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    <title>Transport Research International Documentation (TRID)</title>
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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>How Multi-Tasking Ability Impacts Performance, Workload, Situation Awareness, Stress and Trust with Simulated Imperfect Automation</title>
      <link>https://trid.trb.org/View/2714217</link>
      <description><![CDATA[Objective: This study investigated whether individual differences in multi-tasking ability (MTa) modulate the benefit and cost of supervising imperfect automation on performance, workload, situation awareness, stress, and trust in simulated air traffic control (ATC). Background: Automation is rarely perfectly reliable, and automation failures can have significant detrimental effects. Prior work established that MTa can modulate the benefit of perfectly reliable automation. However, it is unknown whether MTa influences the cost of supervising imperfect automation. Methods: MTa was indexed using a latent factor from three cognitive tasks completed by 113 undergraduate students. Participants completed two ATC blocks: one without automation (manual) and one with automated assistance for accepting incoming aircraft, handing-off outgoing aircraft, and conflict detection (violations of minimum separation). Conflict detection automation was perfectly reliable. Automation highlighting aircraft needing acceptance and hand-off was imperfect, missing 30% of events (the “unreliable” trials). Results: Lower-MTa participants obtained greater performance benefits to aircraft hand-off from reliable-automation but suffered greater costs from unreliable automation compared to manual hand-off, relative to higher-MTa participants. Situation awareness was improved by automation provision, and workload reduced, although MTa did not vary these effects. Stress reduction with automation, compared to manual, was greater for lower-MTa compared to higher-MTa participants. Higher-MTa participants calibrated trust across the different reliability of ATC tasks more effectively. Conclusion: MTa can lead to differentiated effects of imperfect automation on aircraft hand-off, perceived stress and trust. Application: MTa may warrant consideration in personnel hiring and role selection for work contexts where automation reliability is volatile.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:38:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714217</guid>
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    <item>
      <title>Inferring Hidden Attentional States in Driving: A Bayesian Approach to Modeling Distraction and Secondary Task Engagement</title>
      <link>https://trid.trb.org/View/2714211</link>
      <description><![CDATA[Objective: To develop and validate a computational framework that infers individualized attention strategies and latent distraction states to support personalized modeling of multitasking behavior and intervention. Background: Driver distraction from in-vehicle systems is a growing safety concern. However, the level of distraction is often latent and varies significantly across individuals. Existing models typically overlook these differences, limiting their effective use for personalized interventions. Method: We introduce a Partially Observable Semi-Markov Decision Process (POSMDP) to model hidden attentional dynamics and attention allocation decisions. Using behavioral data, including glance behavior, velocity, and pupillometry, from a high-fidelity driving simulator with 18 participants, we estimate personalized reward functions that reflect each driver’s subjective valuation of secondary task utility versus safety cost. Results: The method accurately infers distraction states and recovers participant-specific utility weights governing the trade-off between secondary task benefit and driving cost. Compared to a well-established 2-s glance rule, it improves detection of distraction events and reveals individual variability in attention strategies. Some drivers exhibit highly conservative profiles, while others assign greater value to secondary tasks, even under high distraction. Counterfactual simulations show how perceived task importance could modulate visual attention behavior across individuals. Conclusion: Our POSMDP-based framework provides an interpretable and individualized account of driver attention allocation, capturing both latent states and behavioral variability. Application: This model enables the development of personalized, risk-sensitive driver assistance systems that adapt to individual attention strategies, enhancing road safety through context-aware, graded interventions.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:38:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714211</guid>
    </item>
    <item>
      <title>Understanding aggressive driving: a dual-theory approach using the norm Activation model and the theory of planned behavior</title>
      <link>https://trid.trb.org/View/2709497</link>
      <description><![CDATA[This study integrates the Norm Activation Model (NAM) and the Theory of Planned Behavior (TPB), along with two additional constructs – Multitasking While Driving (MWD) and Ego-Driven Driving Perception (EDDP) – to examine the psychological mechanisms underlying aggressive driving behaviors (ADB). A cross-sectional online survey was conducted among urban drivers in Tehran, Iran, using a convenience sampling strategy. The survey was distributed via social media platforms, and participation was voluntary, yielding 510 valid responses. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that personal norms, influenced by awareness of consequences and ascription of responsibility, reduce ADB, while attitudes, perceived behavioral control, and subjective norms intensify it. Notably, EDDP, influenced by MWD, weakens ethical norms and increases aggression, highlighting the interplay between personal morality and self-perceived expertise in shaping driving behaviors, while offering useful insights for urban traffic safety policies and driver behavior interventions.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709497</guid>
    </item>
    <item>
      <title>Relaxing or imposing constraints? Exploring the associations between digitalization and space-time flexibilities in Qinghe, Beijing</title>
      <link>https://trid.trb.org/View/2714872</link>
      <description><![CDATA[While information and communications technology (ICT)-based multitasking has become a routine feature of daily activity organization, its implications for space-time constraints remain insufficiently understood. This study develops ICT-based multitasking by distinguishing foreground and background ICT use and examines how different forms of ICT use are associated with perceived temporal and spatial flexibility at the activity and daily levels. The analysis draws on a 2-day combined daily activity diary and internet activity diary survey conducted in 2021 in Qinghe, Beijing. In particular, records from both diaries are integrated to distinguish background and foreground ICT use. The associations of foreground and background ICT use with space-time flexibility are estimated by multilevel ordered logistic regression at the activity level and ordinary least squares regression with standard errors clustered at the daily level. Analyses are conducted for light and heavy ICT users separately because they are considered to have different ICT use patterns. Results demonstrate that foreground and background ICT use are associated with perceived flexibility in contrasting ways across analytical scales and user groups. At the activity level, both forms of ICT use are associated with higher perceived temporal and spatial flexibility, particularly among light ICT users. At the daily level, more frequent foreground ICT use is associated with higher average flexibility, whereas frequent background ICT use among heavy ICT users is associated with lower perceived temporal flexibility. These findings highlight the importance of distinguishing foreground and background ICT use when assessing how digitalization relates to activity-travel behavior in the mobile ICT era.]]></description>
      <pubDate>Thu, 16 Jul 2026 16:38:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714872</guid>
    </item>
    <item>
      <title>Exploring task completion times and text performance in pedestrians in single and dual-tasking: Comparative analysis of laboratory and outdoor environments</title>
      <link>https://trid.trb.org/View/2708748</link>
      <description><![CDATA[Background: Increased cell phone use causes individuals to divide their attentional resources between dual tasks in daily life. It is emphasized that this divided attention negatively affects task performance and makes pedestrian cell phone use an increasing safety concern. There is a lack of research on how dual tasks involving cell phone use affect pedestrian movements in laboratory and real-world settings and the time saved by text messaging while walking.Objective: The main purpose of this study is to investigate how dual-tasking affects pedestrians by analyzing task completion time and texting performance, as well as investigating potential gender differences. Methods: 119 students were included in the study. Expanded-Timed Up and Go Test was applied as a single task 1. As a single task 2; text messaging was used on a mobile phone. Dual-task was defined as performing both tasks simultaneously. All evaluations were recorded both in the laboratory and outdoor environment. Results: Between all parameters, task completion times showed a significant difference in favor of dual-task in both environments (p?=?0.05). There was no significant difference in texting performance when comparing the results of two environments (p?=?0.05). Performing texting and walking tasks simultaneously resulted in approximately 43–45% time savings. Conclusions: However, dual-task increases events such as falling or hitting other pedestrians. We recommend that pedestrians do not endanger their health and the health of other pedestrians to save more or less time.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708748</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>Estimating mental workload in executive function-based tasks</title>
      <link>https://trid.trb.org/View/2672761</link>
      <description><![CDATA[Mental workload (MWL) is a critical factor influencing human performance, particularly in complex and dynamic environments such as driving, aviation, and healthcare. Accurate MWL estimation is essential for optimizing system design and enhancing user performance. With advancements in technology, automated vehicles are becoming a reality, yet the cognitive processes involved in sharing control between drivers and vehicle systems remain insufficiently understood. This study investigated the mental workload imposed by these cognitive processes, focusing on the demands of primary executive functions. To achieve this, an experiment was conducted with 36 participants who performed four tasks while data were collected using self-reports and objective physiological measures derived from electroencephalography (EEG). One of the most common method to estimate MWL is using an index/ratio of EEG band powers, known as Mental Workload Index (MWI). This study aimed to enhance MWL estimation by combining features from multiple domains, including statistical, spectral, and complexity-based measures, in contrast to relying solely on the MWI. Using various machine learning classifiers, we demonstrate that combining features from a diverse range of measures significantly improves the accuracy and robustness of MWL estimation. Multilayer perceptron demonstrated superior performance, with an accuracy of 94.5%. Furthermore, the inclusion of additional features beyond MWI leads to improved generalization across subjects and tasks, supporting our hypothesis that a multi-feature approach is more reliable for accurate MWL estimation. These findings provide a more robust solution for real-time MWL estimation, with potential applications in adaptive systems and user-centric design.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672761</guid>
    </item>
    <item>
      <title>MTFENet: A Multi-Task Autonomous Driving Network for Real-Time Target Perception</title>
      <link>https://trid.trb.org/View/2658667</link>
      <description><![CDATA[Effective autonomous driving systems require a delicate balance of high precision, efficient design, and immediate response capabilities. This study presents MTFENet, a cutting-edge multi-task deep learning model that optimizes network architecture to harmonize speed and accuracy for critical tasks such as object detection, drivable area segmentation, and lane line segmentation. Our end-to-end, streamlined multi-task model incorporates an Adaptive Feature Fusion Module (AF²M) to manage the diverse feature demands of different tasks. We also introduced a fusion transform module (FTM) to strengthen global feature extraction and a novel detection head to address target loss and confusion. To enhance computational efficiency, we refined the segmentation head design. Experiments on the BDD100k dataset reveal that MTFENet delivers exceptional performance, achieving an mAP50 of 81.5% in object detection, an mIoU of 93.8% in drivable area segmentation, and an IoU of 33.7% in lane line segmentation. Real-world scenario evaluations demonstrate that MTFENet substantially outperforms current state-of-the-art models across multiple tasks, highlighting its superior adaptability and swift response. These results underscore that MTFENet not only leads in precision and speed but also bolsters the reliability and adaptability of autonomous driving systems in navigating complex road conditions.]]></description>
      <pubDate>Thu, 16 Apr 2026 13:54:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658667</guid>
    </item>
    <item>
      <title>EMVP: An Edge-Assisted Multi-Task Visual Perception System for Multi-Vehicle Scenarios</title>
      <link>https://trid.trb.org/View/2561885</link>
      <description><![CDATA[Visual perception, as a core component of Intelligent Transportation Systems (ITS), plays a key role in enhancing safety and efficiency in urban mobility. While single-task visual perception methods have applications in areas like pedestrian detection and traffic sign recognition, the complexity of real-world scenarios necessitates a shift toward multi-task approaches. This paper introduces the Edge-assisted Multi-task Visual Perception (EMVP) system, which is specifically designed to address the computational intensity and dynamic concurrency challenges inherent to multi-task processing in edge environments. EMVP adopts a collaborative architecture that strategically partitions computational tasks between vehicles and Road-Side Units (RSUs). By integrating Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), the system achieves a lightweight yet efficient multi-task model for resource-constrained environments. To adapt to the dynamic and concurrent nature of multi-vehicle scenarios, EMVP incorporates a content-aware adaptive inference mechanism based on reinforcement learning, enabling dynamic task scheduling to improve Quality of Service (QoS). Experimental results demonstrate that, compared to the single-task baseline model, the multi-task model of EMVP reduces the computational cost by 86.59% on average while achieving a 4.32% improvement in accuracy. Additionally, in dynamic multi-access environments, EMVP’s adaptive scheduling mechanism, which leverages spatiotemporal content awareness, achieves an average QoS improvement of 7.36% over the sub-optimal method.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561885</guid>
    </item>
    <item>
      <title>Interpretable Multi-Task Prediction Neural Network for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2561826</link>
      <description><![CDATA[Owing to the shortage of computing resources for autonomous vehicles and redundant modeling among similar tasks, multi-task models have become a feasible solution. The multi-task prediction model of autonomous vehicles refers to the realization of trajectory, behavior, and risk predictions through a multi-task deep neural network. However, whether the multi-task prediction networks can effectively share information between multiple inputs and whether the shared representations are interpretable remains a concern. To address the aforementioned concerns, this study proposes a multi-source multi-dimensional model interpretation (M3-interpretation) method for multi-task prediction neural network (MPNN). The MPNN proposed in this paper is designed with a structure that emphasizes a “task-specific pipeline as the main, high-level semantic information sharing as the supplement”. Then, based on the information entropy theory, this study creatively extends the information bottleneck attribution method to M3 and uses feature masks to display fine-grained interpretation results. Comparison and ablation experiments using naturalistic trajectory datasets indicated that the proposed model has better prediction performance than single-task models. In addition, fine-grained attribution analysis was conducted on specific behaviors in temporal, spatial, and feature dimensions to explore the laws that affect behavioral inference in MPNN.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561826</guid>
    </item>
    <item>
      <title>Examining multi-tasking behaviour and ICT utilization during commutes in public transport: the role of personality traits, socio-economic factors, and travel characteristics</title>
      <link>https://trid.trb.org/View/2669892</link>
      <description><![CDATA[Promoting information and communication technology (ICT) offers several benefits, including an enhanced travel experience, improved security measures, and more effective multi-tasking. However, existing studies on ICT usage, which are predominantly conducted in developed countries, frequently overlook the role of personality traits. Recent research, however, highlights the significant impact of personality traits on travel behaviour. The present study fills this gap by examining the influence of socio-economic factors, travel characteristics, and two key personality traits (conscientiousness and openness) on ICT usage during commuting in Mumbai, India by employing a hybrid choice model. The results unveil negative associations between ICT usage and factors like travelling with companions, lower income, and conscientiousness. In contrast, openness traits, longer travel time, and access to Wi-Fi at home positively impact ICT usage. These findings carry practical implications, offering policymakers insights to craft targeted strategies for promoting ICT usage and leveraging its benefits for urban commuters.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669892</guid>
    </item>
    <item>
      <title>Multitasking While Walking: How Green Noise and Texting Alter Gait</title>
      <link>https://trid.trb.org/View/2647414</link>
      <description><![CDATA[This study examines the combined effects of green noise and texting on gait to understand how multitasking and auditory stimuli affect walking stability. Green noise, associated with relaxation, was the environmental stimulus; texting the cognitive distractor. We hypothesized texting would reduce cadence and stride length, with green noise either stabilizing or disrupting gait. Ten participants walked on a treadmill under five conditions: normal walking (NW), no texting/sound (WNTNS), sound only (WNTS), texting only (WTNS), and texting with sound (WTS). Gait parameters—step length, step width, single support time, and terminal double support time—were analyzed. Step length decreased in WNTS (p?=?.0106) and WTS (p?=?.0115) versus NW; step width increased in WTNS (p?<?.0001). Single support time was prolonged in WTS; terminal double support time increased in all conditions vs. NW (p?=?.0435). Cognitive and environmental distractions alter gait, potentially increasing fall risk. Further research should explore how green noise and texting influence walking stability.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647414</guid>
    </item>
    <item>
      <title>Impact of External Audio on Driving Task Performance in Simulated under Armor Environments</title>
      <link>https://trid.trb.org/View/2604475</link>
      <description><![CDATA[As part of technology maturation efforts, the COAT Lab evaluated the impact of external audio on driving performance in simulated under armor environments. To do so, we conducted an Engineering Evaluation Test (EET) wherein participants were asked to drive a simulated military vehicle through a Slalom course (primary task) while monitoring for aerial threats (secondary task). Using a combination of objective and subjective metrics, this evaluation quantified participants’ ability to maneuver and detect threats while using external audio as an enabling technology. Evaluation results indicated external audio positively benefited driving performance and situation awareness. However, evaluation results also indicated that external audio was not sufficient in and of itself for detecting time-sensitive aerial threats. Together, these results suggest a development path forward in which external audio is combined with visual information to enhance crew situation awareness under armor.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:25:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604475</guid>
    </item>
    <item>
      <title>Task bundling effect in electric scooter charging platform</title>
      <link>https://trid.trb.org/View/2593773</link>
      <description><![CDATA[This study proposes two innovative optimization-based bundling algorithms to offer attractive options to the decentralized workforce in the electric scooter-sharing platform. The applicability of bundles is raised in enticing workers to a side hustle system of collecting low-battery scooters for a reward per task. The proposed bundling strategy considers the domain-specific characteristics of the scooter charging industry, such as autonomous task selection of workers, depot-oriented workers, a bundle decision phase before the worker selection phase, and limited information on workers’ task preferences. Based on assumptions about worker behavior, the value maximizing bundling (VMB) model aims to generate bundles with a higher reward-to-distance ratio, while the probability maximizing bundling (PMB) model additionally considers the distance required to reach the bundle centroid from the worker depot. The effectiveness of these bundling strategies is evaluated through a series of simulation experiments. Findings suggest that bundles significantly improved scooter collection rates compared to non-bundling scenarios. Additionally, this strategy enhances workers’ profit margins. Scenario-based simulations further demonstrate conditions that amplified the impact of bundling on the overall worker capacity and scooter distribution patterns. Given the superior performance of the PMB model with optimal parameters and the consistent stability of the VMB model, the study offers actionable insights for managers considering the implementation of bundling strategies.]]></description>
      <pubDate>Tue, 18 Nov 2025 11:04:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593773</guid>
    </item>
    <item>
      <title>Uni-EPM: A Unified Extensible Perception Model Without Labeling Everything</title>
      <link>https://trid.trb.org/View/2512337</link>
      <description><![CDATA[Multi-task perception system to simultaneously perceive various kinds of objects is essential for autonomous driving. Existing perception frameworks always rely on multi-labeled datasets, which encompass labels for all pertinent objects, thereby constraining their adaptability to leverage specialized, task-oriented datasets. This approach hinders the efficient utilization of abundant but focused data. Furthermore, stacking multiple expert networks to address these perception objectives inevitably introduces additional computational overhead. To address this limitation, the authors propose Uni-EPM (Unified Extensible Perception Model), with a novel training framework for multi-task perception using task prompt selection to decouple tasks, which enables perceiving traffic signs and traffic lights in addition to lane lines and traffic elements from existing task-specific datasets without re-labeling. To the best of their knowledge, Uni-EPM is the first model can do this in the field of autonomous driving. By introducing the parameter-sharing decoder among tasks, they alleviate the problems of stacking task heads, including significant parameter increase, etc. Uni-EPM achieves state-of-the-art results in multi-task algorithms without substantial increase in parameters, which also demonstrates comparable performance to existing standalone models. The efficiency of the design is validated through comprehensive ablation experiments and results.]]></description>
      <pubDate>Fri, 17 Oct 2025 16:49:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2512337</guid>
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