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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>The logic of empirical testing of accident prediction models</title>
      <link>https://trid.trb.org/View/2666569</link>
      <description><![CDATA[This paper explains the logic of empirical testing of accident prediction models. The key element of empirical testing is to make out-of-sample predictions of the number of accidents. This means that a model developed in sample A is applied, without modification, to predict the number of accidents in sample B. The procedure is illustrated in two samples formed by randomisation. A model fitted to the first sample was applied to predict the number of accidents in the second sample. The model was only partly supported. In general, any accident prediction model is likely to be merely a local statistical description of a particular data set. If tested by means of out-of-sample predictions, the model is very likely to be falsified. This does not mean that accident prediction models do not show general tendencies, but these tendencies are likely to be empirically supported only at a qualitative level, or at best an ordinal level of numerical measurement. In this sense accident prediction models are similar to many models developed in economics. The models predict the direction, and in some cases the relative strength of statistical relationships, but not their precise numerical values.]]></description>
      <pubDate>Thu, 05 Feb 2026 11:54:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666569</guid>
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    <item>
      <title>A refined ground-borne noise prediction methodology for railway traffic in tunnels in bedrock</title>
      <link>https://trid.trb.org/View/2666562</link>
      <description><![CDATA[The expansion of railway networks has significantly improved transportation efficiency but has also led to increased noise and vibration, particularly affecting residential areas near underground infrastructure. Ground-borne noise from rail traffic in tunnels can impact human health, structural integrity, and overall environmental quality. To support effective mitigation and infrastructure planning, accurate and reliable prediction models are needed. This study developed a model and methodology for predicting ground-borne noise from railway traffic in tunnels, tailored for Swedish conditions with high-quality bedrock. Existing models are often proprietary, with limited data available for Swedish bedrock conditions, and the handling of uncertainties is often insufficiently explained. This work addresses these gaps by developing a structured, multi-stage model to support the Swedish Transport Administration projects. The methodology consists of three stages, location, planning, and construction, each adapted to the level of data available. The model is valid up to 1~kHz and incorporates a source term along with correction terms for train speed, distance attenuation, ground-to-building coupling, vibration transmission through structures, and room acoustics. Statistical uncertainty is included for each term, ensuring robust predictions. The model is based on field measurements from the Gårda tunnel in Gothenburg and the Åsa tunnel in Varberg. To enhance understanding, numerical simulations were also conducted to investigate the effects of cracked bedrock zones and tunnel structures on vibration propagation. The simulations showed that the cracked zone causes frequency-dependent attenuation beyond the zone and amplification on the source side under idealized conditions. Tunnel structures were found to reduce vibration levels above the tunnel and introduce fluctuations at higher frequencies. Additional field tests were conducted in a tunnel under construction using both shaker and hydraulic hammer excitations to further refine the model by assessing vibration transfer to nearby buildings. While these tests allowed for a comparison between excitation sources, no significant vibration was detected at the house level.??As a result, a methodology and detailed prediction model is proposed for ground-borne noise assessment in Swedish Transport Administration projects.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666562</guid>
    </item>
    <item>
      <title>Deep learning-based driver intention recognition : evaluating performance, complexity and uncertainty estimations</title>
      <link>https://trid.trb.org/View/2666547</link>
      <description><![CDATA[Deep learning (DL) methods have advanced rapidly and are commonly applied in high-risk, resource-constrained environments such as advanced driver assistance systems (ADAS), where misclassifications can have serious consequences. With upcoming artificial intelligence (AI) legislation, it is essential to extensively evaluate and minimize the undesirable behavior of DL-based systems in such settings. An example is an ADAS that continuously evaluates whether a driver's intended maneuvers are safe to execute given the current traffic context. Driver intention recognition (DIR), which predicts the maneuver a driver intends to perform in the near future, is a central DL-based component of such systems. Since deep neural networks (DNNs) do not inherently provide uncertainty estimates for their predictions, probabilistic deep learning (PDL) methods can be applied to improve the identification of scenarios where model outputs may be unreliable. In this thesis, we first review the current state of DIR research, focusing on the recent shift toward DL methods. We then examine how both established and novel PDL methods influence DIR performance. We evaluate the uncertainty estimations by analyzing their ability to distinguish between correct and incorrect predictions and by measuring their effectiveness in out-of-distribution (OOD) detection. Furthermore, we employ neural architecture search with multiple objectives and search strategies to explore how architectural complexity impacts DIR and OOD detection performance. Finally, we conduct a comparative experiment to evaluate human performance against that of DL-based models in video-based recognition of road user intentions.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666547</guid>
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    <item>
      <title>Computational models for safe interactions between automated vehicles and cyclists</title>
      <link>https://trid.trb.org/View/2666519</link>
      <description><![CDATA[Cyclists, as vulnerable road users, face significant safety risks in traffic, especially at unsignalized intersections where they must interact with motorized vehicles. This PhD thesis investigated bicycle-vehicle interactions at unsignalized intersections and developed predictive models to improve active safety systems and automated driving. The research integrates naturalistic and simulator data to model the behavior of both cyclists and vehicles at intersections. The models included kinematic factors, non-verbal communication, and glance behavior. The studies included in this thesis revealed that kinematic factors, such as time to arrival (DTA), along with cyclists' non-verbal cues, like head movements and pedaling, significantly affect yielding behavior at intersections. Both simulator data and naturalistic data confirmed that visibility conditions and DTA played a critical role in cyclists' decision-making while subjective data from questionnaires highlighted the importance of communication and eye contact between cyclists and drivers in reducing the severity of interactions. Additionally, an analysis of naturalistic data uncovered differences in yielding behavior between professional and non-professional drivers, with professional drivers being less likely to yield to cyclists. Different models, leveraging machine learning and game theory, were developed to predict yielding decisions during these interactions. Lastly, simulator data was used to model drivers' behavior, incorporating kinematics, demographics, and gaze metrics to predict drivers' responses to crossing cyclists. The predictive models developed through this research provide novel insights for the design of threat assessment algorithms for active safety and automated driving, enhancing the machine ability to anticipate cyclist behavior and improve safety.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666519</guid>
    </item>
    <item>
      <title>Correlation- and physics based prediction of noise scenarios : final report</title>
      <link>https://trid.trb.org/View/2666510</link>
      <description><![CDATA[The aviation industry faces increasing pressure to reduce its environmental impact, particularly concerning overall sustainability and noise pollution. This project has attempted to address these issues by exploring various approaches to noise mitigation, environmental trade-offs in engine design, and by assessing flight procedures designed with environmental considerations in mind. The project resulted in five significant papers that contributed to understanding the intricate balance between noise, emissions, and operational efficiency in modern aviation technologies. The project's overarching goal was to create advanced predictive models and develop innovative strategies that enhance both environmental sustainability and noise management in aviation. Through detailed modelling, real-world testing, and psychoacoustic evaluation, the project laid the foundation for improving aircraft designs, flight procedures, and community engagement strategies.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:33:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666510</guid>
    </item>
    <item>
      <title>Navigating within the planetary limits : a prospective life cycle environmental sustainability assessment in support of the energy transition in Swedish aviation</title>
      <link>https://trid.trb.org/View/2666486</link>
      <description><![CDATA[Despite its social and economic benefits, aviation is notoriously known for its impacts on the environment, particularly climate change. In 2023, direct emissions from aviation accounted for approximately 2% of global greenhouse gas emissions, and without intervention, they are projected to increase by two to fivefold compared to 2023 levels by mid-century. To advance our knowledge of aviation sustainability and inform energy transition pathways, this thesis assesses the environmental sustainability of future air travel powered by alternative fuels and novel propulsion systems, using Sweden as a representative case. Due to its multi-dimensionality, aviation is conceptualized from a socio-technical system perspective, where the interplay between political, economic, social, technological, and ecological issues is considered. Using prospective life cycle assessment and absolute environmental sustainability assessment, the potential environmental performance of future air travel in Sweden is evaluated both in relative terms and from an absolute perspective. These different approaches seek to determine whether air travel supported by alternative fuels and novel propulsion technologies can offer environmental advantages over fossil kerosene, and if so, whether they can operate within the planetary limits. The results suggest that while alternative fuels and novel propulsion systems can support air travel with a lower climate change impact than that of fossil kerosene, these travel alternatives may have a relatively higher potential to degrade the overall environment, demonstrating significant burden-shifting between environmental problems, across sectors, geographies, and time scales. When assessing future air travel in an absolute sense, the results indicate that the potential environmental impacts associated with Sweden's projected air travel in 2050, even with advanced technologies, could overshoot the climate change and biodiversity loss thresholds by several orders of magnitude.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:32:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666486</guid>
    </item>
    <item>
      <title>Physics informed grey box modelling of ship dynamics</title>
      <link>https://trid.trb.org/View/2666475</link>
      <description><![CDATA[This project investigates the enhancement of ship manoeuvring models through the integration of prior knowledge embedded in parametric model structures and semiempirical formulas. The research is driven by the question: How can prior knowledge be used to enhance the generalization of ship manoeuvring models? The study begins focusing on one degree of freedom in ship roll motion, aiming to develop parameter identification techniques and propose a parametric model structure with good generalization. This knowledge is then extended to the manoeuvring problem, with objectives including the development of parameter identification techniques for ship manoeuvring models, proposing a generalizable parametric model structure, mitigating multicollinearity between model parameters, and identifying added masses. Methodologically, the research employs various parametric model structures for roll motion and manoeuvring, investigated through free running model tests and virtual captive tests (VCT). A novel parameter identification method combining inverse dynamics with an extended Kalman filter (EKF) is proposed. Additionally, a deterministic semi-empirical rudder model is introduced to address multicollinearity issues. Key findings indicate that inverse dynamics regression is an efficient method for parameter identification in parametric models. The proposed quadratic model structure for roll motion demonstrates good generalization, and the new parameter identification method accurately predicts manoeuvring models from standard manoeuvres. However, challenges with multicollinearity and the need for more informative data are highlighted. The study concludes that semi-empirical formulas can guide identification towards more physically correct models, and VCT can provide the necessary data for accurate model identification. The implications of this research suggest that integrating semi-empirical rudder models and utilizing VCT can significantly enhance the accuracy and generalization of ship manoeuvring models, contributing to more reliable and physically accurate simulations in maritime engineering.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:32:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666475</guid>
    </item>
    <item>
      <title>A Simplified Method to Estimate the Low Temperature Cracking Required Input for the AASHTOWare ME Using E* Data</title>
      <link>https://trid.trb.org/View/2633012</link>
      <description><![CDATA[During the late 1980s/early 1990s the use of the Indirect Tensile (IDT) creep test was developed as part of the Strategic Highway Research Program for evaluation of the cold temperature performance of asphalt mixes and then subsequently used to estimate the development of cold temperature thermal cracking of asphalt pavements. This computational procedure was then implemented in the mechanistic-empirical pavement design guide, now called the AASHTO PAVEMENT-ME. The method required that Poisson's ratio is measured, with these measurements used to estimate the creep compliance, D(t), master curve; the principal input to the prediction method. However, this method of testing has proven difficult for laboratories and there has been a slow industry adoption. Pavement life calculations within PAVEMENT-ME also make use of a complex modulus, E*, master curve. This method has become reasonably routine for laboratories with acceptable accuracy and precision. Mathematically, interconversions exist between the two measurement types. As such, this paper explores the possibility of using the E* data to provide an estimation of the D(t) data as an alternative to running the IDT creep test. This paper further describes the work performed by these researchers and a frame work is proposed for a practical implementation of this method.]]></description>
      <pubDate>Tue, 20 Jan 2026 11:16:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633012</guid>
    </item>
    <item>
      <title>Safety performance functions in a road environment with automated vehicles</title>
      <link>https://trid.trb.org/View/2640566</link>
      <description><![CDATA[The reduction of road fatalities can be achieved by intervening in various aspects, including infrastructure, transportation policy, vehicles, and driver behavior. One of the most promising solutions to solve this issue is to rely on Automated Vehicles (AVs), which can prevent human errors, which account for most crashes. However, the impact of AVs on road safety is still unquantifiable. The reason resides in a lack of observed data, as well as in the uncertainty about AV introduction on roads and their interaction with other vehicles and users. In this paper, a methodology to predict the impact of AVs is proposed, relying on Safety Performance Functions (SPFs). An ad hoc SPF for AVs has been developed just for multivehicle crashes, based on a set of market penetration rates, to propose a mathematical model that can include recent technological innovations in road traffic and be adapted to other contexts. Considering the area of the Province of Bari and three different time horizons, crashes were simulated with the presence of AVs in different traffic scenarios. The proposed scenarios were taken from extensive literature studies about the deployment of AVs. The SPF for the predicted crashes was developed by adding one coefficient that considers the presence of AVs to the baseline equation, controlling for the road geometry. The fitted models show a satisfactory goodness-of-fit, based on different metrics, including CuRe (Cumulative Residuals) plots.]]></description>
      <pubDate>Fri, 19 Dec 2025 10:03:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640566</guid>
    </item>
    <item>
      <title>AR- und KI-gestützte Analyse von Schadensentwicklungen in der Bauwerksprüfung</title>
      <link>https://trid.trb.org/View/2603392</link>
      <description><![CDATA[Methoden bildbasierter Verfahren zur Zustandserfassung und -bewertung in Verbindung mit Künstlicher Intelligenz (KI) haben Entwicklungspotenzial, um Bauwerksprüfungsprozesse zu digitalisieren und effizienter zu gestalten. Untersucht werden soll, wie Bildfolgen von Bauwerksschäden, zum Beispiel von Rissen insbesondere an Brückenbauwerken, mittels AR-/MR-fähiger Geräte (beispielsweise Tablet, Smartphone, Brille) anwendergerecht analysiert und visualisiert werden können. Schadensverläufe sollen nach automatisierter Erfassung vor Ort und Lokalisierung im Digitalen Zwilling beispielsweise KI-gestützt chronologisch geordnet, zueinander in Relation gesetzt und unter Einbezug des Faktors Zeit objektiv bewertet werden. Anschließend sollen exemplarische Prognosen zur weiteren Schadensentwicklung gegeben werden können. Durch die automatisierte Auswertung von Schadensentwicklungen können Prüfende messbar unterstützt und zeitlich entlastet werden. Mit der Bildung neuer Datenrelationen und ihrer Implementierung im Digitalen Zwilling erhalten Prüfende einen verbesserten Überblick über relevante Bauwerksinformationen. Bauwerkseigentümern wird ermöglicht, kostenintensive Schadensausweitungen und Folgeschäden frühzeitig zu erkennen, und die Lebenserwartung von Brückenbauwerken genauer einzuschätzen. Auch verkehrsträgerübergreifend ist ein hoher Mehrwert durch das Projekt zu erwarten. ABSTRACT IN ENGLISH: Image-based methods for condition assessment and evaluation in conjunction with artificial intelligence (AI) have potential for digitising building inspection processes and making them more efficient. The aim is to investigate how image sequences of structural damage, for example cracks, particularly in bridge structures, can be analysed and visualised in a user-friendly manner using AR/MR-enabled devices (e.g. tablets, smartphones, glasses). After automated recording on site and localisation in the digital twin, damage progression is to be chronologically ordered, correlated and objectively evaluated, for example with the aid of AI, taking the time factor into account. Subsequently, it should be possible to provide exemplary forecasts for further damage development.]]></description>
      <pubDate>Wed, 24 Sep 2025 10:15:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2603392</guid>
    </item>
    <item>
      <title>Where are we heading, and where do we want to go? : exploring transport system futures, climate targets and new mobility services</title>
      <link>https://trid.trb.org/View/2598647</link>
      <description><![CDATA[This thesis is written at a time when a considerable gap exists between the current trajectory of the transport system and developments in line with societal goals, such as those set to reduce greenhouse gas emissions. Responding to the challenge, the thesis explores what futures aligned with societal goals can entail. Transport system scenarios fulfilling climate goals are developed and analysed, examining the potential of electrification, biofuels, and vehicle efficiency. The thesis also pays special attention to New Mobility Services (NMS), which enables mobility through vehicle sharing and ridesharing. In addition to developing scenarios where NMS are included, a review explores other studies in which target-fulfilling scenarios have been developed, to understand possible future roles of the services. The thesis also investigates potential contributions from mobility and accessibility services to reduce transport volumes, facilitating a modal shift from car use to alternative transport modes, and enhancing the environmental performance of cars when in use. Potential tensions associated with NMS are also explored, which can be barriers to successful implementation at scale.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598647</guid>
    </item>
    <item>
      <title>Consequences of large-scale hydrogen use in the European transportation sector : geospatial modeling of infrastructure, electricity costs, water risk, and land use</title>
      <link>https://trid.trb.org/View/2598610</link>
      <description><![CDATA[To decrease the greenhouse gas emissions from transportation and industry, the use of electrolytic hydrogen produced from renewable electricity and water has been suggested, to substitute fossil energy and feedstock. But electrolytic hydrogen hasn't been used directly in these sectors on a large scale before, and it hasn't been produced in large quantities in Europe either. This thesis analyses potential consequences of future hydrogen use and production across Europe, in transportation including trucks, shipping, aviation, and industries including steel, ammonia, high value chemicals, and fuel production. Assessments are based on a geospatially specific model built for this thesis. This model simulates specific geographical locations of hydrogen demand for transportation and industry, over a full year, which allows for modeling impacts with more consideration to local context. For transportation, the demand is modeled using detailed logistics data, which allows allocating demand with consideration to transportation flows. For trucks, demand is allocated along logistics route considering power demand due to differentiated influence from road speed and topography, which is shown to significantly impact the simulated location of hydrogen refueling stations. The geospatial hydrogen demand data is used for four assessments: 1. evaluating implications of the EU Alternative Fuels Infrastructure Regulation (AFIR), and analyzing effects of different fuel mix scenarios on 2. electricity cost, 3. water risk, and 4. land use.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598610</guid>
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      <title>The effect of crowding and comfort in public transport on travel choice : empirical pilot study</title>
      <link>https://trid.trb.org/View/2598561</link>
      <description><![CDATA[It is well known that crowding and comfort are important aspects for public transport passengers. Crowding and comfort are closely linked: one effect of high crowding is that comfort is perceived as lower through, for example, less freedom of movement and less chance of getting a seat. On-board comfort also depends on other factors such as noise, vibration, jerky acceleration and braking, seat design, and so on. This feasibility study focuses on in-vehicle crowding and the part of the perceived comfort that is due to crowding. Crowding occurs when so many people want to travel the same route at the same time that the amount of people approaches or exceeds the capacity of the public transport system. At the same time, crowding has a deterrent effect that makes some travelers choose other travel options. In order to calculate realistic passenger flows on different route segments and travel times between different origins and destinations in model-based forecasts, the deterrent effect of crowding needs to be calibrated against people's actual behavior. The purpose of this project has been to investigate the possibility of calibrating the effect of crowding in public transport on travel choices of travelers using ticket validation data and complementary supply data from the Transport Administration in Region Stockholm. The choices referred to are primarily route choices. We hope that the research will provide a better understanding of how comfort effects of crowding affect travelers’ route choices and how these effects can ultimately be integrated into forecasting tools such as Sampers together with Emme through changed algorithms, variables or parameter values. Better modeling of crowding effects can provide more accurate forecasts of passenger flows in public transport, as well as more accurate valuations of benefits that arise when congestion levels are affected by investments or policy measures.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:18:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598561</guid>
    </item>
    <item>
      <title>Calibration of Highway Safety Manual crash prediction models for rural intersections: a case study from Delaware</title>
      <link>https://trid.trb.org/View/2563049</link>
      <description><![CDATA[]]></description>
      <pubDate>Tue, 10 Jun 2025 14:47:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563049</guid>
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    <item>
      <title>Forecasting road incident duration using machine learning framework</title>
      <link>https://trid.trb.org/View/2550861</link>
      <description><![CDATA[]]></description>
      <pubDate>Wed, 07 May 2025 13:46:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550861</guid>
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