range of values, including comprehensibility, integrity, self-efficacy, and relaxedness, are relevant to VRU–AV interactions and should be considered in AV design. However, the predictive power of values for VRUs' behavioral intention was limited. Instead, participants' situation-independent decision preferences to wait or not wait were the strongest predictor of behavioral intention. These findings highlight the relevance of values for understanding VRU–AV interactions while indicating that their predictive power requires further investigation.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735231</guid>
    </item>
    <item>
      <title>A mixed blessing? Explaining the double-edged effects of perceived anthropomorphism on users’ continuance intention to use autonomous taxis</title>
      <link>https://trid.trb.org/View/2735214</link>
      <description><![CDATA[Against the backdrop of the accelerating global commercialization of autonomous driving, users’ continuance intention has become a critical determinant of whether enterprises can achieve sustainable development. However, both practitioners and researchers concerned with the social and ethical risks of artificial intelligence (AI) systems have often overlooked the profound influence of anthropomorphic design, privacy concerns, and psychological factors in shaping human–computer interaction. Focusing on the emerging mobility solution of autonomous taxis, this study empirically examines the double-edged effect of perceived anthropomorphism on users’ continuance intention based on 780 valid survey responses, using Mplus 8.3 and SPSS 27.0 to conduct hierarchical multiple regression analysis and bootstrapping. The results demonstrate that perceived anthropomorphism exerts both facilitating and inhibiting effects. On the one hand, it strengthens continuance intention by enhancing user trust; on the other hand, it undermines continuance intention by heightening user anxiety. Furthermore, privacy concerns significantly moderate the relationships between perceived anthropomorphism, trust, and anxiety, thereby influencing continuance intention. Collectively, this study not only identifies and clarifies the double-edged effect of perceived anthropomorphism in the autonomous driving context but also extends the theoretical boundaries of anthropomorphism research, providing theoretical grounding and practical guidance for balancing anthropomorphic design and privacy governance in autonomous taxi services.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735214</guid>
    </item>
    <item>
      <title>Influence of psychological ownership and media ecosystem for predicting heterogenous shared autonomous vehicle adoption: An extended UTAUT2 model</title>
      <link>https://trid.trb.org/View/2731036</link>
      <description><![CDATA[The transformative potential of Shared Autonomous Vehicles (SAVs) for urban mobility is challenged by user skepticism and disparate acceptance across demographics. Although the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) has been widely applied, significant gaps persist including: the underexplored influence of external factors, specifically mass media, social media, and psychological ownership and a limited understanding of how sociodemographic and travel/driving behavior (STDB) variables affect the heterogeneity by moderating core acceptance pathways. To address these gaps, this study develops an extended UTAUT2 model incorporating these constructs alongside personal innovativeness, attitude toward sharing, and environmental concern. Based on a survey of 723 respondents in Tehran, Iran, the relationships between the proposed theoretical constructs and SAV acceptance are examined using structural equation modelling, complemented by multi-group analysis (MGA). Results confirm the significant direct effects of both core UTAUT2 factors and the extended integrated variables, with the exception of environmental concern, on behavioral intention. Crucially, the MGA reveals that these relationships are distinctly moderated by STDB characteristics such as gender, age, education, car ownership, prior driving and accident experience, and car-sharing usage experience. These findings provide a more holistic and segmented understanding of SAV acceptance, offering refined theoretical insights and practical implications for policymakers and service providers to tailor strategies that address diverse user concerns and accelerate the integration of SAVs into sustainable urban transport systems.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731036</guid>
    </item>
    <item>
      <title>Beyond the plug: Unlocking vehicle to grid potentials during climate-induced disasters</title>
      <link>https://trid.trb.org/View/2728120</link>
      <description><![CDATA[Bidirectional charging goes beyond electric vehicle (EV) charging as a vehicles’ energy is used to supply consumers, buildings and the electrical grid. This way, EVs can provide flexibility to the grid. The present study considers the special case of bidirectional charging during disaster events. Importantly, EV owners’ acceptance for bidirectional charging determines whether it will be successfully employed during future disaster events and if thereby, system resilience can be improved. Through two focus group discussions with homeowners and tenants, all owning an EV, the user perspective is examined and insights into potentials and barriers for bidirectional charging during disaster events are gained. Results show selfish EV-usage motives dominating in anticipation of hypothetical disaster events, even more so for homeowners than tenants. Importantly, the responsibility to provide energy during disaster events was attributed to official actors. Recommendations on how information campaigns for bidirectional charging during disaster events should be designed to enhance acceptance are discussed.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728120</guid>
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