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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>Electronic flight bag positioning in the Airbus C-295 flight deck: an ergonomic evaluation</title>
      <link>https://trid.trb.org/View/2707926</link>
      <description><![CDATA[Air operations impose heavy workloads on pilots; portable electronic flight bags (EFBs) can alleviate this burden. However, specific guidelines for EFB postural ergonomics and usability are lacking despite existing cockpit regulations. To describe postural ergonomic and performance observations in six male Brazilian military pilots using portable EFBs during full-flight simulation (Airbus C-295). Participants were evaluated across four cockpit EFB placements. None of the positions met adequate ergonomic standards. The window position presented medium risk, necessitating mitigation strategies. Thigh, yoke, and handheld positions showed high ergonomic risk requiring correction. Despite high ergonomic risk, pilots maintained flight performance standards, likely through compensatory effort. Preliminary findings indicate that while no EFB placement provided adequate ergonomic posture, cockpit positioning did not compromise flight performance. These results highlight the need for subsequent investigations in larger, more robust studies.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2707926</guid>
    </item>
    <item>
      <title>How cognitive load from visual and auditory non-driving-related tasks affects driver takeover performance differently and interacts with takeover request modalities</title>
      <link>https://trid.trb.org/View/2721809</link>
      <description><![CDATA[Previous studies have examined the effects of various non-driving-related tasks (NDRTs) on takeover performance in automated driving. However, few studies have specifically investigated how cognitive load induced by visual and auditory NDRTs separately affects takeover performance. In addition, the interference effect of NDRT modality on takeover request (TOR) modality remains inconclusive. To address these gaps, the authors recruited 36 participants to participate in a simulated driving experiment. NDRT modality, cognitive load, and TOR modality were included as independent variables, and the N-back task was used as the NDRT. The results revealed significant interactions between NDRT modality and cognitive load on steering wheel angle, maximum lateral acceleration, and maximum longitudinal acceleration. When drivers performed the visual N-back task during automated driving, steering wheel angle and maximum lateral acceleration during takeover increased significantly with increasing cognitive load, whereas maximum longitudinal acceleration showed an opposite trend. By contrast, when drivers performed the auditory N-back task, no clear differences were observed across cognitive load levels. Furthermore, TOR modality significantly interacted with NDRT modality in influencing takeover time, takeover workload, and perceived TOR usefulness and satisfaction. A cross-modal advantage of TORs relative to NDRTs was also observed. Overall, this study provides insights into the safety management of automated driving and offers implications for the design of TORs in automated driving.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721809</guid>
    </item>
    <item>
      <title>Temporal changes in secondary task engagement and system use during partially automated driving: A hybrid naturalistic study</title>
      <link>https://trid.trb.org/View/2739392</link>
      <description><![CDATA[Partially automated driving systems that can control the vehicle's acceleration and lateral position are becoming increasingly common. On-road and driving simulator research suggest that the shift from vehicle operator to system supervisor occurring in partial automation may lead to greater disengagement and driver distraction. The naturalistic driving approach where drivers are observed during everyday driving is often considered more methodologically sound, yet naturalistic studies on partial automation use are sparse. This study bridges this gap by adopting a hybrid naturalistic driving method to investigate behavioral adaptations including system use and secondary task engagement resulting from operating Tesla's, Volvo's, and Nissan's partially automated systems for 7 weeks. Three conditions were considered for each vehicle: manual driving, partial automation use, and experimental control. Manual and partial automation conditions refer to when participants were left free to decide when to drive the vehicle in manual or partially automated mode, respectively (six days/week). The experimental control condition took place when participants had to drive the vehicle in manual mode with no option of using partial automation (one day/week). Engagement in secondary tasks including handheld phone use, consuming food or drinks, and browsing the phone was recorded through the adoption of an automated video-based machine vision system. Results showed that: automation use remained stable over time; an uptick in secondary engagement was observed over time, but no differences were found between the three conditions; no differences in secondary task engagement or automation use were observed between the three systems. The data partly contradicts the existing literature on the human factors of partial automation use. It also underscores the need for more naturalistic research investigating temporal fluctuations in potentially distracting behaviors during partial automated driving.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2739392</guid>
    </item>
    <item>
      <title>Riding simulators: What is the way forward?</title>
      <link>https://trid.trb.org/View/2739378</link>
      <description><![CDATA[This paper introduces a novel micromobility simulator that combines a large treadmill with stereophotogrammetry to enable virtual testing of real bicycles and e-scooters while preserving the balancing task and allowing lateral maneuvering. Unlike existing riding simulators, the platform accommodates real vehicles, increasing ecological validity for research on balance and control. The simulator architecture is presented and its potential demonstrated by comparing lateral control during obstacle avoidance—a critical task that previous simulators could not address with comparable realism. Because full balance control is preserved, the authors propose new metrics for comparing balance across micromobility vehicles and show how they complement traditional indicators of lateral control and performance. Overall, the paper contributes 1) a vehicle-agnostic riding simulator and 2) a minimal yet discriminative set of indicators for maneuvering and balance across tasks and vehicles. Twelve participants performed cruising and obstacle-avoidance tasks at different speeds on both a bicycle and an e-scooter. Four indicators were analyzed: standard deviation of lane position and steering angle, adapted from driving simulation research, and standard deviation of lean angle and relative upper–lower body angle, inspired by motor control literature. Results revealed distinct patterns across tasks, speeds, and vehicles, with low redundancy among indicators. Notably, during obstacle avoidance, participants exhibited different postural strategies on bicycles and e-scooters and collided more frequently when cycling. The simulator enables new research on human–vehicle interaction, ergonomics, and safety in micromobility, although further validation and enhancements are needed to fully exploit its potential.]]></description>
      <pubDate>Fri, 21 Aug 2026 14:01:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2739378</guid>
    </item>
    <item>
      <title>The Measurement of Pilot Performance: A Master-Journeyman Approach</title>
      <link>https://trid.trb.org/View/2732476</link>
      <description><![CDATA[This project evaluated several methods for measuring pilot performance in a general aviation simulator and examined the relationship between performance and workload. An Automated Performance Measurement (APM) System was designed for use in a flight simulator which was instrumented for digital data collection. Performance rating was accomplished by three independent observers. Workload was assessed using a real-time subjective input system with which pilots provided workload estimates every minute. Two groups of pilots participated in the experiment: ten professional high-time pilots and ten recently qualified instrument pilots. Both the APM and the observer ratings showed significant performance differences between the two pilot groups. The automated technique showed more of a spread, however, among individuals in the professional (masters) group. The newly qualified pilots (journeymen) reported significantly higher workload than their masters counterparts and their performance was significantly worse.]]></description>
      <pubDate>Wed, 19 Aug 2026 18:11:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732476</guid>
    </item>
    <item>
      <title>Implementation of an integrated model for understanding the impact of task complexity and coping capacity on crash risk</title>
      <link>https://trid.trb.org/View/2737135</link>
      <description><![CDATA[This study investigates how task complexity and coping capacity interact to influence crash risk within the framework of the Safety Tolerance Zone (STZ). The STZ is defined as a dynamic condition in which the driver remains within acceptable boundaries and is operationalized primarily through headway, which was considered as an indicator of crash risk. This work aims to identify the interaction of road, vehicle and driver-related factors to the estimation of task complexity, coping capacity and risk. To this end, data from a naturalistic driving experiment involving 135 drivers and 31,954 trips collected over a four-month period were analyzed across experimental phases incorporating real-time and post-trip interventions. Generalized Linear Models were used to examine the effect of explanatory variables on key driving behavior indicators, while Structural Equation Models were applied to estimate the relationships among the latent constructs of task complexity, coping capacity and risk, expressed through STZ phases. The results showed that environmental factors, including time of day, weather, distance and duration, were positively associated with task complexity and increased crash risk. Driver-related and vehicle-related state factors influenced coping capacity, which was generally negatively associated with risk. The findings also indicated that the relationship between task complexity and coping capacity is dynamic, suggesting behavioral adaptation under more demanding driving conditions. Moreover, the intervention phases showed that real-time warnings and post-trip feedback contributed to safer driving behavior, including greater headway and fewer harsh events. Overall, the study highlights the potential of data-driven interventions to improve road safety and support more effective driver assistance systems.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:02:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737135</guid>
    </item>
    <item>
      <title>Impact of Freight Task Characteristics on Driving Styles: A DSS-Based Quantitative Analysis</title>
      <link>https://trid.trb.org/View/2717722</link>
      <description><![CDATA[Driving style (DS) assessment plays an important role in intelligent transportation system (ITS) application, such as driving feedback provision and usage-based insurance. This paper aims to study the impact of the freight tasks (i.e., vehicle Axle, cargo type, task mileage, and freight costs) on the DS. After cleaning and processing truck GPS, Waybill, and Map, the concept of a minimal unit of DS recognition (MUDSR) is proposed and considered as a micro-DS expression unit to quantify the impact of the freight tasks on the DS. In addition, a unified expression framework, termed driving style structure (DSS), is proposed to compare and apply results of DS related studies in different conditions. The impacts of different characteristics of freight tasks on the DS are then analyzed using the DSS. The results obtained demonstrate that the freight tasks lead to an average increase of 1.58% in the proportion of safe DS in the trucks' DDS. The safety awareness and the concern for monetary losses of truck drivers significantly affect the DSS. In addition, the freight tasks exhibiting higher costs of goods have a greater impact on the improvement of the DSS safety. Based on the results obtained in this study, the relationship between the safe driving attitude and driving performance is discussed. Relevant recommendations are also provided for freight task allocation and long-term and short-term trucker training.]]></description>
      <pubDate>Tue, 28 Jul 2026 15:25:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717722</guid>
    </item>
    <item>
      <title>Distributed Game-Based Joint Task Offloading over UAV-Assisted Inland Waterways Edge Networks</title>
      <link>https://trid.trb.org/View/2717692</link>
      <description><![CDATA[To enhance the ability of uncrewed surface vessels (USVs) to execute computationally intensive and latency-sensitive tasks in Autonomous Inland Waterway Transportation Systems (AIWTS) under time-varying network topologies and communication conditions, the authors propose a novel cluster-based distributed computation offloading framework. Specifically, to avoid the increased computational complexity that arises in centralized frameworks as the number of users grows, the authors design a distributed task offloading game based on exact potential game (EPG) and multi-agent graph reinforcement learning (MAGRL), which aims to maximize overall offloading efficiency by considering dynamically changing device-to-device (D2D) communication quality and cluster topologies. On this basis, to rationally utilize computing and communication resources, the authors formulate a global delay minimization problem. By designing heuristic algorithms and employing convex optimization methods, the authors address task allocation and bandwidth allocation problems to achieve global delay minimization. Finally, the simulation results validate that the framework can effectively reduce the safety-critical decision latency by up to 28.6% compared with the traditional offloading mechanism.]]></description>
      <pubDate>Tue, 28 Jul 2026 11:07:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717692</guid>
    </item>
    <item>
      <title>Task planning for multi-USV in complex environments: a modular hierarchical architecture method</title>
      <link>https://trid.trb.org/View/2721195</link>
      <description><![CDATA[In this paper, a task planning method for multiple unmanned surface vehicles (USVs) is proposed to increase planning and execution efficiency in complex environments; i.e., a modular hierarchical architecture is adopted to decompose the task planning problem into three collaborative decision-making levels: task allocation, path planning, and action control. First, in the task allocation process, an ant colony optimization (ACO) algorithm is developed on the basis of a dynamic task replanning mechanism to enable the system to respond promptly to both static and dynamic tasks, increasing its flexibility in handling tasks in complex environments. Second, in the path planning process, a waypoint sampling strategy is designed to improve the rapidly exploring random tree star (RRT∗) and Dubins hybrid path optimization methods, which enables the distribution of path waypoints to best meet the motion constraints. Third, in the action control process, an improved soft actor-critic (SAC) algorithm is proposed on the basis of the multi-head attention mechanism, which enhances the decision-making ability in complex environments. Finally, simulation experiments verify that this method effectively addresses the core challenges of task planning for multi-USV in complex environments, significantly improving planning efficiency and execution performance.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721195</guid>
    </item>
    <item>
      <title>Cooperative Task Allocation and Path Planning for Multi-UAVs in Low-Altitude Urban Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2717519</link>
      <description><![CDATA[In low-altitude urban intelligent transportation systems, efficient cooperative task allocation and path planning for multiple unmanned aerial vehicles (UAV) are critical for ensuring the effective execution of complex tasks. This paper proposes a distributed decision-making and autonomous planning framework to achieve cooperative task allocation and path planning for multi-UAVs in low-altitude urban traffic environment. The mission requirements of task allocation and path planning are modeled using evolutionary potential games and show that there exists a Nash equilibrium for the proposed potential function. An Improved Log-linear Learning Algorithm (ILLA) is proposed, and suitable Boltzmann parameters are derived which will enable the proposed ILLA to converge to the optimal Nash equilibrium with a probability one. Furthermore, a Constraint-Based Multi-layer Bidirectional Adaptive A-Star (CBMBA A-Star) algorithm is designed to find optimal and collision free paths for each UAV. Compared with the baseline method, simulation results demonstrate that the proposed approach improves the task reward by 11.67%, reduces the task execution time by 37.41%, and decreases run time by 61.02%, confirming its effectiveness and efficiency in the complex low-altitude urban traffic scenario.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717519</guid>
    </item>
    <item>
      <title>3D Risk Assessment Model of Non-driving-related Tasks in Level 3 Automated Driving</title>
      <link>https://trid.trb.org/View/2682119</link>
      <description><![CDATA[The potential for non-driving related tasks (NDRTs) within the context of automated driving prompts the question of what activities might be both appropriate and safe. A unique Risk Assessment Model (RA-Model) of NDRTs in SAE Level 3 vehicles has been developed, based on several cornerstones: (i) sensory-visual load – encompasses sensory modality and visual load; (ii) overall mental load – refers to mental load and interruptibility; (iii) manual load – concerns the position of driver’s body and hands availability. RA-Model is based on a three-step process: (i) qualitatively assessing the key characteristics of the NDRT; (ii) determining the quantitative level of all three types of loads that enter the overall evaluation of the NDRT; (iii) to carry out an overall assessment of the NDRT into a three-dimensional (3D) model. The novelty of this approach can be summarized as a unique rating system using several criteria which leads to the determination of suitability of NDRTs. The benefits of RA-Model are threefold. Firstly, for a safe approach in the preparation of legal environment for SAE Level 3 vehicles; secondly, for manufacturers of vehicles to fine-tune the setup of human machine interface (HMI); and lastly, for high-quality awareness campaigns accompanying an advent of conditionally automated vehicles.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682119</guid>
    </item>
    <item>
      <title>Analysis of Human Errors in Remote Ship Operation using Task Analysis and Bayesian Network</title>
      <link>https://trid.trb.org/View/2669599</link>
      <description><![CDATA[Remote ship operation is emerging as a practical solution for autonomous ships. However, since human intervention is still required in emergencies, safety risks due to human error remain a concern. In particular, knowledge about remote operation is limited, and few relevant human performance factors have been identified. To address this, the present study analyzes how human factors influence operator performance in remote ship operations. As a representative task, course recovery was examined, assuming that manual intervention is needed to regain control of the ship. First, task analysis was conducted to decompose the task, model task relationships, and identify factors related to performance. Potential human errors and associated factors were then identified, and a Bayesian network was developed to analyze their probabilistic impact. The results indicate that operator experience and human-machine interface design are critical, suggesting that improvements in these areas should be prioritized.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:23:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669599</guid>
    </item>
    <item>
      <title>Assessing human reliability in reactive cargo handling for chemical tanker ships</title>
      <link>https://trid.trb.org/View/2674139</link>
      <description><![CDATA[This study assesses human reliability in reactive cargo handling on chemical tankers and proposes a hybrid Dempster-Shafer (DS)-extended Success Likelihood Index Methodology (SLIM) approach to enhance process safety. A hierarchical task analysis decomposes the operation, DS theory aggregates expert data to address subjectivity and uncertainty, and SLIM derives task/subtask HEPs. Human error probabilities are aggregated at the system level to assess overall system reliability. The novelty lies in assessing an under-researched high-risk shipboard operation with a novel hybrid methodology and offering practical risk mitigation strategies. Key findings show task complexity as the most influential PSF, followed by training/experience. Inhibitor quantity calculations, continuous monitoring procedures, and thermal management operations are revealed as the most critical tasks. Consequently, the overall human reliability is calculated as 8.20E-01. The findings provide valuable insights for maritime stakeholders, suggesting targeted interventions in calculation protocols, monitoring systems, heat management strategies, and specialised crew training to enhance process safety in handling reactive cargo onboard chemical tankers.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674139</guid>
    </item>
    <item>
      <title>A review of in-vehicle touchscreen safety and usability</title>
      <link>https://trid.trb.org/View/2676216</link>
      <description><![CDATA[In-vehicle touchscreens are becoming increasingly common, and in some cases replacing physical controls. Given their pervasiveness, minimizing the negative impacts of touchscreens on safety and usability is essential. Currently, there are no existing guidelines for the design of in-vehicle touchscreens. The authors conducted a rapid review of research investigating the safety and usability of in-vehicle touchscreens, the results of which can be used to inform such guidelines. The authors conducted a search in four databases (ACM, Scopus, SAE Mobilus, Transport Research International Documentation). The focus was on driver touchscreen use in manual vehicles or those with driving assistance (i.e., up to SAE Level 2 driving automation). Studies were included if they investigated the effect of touchscreen design or age on safety/usability using inferential statistics. Studies investigating brought-in or aftermarket touchscreens were excluded. The authors identified and summarized the findings of 73 studies that investigated a range of topics related to touchscreen interaction: comparison with physical or voice controls, screen size, use of driving automation, task type and difficulty, age, and other design considerations, such as feedback and button/text size. Overall, touchscreens tended to have negative effects on driving performance, visual attention, and secondary task performance compared with voice control and physical controls, although there were exceptions for some tasks. There was limited research using larger touchscreens, which are becoming increasingly common, as well as a lack of systematic research on interactions between different touchscreen elements (e.g., feedback, task type, screen size).]]></description>
      <pubDate>Tue, 24 Mar 2026 16:18:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676216</guid>
    </item>
    <item>
      <title>Driver’s mental workload prediction model based on physiological indices</title>
      <link>https://trid.trb.org/View/2661753</link>
      <description><![CDATA[Developing an early warning model to predict the driver’s mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers’ MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver’s MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R2 value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver’s work performance.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661753</guid>
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