<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <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>
    </image>
    <item>
      <title>Machine learning in pedestrian and evacuation dynamics for the built environment: A systematic literature review</title>
      <link>https://trid.trb.org/View/2674373</link>
      <description><![CDATA[The study of pedestrian and crowd movement has produced a plethora of publications over the past decades. Numerous knowledge-based models have been developed to describe, analyze and predict human motion behavior, particularly with respect to evacuation analysis to ensure public safety. In recent years, Machine Learning (ML) models have become widely successful across many disciplines, including applications for human behavior in the built environment, city planning, robotics and autonomous driving. In this review article, based on a systematic search of the Scopus database (2022–2024), the authors present a comprehensive overview of ML-based pedestrian and crowd models, highlighting the most popular approaches, as well as modern data collection methods that have led to public benchmark datasets and increasingly standardized model validation techniques. The authors analyze ML models that provide insights into crowd movement and evacuation performance, potentially supporting building design and safety assessment in the built environment, while outlining similarities and differences between these models with regards to behavioral traits such as goal-driven behavior and collision avoidance. Moreover, the authors review the involved learning paradigms, including supervised and reinforcement learning, and the associated quantities of interest that can be predicted, such as velocity, density, flow, and evacuation time.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:40:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674373</guid>
    </item>
    <item>
      <title>Monitoring production pressure in socio-technical systems: The case of Belgian railroads</title>
      <link>https://trid.trb.org/View/2680161</link>
      <description><![CDATA[The pursuit of efficiency in Socio-Technical Systems (STSs), where people and technology are interacting to achieve shared goals under dynamic, high-pressure conditions, often places strain on resources. Many transportation settings exemplify an STS, where infrastructures, technologies, and operators are tightly interdependent. In such settings, the drive for efficiency can give rise to Production Pressure (PrP); the tension between performance demands and the capacity to meet them without compromising safety. Left unmanaged, PrP leads to workarounds, cognitive overload, or unsafe practices. The authors present an initial step toward systematically tackling PrP in transportation settings by introducing a novel, quantitative mechanism to measure and monitor it. The authors develop an analytical framework that deploys Data Envelopment Analysis (DEA) to evaluate PrP in STSs. The proposed approach is applied to Traffic Control Centers (TCCs) at Infrabel, Belgium’s railway infrastructure company, where PrP is modeled as the trade-off between railway traffic density and operator workload. The results demonstrate that the model provides a nuanced understanding of the pressures faced by railway traffic controllers. In doing so, this study contributes to the growing need for robust, data-driven tools that integrate human and technical perspectives to support safe, efficient operations in STSs.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680161</guid>
    </item>
    <item>
      <title>Ship availability after a megathrust earthquake and tsunami event: A case study on Canada’s West Coast</title>
      <link>https://trid.trb.org/View/2680149</link>
      <description><![CDATA[Disasters such as earthquakes, tsunamis, hurricanes, and storm surges can severely damage vessels, port infrastructure, and other maritime assets, which are vital for the emergency response of coastal communities and islands. One region particularly susceptible to megathrust earthquakes is near Vancouver Island, where the Cascadia Subduction Zone is located. In this context, this paper presents a model designed to estimate the number of vessels (including ferries and tug-barge combinations) available for emergency response, map out the most dangerous areas for navigation, and identify the routes expected to experience a reduced flow of supplies. The case study results indicate that smaller vessels, such as small ferries and certain tugs, are likely to be unavailable during emergencies, while larger ferries are expected to remain operational. The insights gained from the model are valuable for decision-makers, as they can improve their existing emergency response plans. Future studies could apply the model to other contexts, incorporate additional features, and enhance the overall evidence base.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680149</guid>
    </item>
    <item>
      <title>Safety, identity, and inequity at the last mile: A qualitative study of app-based bicycle delivery riders in Spain</title>
      <link>https://trid.trb.org/View/2680148</link>
      <description><![CDATA[The expansion of the gig economy has led to a growing number of urban workers engaged in app-based food delivery. This sector, often seen as flexible, conceals complex occupational, legal, and psychosocial risks. Recent evidence suggests that delivery riders’ safety is shaped not only by infrastructure or individual behavior, but also by precarious work conditions, limited legal protections, and forms of social exclusion that remain largely unaddressed. This qualitative study examined how safety, identity, and equity are experienced and negotiated in app-based bicycle delivery in Spain, with attention to algorithmic timing, organizational rules, and street-level conditions. Twenty semi-structured interviews were conducted with food delivery riders (mostly migrant men) in urban areas of Spain. A reflexive thematic analysis (inductive) was applied, with attention to patterns, contrasts across cases, and speech insights suggesting broader socio-labor dynamics. Three core themes were identified: (1) persistent exposure to traffic and environmental hazards, often aggravated by digital pressures and limited enforcement of safety regulations; (2) a fragmented social identity, with riders feeling excluded from both formal labor structures and mainstream cycling culture; and (3) strong perceptions of systemic inequity, including legal precarity, economic fragility, and marginalization in public and policy narratives, which may influence how riders manage risk in practice (e.g., rule compliance, incident reporting) and, in turn, safety outcomes. The findings highlight the vulnerabilities of bicycle food delivery riders and suggest the need to rethink how safety, labor protections, and urban inclusion are framed and implemented in this sector.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680148</guid>
    </item>
    <item>
      <title>Big five personality traits as predictors of safety attitudes in aviation maintenance personnel</title>
      <link>https://trid.trb.org/View/2681458</link>
      <description><![CDATA[This paper explores the relationship between personality traits, as measured with the Big Five Inventory, and safety attitudes among aviation maintenance personnel. A questionnaire-based study was performed with an adapted version of the Flight Safety Attitudes Questionnaire (FSAQ). A correlational analysis explored the relationship between the five personality traits and safety attitudes. The results indicated that safety attitudes were significantly positively correlated with extraversion, agreeableness, conscientiousness, and openness, while negatively correlated with neuroticism. Moreover, a robust regression analysis was conducted to determine whether personality traits explain additional variance in safety attitudes beyond the influence of demographic factors. The regression analysis has identified that conscientiousness, agreeableness, and extraversion can predict positive safety attitudes, even after controlling for demographic variables such as length of work experience and leadership roles. Conscientiousness was found to be the most influential predictor across all safety attitudes, including teamwork in emergencies and effective communication. Both total and role-specific work experience have the potential to contribute positively to safety culture and adherence to procedures. The potential integration of personality assessments into safety training may contribute to a positive safety culture and, thus, improved safety performance. The study contributes to the body of knowledge on personality-informed safety interventions. Also, it highlights the benefits that may be brought via tailored Maintenance Resource Management (MRM) training approaches for aviation maintenance personnel.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681458</guid>
    </item>
    <item>
      <title>Integrating electrical resistivity tomography into predictive thermal modeling of permafrost beneath railway infrastructure: Case study of the Hudson Bay Railway</title>
      <link>https://trid.trb.org/View/2647846</link>
      <description><![CDATA[This study investigates the integration of electrical resistivity tomography (ERT) data into predictive thermal modeling of permafrost conditions at three sites along the Hudson Bay Railway in northern Manitoba. The model was initially calibrated using borehole temperature data collected under undisturbed natural conditions, followed by calibration of the subsurface temperature regime beneath the railway embankment using ERT-derived resistivity fields. The calibrated model was then used to forecast the ground temperature evolution over a 30-year period, supporting the assessment of infrastructure stability and long-term maintenance planning. This integrated approach demonstrates the value of ERT in locations where conventional ground temperature monitoring is limited or infeasible. By improving the spatial resolution of initial model conditions, the methodology enhances predictive accuracy, supporting better-informed design strategies and mitigation measures for infrastructure projects in permafrost regions.]]></description>
      <pubDate>Thu, 02 Apr 2026 16:58:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647846</guid>
    </item>
    <item>
      <title>Study on the influence of temperature field during thawing and sinking process of tropical undersea tunnel based on pipe curtain freezing method</title>
      <link>https://trid.trb.org/View/2652386</link>
      <description><![CDATA[With the rapid economic development of tropical coastal cities, undersea tunnels have become a crucial component of urban three-dimensional transport infrastructure. However, in addition to traditional construction challenges, tropical undersea tunnels also encounter significant risks related to freezing, thawing, and subsidence. The pipe curtain freezing method is the primary technique employed to address the issues of thawing and sinking of soft strata during the construction of tropical undersea tunnels. Inaccurate understanding of the variations in the thawing temperature field can result in rapid settlement during the thawing process, making the study of the thawing temperature field a critical issue. This study, set against the backdrop of the Sanya estuary channel project, employs both physical similarity tests and numerical simulations to validate findings mutually. It systematically elucidates the evolution of the forced thawing temperature field and the thawing behavior of permafrost using the pipe curtain freezing method. The results indicate that forced thawing significantly reduces the thawing cycle of the soil mass. Specifically, the temperature rise rate at monitoring points is faster the closer they are to the freezing tubes, followed by a brief phase change latent heat period; conversely, the further the distance from the tubes, the longer the phase change duration. The trends in temperature changes observed through both research methods during the thawing process are largely consistent, with temperature differences ranging from 1.5 °C to 2 °C, confirming the reliability of the numerical model. Furthermore, the thawing duration of the soil mass markedly decreases as the temperature of the circulating hot water increases. However, this effect becomes negligible when the circulating hot water temperature reaches 50 °Cor higher, indicating a threshold state between the thawing duration and water temperature increase, wherein thawing does not decrease linearly with temperature. The study establishes that there is an optimal thawing temperature for the pipe curtain freezing construction in tropical underwater tunnels, highlighting the importance of selecting an appropriate thawing temperature during actual construction processes.]]></description>
      <pubDate>Thu, 02 Apr 2026 16:58:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652386</guid>
    </item>
    <item>
      <title>Data-driven natural gas pipeline reliability evaluation focusing on the mitigation effectiveness for frost heave in cold regions</title>
      <link>https://trid.trb.org/View/2652384</link>
      <description><![CDATA[Natural gas pipelines are widely distributed across cold regions, where they are threatened by frost heave. Parameter uncertainty and the complex mechanism of frost heave limit the reliability assessment of pipelines in cold regions. This study presents a novel framework to overcome these limitations in implementing reliability analysis. In the framework, the Monte Carlo Simulation (MCS) method is incorporated to quantify uncertainty by generating a larger number of samples. A data-driven Back Propagation Neural Network (BPNN) model is developed to avoid the complex Limit State Function (LSF) for calculating pipeline damage. A closed-form Elastic Foundation Beam Model (EFBM) is developed to evaluate frost-heave-induced pipeline damage and to generate the database for training the BPNN model. The results indicate that the developed BPNN model can accurately predict frost-heave-induced bending stress, with a maximum error of 13.6 MPa. From the perspective of hazard mitigation, the finding reveals that targeting the frost-heave height is the most effective measure for improving reliability, which reduces additional failure probability by 18% and 42% compared with other measures. As mitigation levels increase, the uncertainty-induced pipeline failure probability discrepancy reaches 10.7%. The analysis results can guide targeted management strategies to improve the structural resilience of pipelines in cold regions.]]></description>
      <pubDate>Thu, 02 Apr 2026 16:58:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652384</guid>
    </item>
    <item>
      <title>Thawing permafrost under Qinghai-Xizang Highway and its impacts on road performance based on multi-source observed data</title>
      <link>https://trid.trb.org/View/2652373</link>
      <description><![CDATA[The Qinghai-Xizang Highway (QXH) exhibits widespread pavement damages because of underlying permafrost thawing. To comprehensively reflect the pavement damages and their controlling factors, images were processed and compared from ground penetrating radar (GPR) and unmanned aerial vehicle (UAV) in 7 typical sections along the QXH in the permafrost regions. The field monitoring data of ground temperature, embankment deformation were also collected to jointly investigate distribution, formation process and development mechanisms of roadway distress based on multi-source data. Indices such as distress ratio, pavement roughness and lateral deformation of the QXH were calculated by image segmentation and spatial analysis based on the UAV images. Results showed that (1) the temporal-spatial distribution of standard deviation of pavement altitude from the UAV image can quantitatively reflect the pavement roughness caused by embankment settlement and and vehicle loading during the roadway operation. The standard deviation has the maximum of difference of with 20–30 cm/a. (2) The average lateral deformation of the QXH can be extracted from the UAV image in thick embankment sections, which was 0.09 m/a in 4 of the 7 selected sections (K3059, K3119, K3177 and K3188). (3) Field monitoring data revealed the climate warming and permafrost thawing along the QXH. The GPR results and the UAV image can mutually verified for the explanation for the formation and development of the pavement damages. The findings can provide a comprehensive analysis method for the pavement damage and embankment distress based on multi-sourced data, and scientific guide for distress prediction and roadway maintenance.]]></description>
      <pubDate>Thu, 02 Apr 2026 16:58:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652373</guid>
    </item>
    <item>
      <title>When less travel means more carbon: How rainfall-induced shifts from public transit to cars increase urban transport emissions</title>
      <link>https://trid.trb.org/View/2649678</link>
      <description><![CDATA[Climate change is expected to increase the frequency and intensity of extreme precipitation events, with significant implications for urban mobility and the associated carbon emissions. This study examines how rainfall-induced changes in travel demand vary by mode of transportation and how these differences can alter per-passenger carbon intensity and total system-level emissions. Using daily panel data for 2024 from two administrative districts in Busan, South Korea, precipitation elasticities were estimated separately for private cars, buses, and the metro. The results show that rainfall reduces travel demand across all modes—higher rainfall can lower total system-level carbon emissions. Nevertheless, private car use is less elastic to rainfall than public transport; many car users may continue traveling in adverse weather conditions. This lower sensitivity can increase per-passenger carbon intensity in urban transport systems. Furthermore, if rainfall prompts a modal shift from public transport to private cars among mandatory trip makers, the total system emissions may increase despite an overall decline in travel demand. By combining mode-specific elasticity estimates with carbon intensity factors, this study identifies the thresholds at which rainfall-driven mode shifts result in net emission increases. These findings highlight the importance of transport adaptation strategies in mitigating the risk of adverse modal shifts resulting from changing precipitation patterns.]]></description>
      <pubDate>Wed, 01 Apr 2026 11:47:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2649678</guid>
    </item>
    <item>
      <title>From roads to water organisms? – Sequential extraction indicates high bioavailability of Cd, Zn and Pb from tire and road wear particles from a highway tunnel and go-kart tracks</title>
      <link>https://trid.trb.org/View/2648262</link>
      <description><![CDATA[Tire wear particles (TWP) are a rising issue with emerging ecotoxicological concerns, so that the new Euro 7 regulation is going to set emission limits for tire abrasion. On road surface, TWP incorporate various (non-)traffic related particles (containing heavy metals) forming tire and road wear particles (TRWP). During rainfall, these composite particles are mobilized via drainage systems into the aquatic environment, where TRWP may release incorporated and adsorbed heavy metals from the road into the surrounding water. Consequently, these heavy metals may become bioavailable to aquatic organisms. To evaluate the potential release of heavy metals from TRWP and their bioavailability, we applied a sequential extraction (Community Bureau of Certified References, BCR) to TRWP samples from a highway tunnel and go-kart lanes. For TRWP samples in the aquatic environment, sequential extraction indicated high bioavailability for Cd, Zn (> 50 % under aerobic conditions) and Pb (> 70 % under anaerobic conditions). The relative mobility of the investigated trace elements followed the order: Zn > Pb > Cd >> Cu > Co > As >> Ni > Cr. Furthermore, experiments using river water from the Freiberger Mulde showed that newly adsorbed Cd and Zn on TRWP from the surrounding water are only weakly bound and thus readily bioavailable (Cd > 95 %, Zn > 85 %). These findings indicate that TRWP can significantly influence the mobility and bioavailability of heavy metals in aquatic systems, which is crucial for assessing ecological risks and predicting the potential impact of TRWP as carriers of toxic metals to aquatic biota.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648262</guid>
    </item>
    <item>
      <title>Research on connection stiffness and load transfer performance of precast assembled pavement panels</title>
      <link>https://trid.trb.org/View/2651542</link>
      <description><![CDATA[Prefabricated pavement systems provide a novel approach for rapid construction of airport pavement. It is worth further research on the connection stiffness and load transfer performance of prefabricated pavements with limited connection points. Currently, there is a lack of research on full-scale plate testing and dynamic load analysis in the assessment of joint stiffness and load transfer capacity for such pavements. Based on the design of prefabricated pavement panels at Guangzhou Baiyun International Airport, this paper conducted a falling weight deflectometer (FWD) test on a full-scale pavement model. The deflection at the bottom of prefabricated panels was measured, and the load transfer coefficients were calculated. By finite element simulations of transient dynamics and static mechanics, a comparative analysis of testing and finite element simulating results under static and dynamic loads was carried out. By introducing a connection stiffness coefficient, it was found that the relationship between the connection stiffness coefficient and the load transfer coefficient of deflection followed an “S”-shaped curve, and two formulas were fitted for simplified calculation of the load transfer coefficient in prefabricated pavements. Based on equivalent calculations of joint stiffness and comparisons with the test results, it was suggested that load transfer through connecting steel bars to the surrounding area did not follow a linear pattern. Additionally, even without panel connections, a load transfer coefficient of 20% to 30% can be achieved relying solely on the monolithic base layer, and this value was independent of the modulus of reaction at the base. The research contributes to the design of joint stiffness and the calculation of load transfer coefficients in prefabricated pavements, thereby ensuring their practical engineering applications.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:13:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651542</guid>
    </item>
    <item>
      <title>Risk analysis of domino effect of leakage accident of petrochemical pipeline based on analytic hierarchy process and fuzzy fault tree analysis</title>
      <link>https://trid.trb.org/View/2648945</link>
      <description><![CDATA[Pipeline transportation is a prevalent method for the conveyance of petrochemical fluids. Pipeline leakage can lead to severe consequences, such as fire or explosion accidents. Unlike traditional methods, which take pipeline leakage/failure as the top event of fault tree analysis (FTA), this paper studies the domino effect of pipeline leakage with the top event of the fire or explosion accident caused by petrochemical pipeline leakage. The risk assessment is based on the hybrid method combining the Analysis Hierarchy Process (AHP) and fuzzy theory. AHP is established to evaluate the ability of experts and fuzzy theory is used to convert the experts’ opinions into occurrence probabilities of basic events. The effectiveness of the approach is demonstrated by performing a risk assessment in a long-distance oil pipeline. Qualitative analysis results based on the structural importance indicated that the formation condition of the combustible mixture has the largest structural importance. Quantitative analysis results showed that the proposed method, based on AHP and fuzzy theory, can evaluate the risk of the pipeline domino effect, which can be used to support risk management and decision-making for petrochemical pipelines.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:20:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648945</guid>
    </item>
    <item>
      <title>Capturing macro trend and uncertainty in speed-related CO₂ emission rates for heavy-duty diesel trucks</title>
      <link>https://trid.trb.org/View/2651541</link>
      <description><![CDATA[Accurate estimation of CO₂ emissions from heavy-duty diesel trucks (HDTs) is critical for researchers and policymakers, but remains challenging due to uncertainties in vehicle operating conditions and engine emission factors (EFs), especially when working with limited input data such as trace average speed and operating length (duration or mileage) at the macro scale. In this context, capturing the uncertainty in HDTs’ CO₂ emission estimates under various operating conditions and modeling scales becomes even more critical. This study analyzes and quantifies the macro trend and uncertainty in CO₂ emission rates, expressed as emissions per 100 km traveled, in relation to trace average speed at different trace aggregation classes. Utilizing half-yearly second-by-second trajectory and corresponding CO₂ emission data from 28 HDTs in China, the research examines trace aggregation classes, including durations ranging from 10 min to 120 min, and mileages ranging from 3 km to 100 km. Results show that CO₂ emission rates decrease rapidly at lower speed bins (< 15 km/h) and stabilize at higher speed bins across all classes. The greatest uncertainty occurs at lower speeds (around 5 km/h) in duration classes due to cumulative uncertainty over time and at intermediate speeds (around 30 km/h) in mileage classes due to variability in operating conditions and EFs. Increasing trace aggregation length significantly reduces CO₂ emission rate uncertainty. This study offers insights into the median and 95-percent confidence intervals of CO₂ emission rates from HDTs at specific speed bins and aggregation classes, providing valuable information for evaluating uncertainties in various modeling scales.]]></description>
      <pubDate>Thu, 26 Mar 2026 17:03:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651541</guid>
    </item>
    <item>
      <title>Research on the effects of train-induced wind on the thermal environment of tunnels in seasonally frozen regions</title>
      <link>https://trid.trb.org/View/2648463</link>
      <description><![CDATA[With the increasing operating speed of high-speed trains, the aerodynamic flow induced by trains during tunnel traversal exerts a growing impact on the thermal environment of cold-region tunnels. Based on the temperature monitoring data from the Wafangdian Tunnel, this study defines two thermal conditions: the positive-effect condition (P-Condition) and the negative-effect condition (N-Condition). Utilizing the validated numerical model, this study first analyzed the effect of train-induced wind on the tunnel thermal environment under the two conditions, then systematically investigated the regulatory roles of blocking ratio (B), train speed (v), and train length (L). The results indicate that under the N-Condition, the train-induced wind serves as a “cold pump,” which significantly enhances heat dissipation. This leads to a net heat loss of 85.22 × 103 kJ, thus impairing the tunnel's thermal insulation. Conversely, under the P-Condition, the wind operates as a “heat source,” generating a cumulative net heat gain of 168.47 × 103 kJ (equivalent to the heat from 20.12 kg of raw coal combustion, calorific value 8374 kJ/kg) and thereby benefiting the anti-freezing capacity. Furthermore, factors B, v, and L significantly regulate the tunnel thermal environment. Under N-Condition, higher values of these factors intensify heat dissipation, which is unfavorable for maintaining anti-freezing performance. Conversely, under P-Condition, increased levels promote heat accumulation within the tunnel, thereby enhancing freeze resistance. Based on this, during the operation, it is recommended to prioritize the monitoring of dynamic changes in the thermal condition, and further optimize the tunnel's anti-freezing performance by reasonably adjusting train operation parameters.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:45:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648463</guid>
    </item>
  </channel>
</rss>