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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Mental workload classification using EEG in drivers: A personalized approach to occupational risk reduction</title>
      <link>https://trid.trb.org/View/2744909</link>
      <description><![CDATA[Background Mental state is a key factor influencing occupational health and safety, particularly in professional drivers who are exposed to high cognitive demands during long driving hours. Understanding how mental workload varies across individuals is essential for reducing risks related to fatigue and distraction in transportation occupations. Objective This study classified mental workload using EEG data collected during real-driving conditions and examined how personal factors (age, gender, and driving experience) contribute to inter-individual variability. Methods EEG data were recorded from 39 active drivers during a real-world driving task on a route including main and secondary roads representing higher and lower workload conditions. Mental workload levels were labeled according to road type. A subject-specific classification approach was applied using four machine learning models, with individually optimized parameters. Results The models discriminated between workload levels associated with different road types. The highest average accuracy reached 89.86%, with a maximum of 98.06%. The Multi-Layer Perceptron model combined with Principal Component Analysis gave the best results. Feature importance analysis revealed that frontal theta, beta, and particularly gamma band powers were the most discriminative features, although dominant features varied across individuals. Conclusions The findings demonstrate that EEG-based mental workload levels associated with different road types can be classified using subject-specific models. These results support the development of personalized and adaptive monitoring systems aimed at enhancing occupational road safety and driver health.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2744909</guid>
    </item>
    <item>
      <title>Understanding Trust and Fatigue Under Transparency Regulation in Air Traffic Control: A Multimodal Approach</title>
      <link>https://trid.trb.org/View/2685721</link>
      <description><![CDATA[The integration of artificial intelligence and automation into safety-critical domains, such as air traffic management (ATM), raises new challenges in managing operators’ trust and fatigue under high-workload conditions. Although transparency regulation has been identified as a key factor shaping human–automation interaction, prior work has largely focused on driving or monitoring tasks. Little attention has been paid to managing complex work, such as air traffic control. Moreover, trust and fatigue are typically examined in isolation, with limited understanding of their dynamic interplay in human–machine collaboration. This article introduces a transparency-regulated ATM simulation platform that allows three fixed transparency levels (low, mid, and high) and a user-switchable (mix) mode, enabling controlled investigation of effects on operators’ trust and fatigue. Multimodal data were collected from eye tracking, electroencephalography, and system status logs under varying transparency and workload conditions. By applying machine learning and deep learning approaches, we compare unimodal and multimodal prediction of trust and fatigue. The results show that increasing transparency enhances operators’ understanding, trust, and willingness to rely on the system, while multimodal fusion achieves superior predictive accuracy compared with single-modality inputs. The findings reveal a positive but nonlinear coupling between trust and fatigue, suggesting that adaptive transparency can balance operators’ reliance and cognitive effort. In particular, temporal deep models exhibit strong sensitivity to eye tracking features. Overall, this article contributes a unified framework linking transparency regulation, multimodal state estimation, and adaptive interface design, offering theoretical and practical insights for building resilient ATM automation systems that maintain appropriate trust while mitigating fatigue risks.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685721</guid>
    </item>
    <item>
      <title>Enhancing in-car interface efficiency: The influence of menu configuration on cognitive load and visuospatial memory</title>
      <link>https://trid.trb.org/View/2672519</link>
      <description><![CDATA[The study examines the impacts of different menu types on touchscreen operations under varying visuospatial working memory (VSWM) loads through an in-vehicle information/infotainment system (IVIS). Using eye-tracking, EEG data, and the NASA-TLX questionnaire, it assesses the effects of menu types and VSWM loads on task performance, visual search efficiency, and mental workload. The 36 participants were divided into hierarchical and grouping menu groups, demonstrating that grouping menus exhibit better task performance and visual search efficiency. In contrast, hierarchical menus cause a higher subjective mental workload under greater VSWM loads. Theta waves in the occipital brain region indicate reduced mental workload for grouping menus, and alpha waves in the central region correlate with VSWM load. For goal-oriented search tasks, consider the number of fixations and VSWM interference in IVIS testing. Future studies should simulate real menu usage scenarios and multitasking to offer practical design guidance for in-vehicle and aviation systems.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672519</guid>
    </item>
    <item>
      <title>An improved feature extraction algorithm for EEG-based driving fatigue recognition</title>
      <link>https://trid.trb.org/View/2611427</link>
      <description><![CDATA[Fatigue driving detection is essential to prevent traffic accidents and ensure driving safety. Electroencephalogram (EEG) signals serving as the key physiological indicators of brain activity are widely used to assess the driving fatigue. However, the main challenge in EEG-based fatigue detection is the efficient extraction of features closely related to driving fatigue. This study explores preprocessing and feature extraction methods for fatigue EEG signals. Addressed the susceptibility of raw EEG signals to electrooculography (EOG) artifact interference, an innovative method combining Ensemble Empirical Mode Decomposition (EEMD) and Fast Independent Component Analysis (FastICA) is proposed. This method can effectively filter out EOG artifacts, resulting in purer EEG signals. Additionally, to overcome the limitations of single-method feature extraction and enhance detection accuracy, a novel strategy integrating Wavelet Packet Transform (WPT) and Sample Entropy (SampEn) is introduced. This approach first employs WPT to extract time-frequency features from purer EEG signals, then applies SampEn to capture their nonlinear features, and finally integrates these features into a comprehensive vector, which is classified using an Support Vector Machine (SVM). The experimental results demonstrate that compared to the single feature extraction methods, the proposed multi-feature fusion approach can more effectively capture the detailed information in fatigue EEG signals, significantly improving the recognition accuracy of driving fatigue detection.]]></description>
      <pubDate>Tue, 21 Apr 2026 09:29:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611427</guid>
    </item>
    <item>
      <title>Seafarer fatigue identification based on multimodal data fusion</title>
      <link>https://trid.trb.org/View/2679728</link>
      <description><![CDATA[Identifying seafarer fatigue states is important for maritime traffic safety. Compared to identification based on single-modal data, multimodal data introduces complementary information from each modality. Furthermore, most methods neglect valuable spatial-temporal information. Therefore, this study proposes PSO–CNN–LSTM–ATT, an innovative fatigue identification method for seafarers, which integrates a particle swarm optimization (PSO), a convolutional neural network (CNN), a long short-term memory neural network (LSTM), and attention mechanisms. Notably, this study advances the development and experimental validation of a multimodal fatigue identification framework through real-world ship navigation experiments. Five frequency bands were extracted from electroencephalogram (EEG) signals using a wavelet transform, with five feature values per band serving as EEG features. An enhanced YOLOv5 model identified subjects’ eye-open/eye-closed states, from which percentage of eyelid closure (PERCLOS) values were then calculated. These values, alongside facial landmark coordinates, constituted the video features. Finally, the two types of data features were fused, and the PSO-CNN-LSTM-ATT model was used to identify fatigue states. Results demonstrated that the proposed method achieves superior performance in seafarer fatigue identification, attaining an average accuracy of 93.78%. Furthermore, multimodal data fusion yields better results than the use of either EEG signals or video data alone.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679728</guid>
    </item>
    <item>
      <title>Cross-Driver Domain Generalization for Improved Drowsiness Recognition Based on EEG Signals</title>
      <link>https://trid.trb.org/View/2617677</link>
      <description><![CDATA[Designing brain-computer interface systems for electroencephalogram (EEG)-based driver drowsiness recognition remains a significant challenge due to the significant variation in EEG signals across subjects and recording sessions. To address this problem, this paper develops a novel two-stage information transfer strategy framework for domain generalization. The framework has two domain mappers to reduce the distribution differences of EEG features from different individuals, a mapper mix block for generating hybrid mapping features, and a domain adversarial neural network (DANN) for drowsiness recognition based on hybrid EEG features. In the process of DANN to capture common features, we additionally employ two models based on self-attention mechanism to capture domain-invariant attention relationships between electrode channels and between frequency bands. Experimental results show that the proposed framework achieves an average accuracy of 81.34% in the leave-one-out cross validation for driver drowsiness recognition, which is higher than the state-of-the-art model with the number of 79.37%. In addition, we explore the impact of EEG features from different frequency bands and brain regions on this cross-subject task. The results show that EEG features from delta, theta and alpha bands can achieve much better performance than the other two bands, and features from the frontal lobe region perform better than the other regions. These findings reveal domain-invariant features and their relationships with brain regions and frequency bands, enhancing our understanding of the underlying messages of EEG signals.]]></description>
      <pubDate>Tue, 24 Mar 2026 17:01:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617677</guid>
    </item>
    <item>
      <title>Improving Driving Performance in Difficult Driving Scenarios Using Personalized Real-Time Neurofeedback</title>
      <link>https://trid.trb.org/View/2580274</link>
      <description><![CDATA[Excessive arousal in difficult driving scenarios is a major contributor to traffic accidents. To address this issue, this study proposes a novel method that leverages personalized neurofeedback to modulate driver arousal in real-time. Firstly, by analyzing the relationship between pupil size and reaction time, the Yerkes-Dodson law was validated. Additionally, a cascade model structure was implemented to develop an EEG-based arousal decoder, achieving an arousal recognition accuracy of 74.8%. Secondly,  the real-time generic neurofeedback was demonstrated to help a driver manage over-arousal in difficult driving scenarios. Compared to both silence and sham control conditions, the generic neurofeedback significantly extended crash-free driving time. Notably, when comparing real-time neurofeedback to silence alone, the average driving duration increased by 7.95%. Finally, instead of continuous feedback in generic neurofeedback, this study proposes a Markov decision process (MDP) framework to tailor neurofeedback strategies according to individual differences in workload and arousal. Unlike the continuous generic feedback, the MDP-based approach delivers feedback intermittently. Results indicated that the MDP-based approach further improved driving performance and stabilized arousal levels. Compared to a generic neurofeedback, the MDP strategy yielded an additional 10% increase in average driving time.The findings highlight the feasibility of incorporating EEG-derived arousal states into an adaptive neurofeedback system, confirming that personalized neurofeedback helps drivers maintain optimal arousal under difficult driving conditions, ultimately reducing driving errors and promoting safer road performance.]]></description>
      <pubDate>Tue, 24 Mar 2026 13:08:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580274</guid>
    </item>
    <item>
      <title>Real-time EEG-based mental fatigue estimation model</title>
      <link>https://trid.trb.org/View/2665951</link>
      <description><![CDATA[Numerous high-risk professions require sustained states of alertness. It is estimated that nearly one-third of traffic accidents worldwide may result from inattention and fatigue while driving. Developing methods to prevent as many accidents as possible caused by inattention and fatigue is essential. This conference paper focuses on real-time fatigue estimation by measuring electrical brain activity. A non-invasive method of recording brain activity, using a device called an electroencephalograph, was employed. The study of mental fatigue and its estimation is an area of interest for many studies for several years. Electroencephalography allows the estimation of mental fatigue in a short-term signal, making it an ideal method for this study. We focused on the beta/(theta+alpha) ratio, which is identified as the fatigue index. A simple yet effective model was developed in Matlab Simulink, using power spectral density method to calculate the fatigue index. Measurements were conducted in a way to simulate monotonous driving in a car on a highway. The model can estimate mental fatigue level within the first ten seconds of real-time measurement. For future studies, the focus should be on the technical design and development of a wearable single-electrode electroencephalograph. This device may be used for the estimation of mental fatigue levels in real-life situations, making it suitable for high-risk professions and everyday driving.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665951</guid>
    </item>
    <item>
      <title>The relationship between electroencephalographic measures and driving performance in older adults: A scoping review</title>
      <link>https://trid.trb.org/View/2663681</link>
      <description><![CDATA[With the number of older adult drivers on the road increasing, more older adults are experiencing age-related changes in cognitive functions necessary for driving. Previous research suggests electroencephalography (EEG) may be a useful methodology for assessing these changes, given its high temporal resolution. However, the relationship between specific EEG markers and components of driving performance in older adults is currently unknown. The aim of this scoping review is to examine the current state of knowledge on EEG measures and driving performance in older adults. This scoping review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Papers were eligible for inclusion if they examined a) the relationship between EEG and driving performance measures or b) EEG and driving performance measures simultaneously, in older adults aged 55 years and older. A total of 468 papers were identified, and six papers were included in the final analysis. Results indicate frequency band analyses and event-related potentials are the most commonly used EEG measures to assess changes in driving performance. However, there is considerable variability between the current studies, in terms of the sample sizes, experimental design and the variables of interest. Considerable methodological heterogeneity and the lack of experimental data using cognitive paradigms for EEG, limits the ability to draw conclusions on the relationship between neurocognitive changes and driving performance in older adults. Directions for future research are discussed.]]></description>
      <pubDate>Wed, 25 Feb 2026 08:53:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663681</guid>
    </item>
    <item>
      <title>Cross-Scenario Vigilance Detection Based on EEG Analysis for Safety Driving in Autonomous</title>
      <link>https://trid.trb.org/View/2591243</link>
      <description><![CDATA[Safety driver vigilance is a prerequisite for the safe operation of autonomous vehicles. In contrast to vehicle behavioral trajectory detection, which suffers from high latency and low accuracy, vigilance detection based on physiological signals is currently the most reliable and accurate method. While vigilance monitoring methods using electroencephalograms (EEG) have made considerable progress in experimental scenarios, they remain a challenging problem in scenario-constrained conditions, such as high-speed moving autonomous vehicles. This is due to the low signal-to-noise ratio in EEG signal acquisition and the difficulty of real-time processing. Moreover, cumbersome data acquisition processes and the challenges of labeling have hindered progress in this area. Given the successful use of EEG for monitoring in experimental settings, we believe that the transfer of knowledge learned from these scenarios to new contexts is reasonably feasible. Thus, this work aims to bridge the domain gap between experimental and real-world scenarios while balancing the number of channels and accuracy. Specifically, we propose a framework for EEG vigilance detection capable of Cross-scenario, Cross-subject, and Cross-device, called CCC. The proposed framework leverages the standard montage structure of EEG channels, reducing the number of channels by considering the common regions of EEG channels across different scenarios. The results show that our proposed model achieves an average accuracy of 86.20% on the SEED-VIG dataset with 12 subjects, which is higher than the 82.21% achieved by state-of-the-art deep learning approaches. Finally, we investigate the role of the attention mechanism and transfer learning, and further attempt to explain the advantages of our proposed approach from a visualization perspective.]]></description>
      <pubDate>Fri, 20 Feb 2026 09:03:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591243</guid>
    </item>
    <item>
      <title>Emerging Trends in Internet of Things Enabled Alert Systems for Detecting Driver Drowsiness: A Comprehensive Review</title>
      <link>https://trid.trb.org/View/2642257</link>
      <description><![CDATA[Automobiles are an integral part of civilized society and the fastest world. An accident rate due to the fault of the driver continues to increase at an alarming rate year after year; it can cause permanent damage to the person involved and pose social risks. Logistics and commodity transportation at night are essential, but long trips can cause drowsiness caused by illness or fatigue, and can also be a problem due to the aging of drivers. Driver behavior analysis can help select drivers and can avoid accidents due to drowsiness. The Internet of Things can get real-time information to alert the driver and his relatives about the location of the driver. Detection with IoT can monitor driver sleep through physiological, behavioral, or vehicle inputs and send warning messages to higher officials and drivers’ relatives. Intimation to higher officials can be helpful in creating a database on the general sleep character of the driver. To avoid accidents, the main target is the rapid detection of drowsiness of the driver. Deep learning is used for the fastest detection and can predict accidents. The review of the latest articles supports the idea that electroencephalography (EEG) signals from the brain can provide basic information about the initial stages of sleep in a person. This systematic review summarizes the hybrid mode of detection that is found to be the best compared to the system with vehicle and physiological only considered. Detection speed improvement is also a core area where research is needed.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642257</guid>
    </item>
    <item>
      <title>Towards operational safety in maritime transportation: a neurophysiological workload measurement using deep learning</title>
      <link>https://trid.trb.org/View/2664986</link>
      <description><![CDATA[Human factors account for 70 %–90 % of maritime accidents, with mental workload (MWL) being a significant risk element. Current assessments of seafarer MWL lack objective neurophysiological measures, restricting accurate monitoring and proactive safety measures. This study employed electroencephalography (EEG) data from 10 crew members during simulated navigation tasks to introduce an EEG-based MWL measurement framework for simulated navigation tasks, aimed at monitoring cognitive states and supporting maritime safety management. Three innovations are proposed: (1) a maritime-specific EEG index indicating task-related cognitive demand; (2) a neurobehavioral link between EEG metrics and observed operational errors, showing how high MWL relates to operational safety; (3) a hybrid convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model for classifying MWL states. The model provides objective assessments by identifying key EEG bands with Shapley Additive Explanations (SHAP). In a cross-subject validation, the model achieved an AUC of 0.94, demonstrating its ability to generalize across different seafarers. These findings illustrate how EEG-derived workload assessment can inform data-driven crew management and training strategies, providing a neurophysiological foundation that may support enhancing maritime safety from a human factor perspective.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:42:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664986</guid>
    </item>
    <item>
      <title>COVERT Cognitive Internet of Vulnerable Road Users in Traffic Data Management Plan</title>
      <link>https://trid.trb.org/View/2652002</link>
      <description><![CDATA[This project utilizes a novel approach to investigate the direct incorporation of Vulnerable Road Users (VRUs) in the Vehicle-to-Everything (V2X) architecture, bridging bidirectional communication between VRUs and connected autonomous vehicles (CAVs) and leveraging cooperative sensing for forecasting VRUs’ intent and future motion trajectories. This data management plan describes all of the data that will be collected, how it will be managed, and how it will be stored and archived.]]></description>
      <pubDate>Wed, 28 Jan 2026 08:51:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652002</guid>
    </item>
    <item>
      <title>A Wearable EEG Dataset for Road Crossing Decision-Making [supporting dataset]</title>
      <link>https://trid.trb.org/View/2652001</link>
      <description><![CDATA[Abstract of the final report is stated below for reference: Pedestrians who cross roads, often emerge from occlusion (i.e., obstructed views) or abruptly begin crossing from a standstill, frequently leading to unintended collisions with vehicular traffic that result in accidents and interruptions. Existing studies have predominantly relied on external network sensing and observational data to anticipate pedestrian motion. However, these methods are post hoc (reactive) and insufficient when pedestrians are occluded or stationary, reducing the vehicles’ ability to respond in a timely manner. This study addresses these gaps by introducing a novel data stream and analytical framework derived from pedestrians’ wearable electroencephalogram (EEG) signals to predict motor planning in road crossings. Experiments were conducted where participants were embodied in a visual avatar as pedestrians and interacted with varying traffic volumes, marked crosswalks, and traffic signals. To understand how human cognitive modules flexibly interplay with hemispheric asymmetries in functional specialization, we analyzed time-frequency representation and functional connectivity using collected EEG signals and constructed a Gaussian Hidden Markov Model to decompose EEG sequences into cognitive microstate transitions based on posterior probabilistic reasoning. Subsequently, datasets were constructed using a sliding window approach, and motor readiness was predicted using the K-nearest Neighbors algorithm combined with Dynamic Time Warping. Results showed that high-beta oscillations in the frontocentral cortex achieved an Area Under the Curve of 0.91 with approximately a 1-second anticipatory lead window before physical road crossing movement occurred. These preliminary results signify a transformative shift towards pedestrians proactively and automatically signaling their motor intentions to autonomous vehicles within intelligent vehicle-to-everything (V2X) systems. The proposed framework is also adaptable to various human-connected autonomous vehicle (CAV) interactions (e.g., bicyclists in traffic), enabling seamless collaboration in dynamic and connected traffic environments.]]></description>
      <pubDate>Wed, 28 Jan 2026 08:51:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652001</guid>
    </item>
    <item>
      <title>COVERT: Cognitive Internet of Vulnerable Road Users in Traffic</title>
      <link>https://trid.trb.org/View/2652000</link>
      <description><![CDATA[Pedestrians who cross roads, often emerge from occlusion (i.e., obstructed views) or abruptly begin crossing from a standstill, frequently leading to unintended collisions with vehicular traffic that result in accidents and interruptions. Existing studies have predominantly relied on external network sensing and observational data to anticipate pedestrian motion. However, these methods are post hoc (reactive) and insufficient when pedestrians are occluded or stationary, reducing the vehicles’ ability to respond in a timely manner. This study addresses these gaps by introducing a novel data stream and analytical framework derived from pedestrians’ wearable electroencephalogram (EEG) signals to predict motor planning in road crossings. Experiments were conducted where participants were embodied in a visual avatar as pedestrians and interacted with varying traffic volumes, marked crosswalks, and traffic signals. To understand how human cognitive modules flexibly interplay with hemispheric asymmetries in functional specialization, we analyzed time-frequency representation and functional connectivity using collected EEG signals and constructed a Gaussian Hidden Markov Model to decompose EEG sequences into cognitive microstate transitions based on posterior probabilistic reasoning. Subsequently, datasets were constructed using a sliding window approach, and motor readiness was predicted using the K-nearest Neighbors algorithm combined with Dynamic Time Warping. Results showed that high-beta oscillations in the frontocentral cortex achieved an Area Under the Curve of 0.91 with approximately a 1-second anticipatory lead window before physical road crossing movement occurred. These preliminary results signify a transformative shift towards pedestrians proactively and automatically signaling their motor intentions to autonomous vehicles within intelligent vehicle-to-everything (V2X) systems. The proposed framework is also adaptable to various human-connected autonomous vehicle (CAV) interactions (e.g., bicyclists in traffic), enabling seamless collaboration in dynamic and connected traffic environments.]]></description>
      <pubDate>Wed, 28 Jan 2026 08:51:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652000</guid>
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