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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>Numerical Simulation Study on the Influence of Explosive Shape on the Dynamic Response of Tracked Vehicles</title>
      <link>https://trid.trb.org/View/2777858</link>
      <description><![CDATA[This study utilizes finite element analysis and a multi-material arbitrary Lagrangian-Eulerian (ALE) formulation to systematically investigate the dynamic responses of tracked armored vehicles under underbody blast loading from cylindrical, spherical, and cubic charges. Numerical results indicate that, given equivalent charge masses and detonation distances, the shape of the explosive geometry significantly affects shock wave propagation characteristics and the subsequent structural response behavior. Compared to the other two charge configurations, the cylindrical charge exhibits superior shock propagation velocity and a more rapid dynamic response time in the vertical direction. Consequently, the cylindrical charge induces a maximum elastic displacement of 114.9 mm in the underbody structure—surpassing the cubic and spherical charges by factors of 1.16 and 1.18, respectively—while significantly intensifying near-field overpressure.]]></description>
      <pubDate>Wed, 16 Sep 2026 09:15:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2777858</guid>
    </item>
    <item>
      <title>Methodology to Account for Rising Groundwater in Flexible Pavement Analysis and Design</title>
      <link>https://trid.trb.org/View/2775068</link>
      <description><![CDATA[Many pavement design methods, including the widely adopted AASHTO 1993 empirical approach and most mechanistic-empirical (M-E) approaches, only account for seasonal variation of subgrade modulus throughout the pavement life and use either a single stiffness measure or a single set of seasonal values for the whole subgrade layer. This limits their reliability under changing moisture conditions caused by rising groundwater tables, sea-level rise, or other factors. This study introduces a methodology to integrate temporal and spatial variations in subgrade modulus caused by rising groundwater into existing empirical and M-E pavement design methods. The approach uses soil–water characteristic curve relationships to model moisture-dependent modulus changes and applies layered elastic analysis to derive equivalent modulus values. Predictive models were developed for all standard AASHTO subgrade soil classes, enabling more accurate pavement performance forecasts under varying groundwater conditions. To demonstrate application of this work, the predictive models were applied to predict pavement deterioration curves for empirical and M-E approaches that reflect evolving moisture conditions on pavement performance over time. The final output of this study is a set of predictive models that can be leveraged to improve pavement resilience by informing more effective decision-making, optimizing design strategies, and supporting proactive adaptation to changing moisture conditions.]]></description>
      <pubDate>Wed, 09 Sep 2026 08:49:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775068</guid>
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    <item>
      <title>Benchmarking Fatigue in United States Helicopter Air Ambulance Pilots</title>
      <link>https://trid.trb.org/View/2742772</link>
      <description><![CDATA[Helicopter air ambulance (HAA) pilots are exposed to fatigue risk due to on-call and shiftwork operations required for 24-hour emergency service. The current field study aimed to provide a benchmark for fatigue in US HAA operations to inform future fatigue management strategies. US HAA pilots working 7-days-on, 7-days-off schedules were invited to participate in a field study. Data collection included 3 days Pre-hitch, a 7-day Duty Period (hitch), and 3 days Post-hitch (recovery) and involved reaction time tests, subjective sleepiness and fatigue ratings, workload ratings, and actigraphy to estimate sleep metrics. Pilots (N = 120, from 21 US HAA companies) working either daytime, nighttime, or mixed (e.g., 3 days followed by 4 nights) schedules completed an initial survey followed by 13 days of data collection during normal operations. The current study found that: (i) half of the HAA pilots surveyed have poor quality sleep; (ii) cumulative fatigue builds across a 7-day hitch, affecting performance on all schedule types; (iii) pilot subjective fatigue and sleepiness ratings remained consistent, even when objective measures of performance worsened; (iv) pilots obtain less sleep during a hitch than on days off on all schedule types, despite taking extra naps during night duties; (v) fatigue accumulates faster across a duty period for night and mixed schedules compared to day schedules; (vi) fatigue accumulated across a hitch persists into recovery days; (vii) circadian disruptions associated with flight requests and subsequent trips operated during periods of low circadian alertness negatively impact performance; and (viii) pilots experience sleep inertia effects (reduced alertness) when woken up by a flight request. Altogether, these results offer the most comprehensive assessment of US HAA pilot fatigue since the introduction of updated HAA regulations in 2014 and offer a number of potential fatigue mitigation opportunities for the industry.]]></description>
      <pubDate>Mon, 24 Aug 2026 08:44:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742772</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>Systemic Modelling and Simulation Approach to Estimate Brake Response Time in a Pneumatic Brake Systems</title>
      <link>https://trid.trb.org/View/2761931</link>
      <description><![CDATA[Brake response time in truck air brake systems is crucial for ensuring safety and operational efficiency. This paper details the development of a simulation model aimed at fulfilling all regulatory requirements for brake response time, as well as serving as a tool for stopping distance calculations. The actual pneumatic circuit, including brake valves, relay valves, brake chambers, and plumbing have been replicated. The aim is to use 1D simulations to predict the response time compliance during the pressurizing phase (when brakes are applied) of the brake system. A mathematical model is developed using a commercially available 1D simulation tool. This model employs a lumped parameter approach for the pneumatic components, with governing equations derived from compressible flow theory and empirical valve flow characteristics. The simulation outcomes provide detailed response time and pressure build-up profiles. Validation against 201 vehicle test cases showed 96% of simulations within ±10% of measured response times, aligning with FMVSS thresholds. These results confirm the model’s predictive accuracy and its value in optimizing brake system design while reducing physical testing. Overall, the proposed approach offers actionable insights to optimize air brake designs during the development phase, thereby, significantly reducing the need for expensive testing.]]></description>
      <pubDate>Wed, 19 Aug 2026 13:48:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761931</guid>
    </item>
    <item>
      <title>Optimizing Collision Warnings: A Shift from Time to Collide to Time to Brake</title>
      <link>https://trid.trb.org/View/2761761</link>
      <description><![CDATA[In automotive safety systems, Time to Collide (TTC) is traditionally used to trigger warnings in auto-emergency braking systems. However, TTC can lead to premature or inaccurate warnings as it is calculated based on the relative speed and distance between the ego and an obstacle. TTC does not consider the vehicle’s braking dynamics, such as brake prefill lag which varies across different vehicles, maximum deceleration, and the effectiveness of braking systems and assumes constant speed which may not always be realistic. We propose Time to Brake (TTB) as a more effective parameter for driver warnings. TTB directly relates to the action a driver needs to take—braking. It provides a clear indication of when braking should begin to avoid a collision, whereas TTC only tells us about the possibility of a collision. To calculate TTB we utilize the brake profile, which incorporates both deceleration and system jerk for improved accuracy. The proposed warning time is the sum of variable brake prefill lag, average driver reaction time, and TTB. TTB is calculated for two distinct scenarios due to differing constraints using Newtonian equations of motion. The comoving and oncoming scenario involve both ego and object colliding at the same location, but in the former, the relative velocity is zero, and in the latter the ego’s velocity is zero at the point of collision. This approach enhances driver response and safety by providing timely and relevant warnings. Tailored for specific braking dynamics, TTB improves the effectiveness of automotive safety systems.]]></description>
      <pubDate>Wed, 19 Aug 2026 13:48:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761761</guid>
    </item>
    <item>
      <title>Virtual Validation of ADAS Algorithms Using Driver-in-the-Loop Simulation</title>
      <link>https://trid.trb.org/View/2761742</link>
      <description><![CDATA[Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test Drive (VTD). By developing an integrated solution for acquiring driver response, creation of scenarios and evaluation of control systems, this paper focuses on virtual validation of systems in a time saving and cost-effective manner.]]></description>
      <pubDate>Wed, 19 Aug 2026 13:48:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761742</guid>
    </item>
    <item>
      <title>Analysis of physical and gaze behavior factors influencing driver responses in car-to-cyclist intersection collisions</title>
      <link>https://trid.trb.org/View/2706128</link>
      <description><![CDATA[Drivers' physical abilities significantly contribute to collision occurrences, and gaze behavior relates to hazard detection. However, it remains unclear how drivers' physical abilities and gaze behavior comprehensively affect collision occurrences, as these factors have often been examined separately. This study aims to identify the factors influencing drivers' braking reaction time (BRT) in car-to-cyclist collisions at intersections, using visual ability measures from driving aptitude tests as well as gaze behavior data obtained from driving simulator experiments. A decision tree model was applied, and results show that scan frequency and drivers' ability affect BRT.]]></description>
      <pubDate>Tue, 18 Aug 2026 14:11:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706128</guid>
    </item>
    <item>
      <title>When words split on the road: Recognition costs of across-line destination-name order in pavement markings</title>
      <link>https://trid.trb.org/View/2716704</link>
      <description><![CDATA[Destination names are often painted directly onto road surfaces. When lane width is limited, these names are typically split across two lines, creating a design choice regarding their vertical order. While current models of reading propose that preserving canonical order (Top-to-Bottom) facilitates word recognition, real-world implementations often violate this principle. In two go/no-go lexical decision experiments, the authors examined whether vertical ordering of split destination names influences recognition efficiency across two display settings. Spanish city names were presented as split markings in either canonical Top-to-Bottom order (e.g., BARCE above LONA) or inverted Bottom-to-Top order (e.g., LONA above BARCE); in the inverted arrangement, the first fragment appeared on the lower (nearer) line. Experiment 1 used a neutral asphalt texture, whereas Experiment 2 embedded the markings in a realistic road scene. Bayesian mixed-effects analyses showed clear evidence for an advantage of canonical ordering, with response times substantially faster for Top-to-Bottom than for inverted layouts (164 ms in Experiment 1; 211 ms in Experiment 2). From a traffic and road-design perspective, preserving canonical reading order when splitting destination names appears to be a simple, low-cost strategy to reduce processing demands and increase effective preview time for lane-choice decisions.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716704</guid>
    </item>
    <item>
      <title>Hazard perception in manual and hands-off Level 2 driving under daytime and after dark lighting conditions: A driving simulator study</title>
      <link>https://trid.trb.org/View/2705358</link>
      <description><![CDATA[The aim of this study was to examine drivers’ hazard perception in an urban scene, under two different lighting conditions (daytime and after dark) and for two different levels of driving automation (manual driving and hands-off SAE level 2 automation). Forty-eight participants took part in four experimental drives in a driving simulator, encountering six different hazardous/potentially hazardous events in each drive. Results showed that drivers detected hazards significantly earlier and were more likely to react to the hazards in daytime, compared to after dark environments, particularly when pedestrians were approaching the road from the left. However, there was no significant difference in their response time towards the hazards, between the daytime and after dark environments. In terms of driver response in the two automation levels, the majority of drivers were proactive and reacted before the potentially hazardous events turned into actual hazards during manual driving, but responses were more reactive during automated driving. These findings highlight the need to account for context of the driving environment such as lighting conditions and levels of driving automation when designing systems or protocols that aim to support hazard perception and timely driver response.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705358</guid>
    </item>
    <item>
      <title>Effect of mobile phone use on driver reaction time: A comprehensive review</title>
      <link>https://trid.trb.org/View/2731140</link>
      <description><![CDATA[Mobile phone use while driving remains a major road safety concern, as it causes widespread, multifaceted distraction effects. This study reviews the literature on the effect of mobile phone use on driver reaction time (RT), which is considered one of the main determinants of the prompt ability to deal with a hazard and avoid collision. Using a structured narrative review approach informed by PRISMA reporting principles, 41 peer-reviewed studies published between 2001 and 2025 were identified and synthesized, encompassing laboratory, simulator, field, and naturalistic driving studies. Response time effects by mobile phone tasks, handset configurations, driving contexts, and driver attributes are compared in this review. The evidence consistently shows that every type of mobile phone interaction delays driver RT significantly in comparison to undistracted driving. It reveals a clear hierarchy of distraction severity, with social media browsing and texting (i.e., message composition) producing the most pronounced delays, followed by dialing/emailing, picking up calls or messages, and conversational phone use. RT degradation scales with visual-manual engagement and sustained cognitive activity, and results in large increases in stopping distance that increase crash chance. Importantly, hands-free phone usage does not remove impairment from the context, indicating cognitive distraction as the principal cognitive driver. Driver age is consistently a moderator of RT effects, and gender and driving practice display less strong, context-dependent effects. On the whole, these findings demonstrate that no form of mobile phone use while driving is benign and call for task-based, interaction-aware approaches in road safety policy and intervention design.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:01:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731140</guid>
    </item>
    <item>
      <title>Dynamics of traffic oscillations in stochastic linear car-following models considering reaction delay: Analytical investigation and numerical analysis</title>
      <link>https://trid.trb.org/View/2726668</link>
      <description><![CDATA[In real traffic, reaction delay and stochasticity are intrinsic characteristics of human drivers. A simple approximation of the stochastic term based on experimental data reveals that the stochastic term can be decomposed into an additive term and a multiplicative term. The combined impact of the reaction delay, the additive term, and the multiplicative term on the speed variance (the bound of traffic oscillations) has been investigated analytically through closed form expressions and numerically. It is found that there is an interplay between stochasticity and delay, and traffic oscillations become more pronounced with increasing levels of stochasticity and delay. The amplitude of these oscillations, though bounded, can be significant and accompanied by an extended spatial autocorrelation even in the stable regime. Moreover, low frequency modes contribute most to the speed variance, suggesting that traffic oscillation frequencies resemble those observed in real traffic. The string stability of stochastic linear car-following models with reaction delay has also been investigated. Sufficient but not necessary string stability conditions were derived using the Lyapunov functional method. It is found that for relatively low sensitivity to spacing and high sensitivity to velocity difference, and when the instability is of a long wavelength nature, stochasticity significantly destabilizes traffic flow. The destabilizing effect of stochasticity becomes negligible when the instability is of a short wavelength nature, regardless of sensitivities to spacing and velocity difference. To complement the qualitative analysis, numerical simulations were conducted to provide quantitative insights.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726668</guid>
    </item>
    <item>
      <title>Human centric lightning aurora : final research report</title>
      <link>https://trid.trb.org/View/2751940</link>
      <description><![CDATA[This project investigated how lighting conditions affect fatigue in air traffic controllers (ATCOs) working in Remote Tower Center (RTC) environments. While traditional towers rely on natural daylight, RTCs operate in fully digital settings with artificial lighting that may influence operator performance and well-being. The project had two main objectives: to develop a portable light tracker for continuous field measurement, and to examine how different lighting conditions impact fatigue-related outcomes during operational shifts. A dedicated light tracker device was designed using an Arduino-based sensor system and validated through laboratory and field testing, enabling low-cost, high-resolution monitoring of light intensity and spectral composition. The field study compared two lighting setups (a dim, screen-only, and bright, ceiling light with calibrated illumination) across morning and evening shifts. Self-rated and performance-based measures were conducted at multiple time points per operational shift. Subjective sleepiness was assessed using the Stanford Sleepiness Scale (SSS), and performance was operationalized by sustained attention measures using the Psychomotor Vigilance Task (PVT-B). Results showed that while lighting intensity varied clearly between conditions, its direct influence on fatigue was limited. No consistent differences were found in sleepiness or reaction time across conditions, though sustained attention declined toward the end of evening shifts, suggesting time on shift and circadian factors as stronger determinants. Correlations indicated that higher light intensity after shifts was associated with lower sleepiness and faster reaction times. Qualitative feedback highlighted a trade-off between comfort and glare, underscoring that one fixed lighting level is insufficient. Participants preferred lighting that adapts to time of day and personal needs. In conclusion, the findings emphasize that light plays a secondary but relevant role in managing fatigue, primarily through effects on comfort and visual strain. The developed light tracker offers a robust tool for continued research and integration of Human Centric Lighting (HCL) principles into operational fatigue management frameworks in aviation.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:34:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2751940</guid>
    </item>
    <item>
      <title>Stall prediction and identification for civil aircraft based on a long short-term memory model</title>
      <link>https://trid.trb.org/View/2698270</link>
      <description><![CDATA[Stall is a major contributor to aircraft safety incidents. Most civil aircraft lack stall prediction, and existing warnings afford little reaction time, limiting pilots' ability to avert accidents. This paper proposes a deep learning-based stall identification approach. We first analyse flight-performance parameters for a representative civil aircraft and define identification methods of incipient stall. We then conduct cruise-phase stall simulations across 28 operating points for the same type and build a long short-term memory (LSTM) model to forecast the resulting time-series signals. Finally, we train and evaluate the model under varied train/test strategies and compare alternatives. Results show the proposed LSTM reliably anticipates stall and supports timely recovery guidance. The framework demonstrates that data-driven sequence modelling can extend crew reaction time and improve safety, offering a practical path toward onboard stall prediction systems for civil aviation. Performance remains robust under realistic noise and sensor latencies.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:16:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698270</guid>
    </item>
    <item>
      <title>An Online-Offline Machine Learning Approach for Estimating Driver States Using Multi-Sensory Data</title>
      <link>https://trid.trb.org/View/2717687</link>
      <description><![CDATA[Drivers' mental and physiological states critically influence traffic safety, making reliable estimation essential for timely warnings and interventions. However, these states evolve over time and are only indirectly observable through noisy, modality-dependent responses, which can induce biased labels and fragile detectors. To improve the accuracy and reliability of state estimation, the authors propose a unified nonlinear state-space framework that jointly models the dynamics of latent driver states, the influence of driving context, and their effects on multi-sensory responses. Within this framework, an offline multi-view clustering method fuses complementary indicators to establish robust benchmark states, while an online ResNet-Transformer maps multi-sensory data to states in real time. The authors evaluated the proposed framework in a driver-fatigue case study, where fatigue labels were inferred offline from self-reports, reaction-time variability, and eye-aspect-ratio variability; and were subsequently detected online using blood volume pulse, electrodermal activity, and facial images. The results show that the offline-derived levels exhibit statistically significant separation across multiple fatigue indicators, validating the benchmark labels, while the online model achieved 82.5% mean accuracy with 10-second windows and up to 89.5% with 30-second windows, outperforming representative baselines without evidence of class-specific bias. These findings demonstrate that the proposed state-space representation effectively captures latent driver-state dynamics and that the online-offline estimation approach offers reliable and effective means of inferring these states from diverse observational sources, supporting proactive warnings and safer human-vehicle interaction.]]></description>
      <pubDate>Tue, 28 Jul 2026 11:07:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717687</guid>
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