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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Deep learning with entity embedding for offshore route-based wind speed prediction considering spatiotemporal correlation</title>
      <link>https://trid.trb.org/View/2638235</link>
      <description><![CDATA[Short-term wind speed prediction along the route is essential for ship safety and economic operation. Existing studies rely on regional prediction requiring sufficient wind field data that are difficult to obtain and computationally expensive. It is attractive to predict wind speed utilizing only data along the route. However, the wind data along the route contains sparse spatiotemporal information that limits accuracy improvement. To achieve efficient and accurate prediction, a route-based prediction framework integrating entity embedding and a hybrid convolutional neural network and gated recurrent unit (CNN-GRU) is proposed. The entity embedding transforms location nodes into embedding vectors to represent spatial correlation in embedding space. The CNN-GRU network recognizes complex temporal patterns. The wind data of an environmental monitor ship sailing in the Pacific Ocean are collected for verification. The results show the proposed model achieves higher accuracy and lower complexity than regional CNN-GRU model. Wind speed change is strongly related to the L2 norm of embedding vector so that entity embedding can accurately recognize the descent trend of the wind speed along the route, unlike previous studies extracting the feature of the wind speed rather than the wind speed change. The proposed model has potential for shipboard wind speed prediction due to high accuracy and fast speed.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2638235</guid>
    </item>
    <item>
      <title>AI-based System for Road Surface Condition Forecasting Using Multi-Source Meteorological Data</title>
      <link>https://trid.trb.org/View/2683917</link>
      <description><![CDATA[Accurate and timely forecasts of road surface conditions are crucial for efficient winter maintenance, enhanced traffic safety, and the optimized use of de-icing agents. Road surface phenomena, in complex fields present challenges to traditional forecasting methods due to their nonlinear and localized nature. This study presents a machine learning framework predicting real-time road states (dry, wet, icy, snowy) across Bavaria, Germany. It integrates data from over 516 Road Weather Stations (RWS), thermal measurements from winter maintenance vehicles, and elevation data from the Open Elevation API. Data undergoes temporal alignment, spatial interpolation, and missing-value imputation. Decision Trees form the core model for interpretability and nonlinear pattern handling. Each RWS employs a localized model, while a generalized version covers unmonitored roads via spatial adjustments. With over 85% accuracy, the system facilitates dynamic winter maintenance and minimizes resource waste. Cyber-physical in smart mobility and transportation networks support improved real-time hazard responses. This approach shows how scalable infrastructure can be made resilient using machine learning.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683917</guid>
    </item>
    <item>
      <title>Near-surface wind field in coastal mountain terrain under typhoon influence</title>
      <link>https://trid.trb.org/View/2672702</link>
      <description><![CDATA[The near-surface wind field during typhoon landfall is significantly influenced by terrain, particularly in coastal mountainous regions. Based on the WRF model, the near-surface wind field in coastal mountain terrain under typhoon influence is studied by using the simulation results of Typhoon Dujuan (1521). A sensitivity analysis was conducted to determine the suitable simulation period (60 h before landfall to 12 h after landfall) and the appropriate physical schemes (Medium-Range Forecast boundary layer scheme and Betts-Miller-Janjic cumulus convection scheme) for Typhoon Dujuan. The comparison between simulated and observed data revealed a high correlation, with a Pearson correlation coefficient of 0.91 during the wind speed mutation period. A comprehensive analysis was then performed, combining horizontal and vertical wind fields, wind speed, and wind direction, with respect to the terrain. This analysis explored phenomena such as wind speed mutations at the station, the stable position of maximum wind speed, and alternating high-low wind speeds in the horizontal wind field. Results indicate that the initial simulation time significantly impacts the typhoon path in complex geographic areas. Selecting an appropriate simulation period effectively reduces terrain-induced interference on the typhoon trajectory. Wind speed mutations during landfall are primarily driven by dynamic changes in the typhoon circulation, highlighting the close relationship between typhoon weakening and these mutations. Mountainous terrain notably alters the near-surface wind field, especially when the typhoon structure interacts with elevated terrain, causing pronounced alternating high-low wind speed patterns. Additionally, during landfall, the typhoon vertical structure becomes unbalanced, further contributing to its weakening. Through a thorough analysis of the near-surface wind field and mountainous terrain, a better understanding of wind speed variations during typhoon landfall is achieved. These findings provide a theoretical foundation for enhancing the accuracy of typhoon predictions in complex terrain conditions, thereby improving the reliability of typhoon path and intensity forecasts.]]></description>
      <pubDate>Tue, 14 Apr 2026 10:10:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672702</guid>
    </item>
    <item>
      <title>Enhanced Road Surface Temperature Prediction Using Random Forest Model and NWS Weather Forecast Data</title>
      <link>https://trid.trb.org/View/2562191</link>
      <description><![CDATA[Effective winter road maintenance depends on obtaining precise information about the temperature of the road surface. Transportation agencies typically utilize Road Weather Information Systems (RWIS) to monitor RST. However, due to the high costs associated with RWIS deployment, their availability is limited, especially in remote areas. The National Weather Service (NWS) provides high-resolution gridded weather forecasts, presenting an opportunity to estimate RST in areas without RWIS coverage by correlating these forecasts with RST using predictive models. However, current studies have not explored the application of Random Forest (RF) as a robust predictive model for estimating RST. This study evaluates the potential of the Random Forest model to enhance RST predictions based on NWS forecasts. The RF model was trained using a data set of actual RST measurements from North Texas roadways and corresponding NWS weather forecasts. The model’s performance was evaluated against other conventional predictive models, including Linear Regression, Deep Learning, Support Vector Machine, and K-nearest Neighbor. The Random Forest model demonstrated the highest accuracy, with an R-squared value of 0.96, a root mean square error of 1.37°C, and a mean absolute error of 1.03°C. This research advances road safety by offering more precise RST prediction models utilizing publicly available weather data. These models can be integrated into intelligent winter operations management systems, assisting highway agencies in obtaining reliable RST estimates in regions without RWIS.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562191</guid>
    </item>
    <item>
      <title>Ongoing Research Activities Supporting General Aviation Pilots and Weather Information Dynamics</title>
      <link>https://trid.trb.org/View/2631941</link>
      <description><![CDATA[Most weather-related accidents in General Aviation (GA) can be attributed to incorrect situation assessment and decision-making. The FAA’s Weather Technology in the Cockpit (WTIC) Program sponsors research to address these human factors challenges, which include introducing and evaluating new weather-related cockpit displays, mobile technologies, and applications by industry. In this session, a moderated panel will discuss ongoing WTIC-sponsored research projects being performed at different universities, aimed at understanding and addressing weather-related, GA issues. The projects relate to the (1) use of extended reality (XR) to enhance student pilot learning, (2) development of speech recognition technology to automate the pilot report (PIREP) submission process, and (3) assessment of pilot sensemaking and weather information usage in low altitude operations. The results of this collective work demonstrate ongoing and safety-focused research intended to support GA pilots through multiple stages of their flying experience.]]></description>
      <pubDate>Fri, 19 Dec 2025 16:53:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2631941</guid>
    </item>
    <item>
      <title>General Aviation Pilots’ Preflight Planning for Weather: A Behavioral Observation Approach</title>
      <link>https://trid.trb.org/View/2632050</link>
      <description><![CDATA[General aviation (GA) pilots are increasingly conducting weather self-briefings using online tools rather than consulting with experts, raising questions about the efficacy of these methods. This study used behavioral observation to analyze how 39 certificated GA pilots conducted preflight weather briefings using a simulated flight planning tool across two scenarios involving forecast icing and fog conditions. Participant behavior was compared against benchmark strategies developed by aviation meteorology experts. Researchers recorded product access, sequence, and duration. In both scenarios, pilots frequently overlooked key imagery products and used a linear “next” button to navigate products, often spending less than a second on critical content. These results suggest gaps in pilots’ weather literacy and/or interface usability challenges. This study provides a foundation for the guiding question: Do flight planning apps help rather than hinder pilots during weather planning? Findings support the development of more intuitive, user-centered briefing tools.]]></description>
      <pubDate>Wed, 17 Dec 2025 14:37:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632050</guid>
    </item>
    <item>
      <title>Reinforcement learning for optimizing hurricane evacuation decisions: Hurricane Irma case study</title>
      <link>https://trid.trb.org/View/2573738</link>
      <description><![CDATA[Tropical cyclone risks are expected to intensify with climate change, significantly complicating evacuation management. During Hurricane Irma in 2017, Florida faced its largest evacuation, impacting 6.5 million people and involving 4 million vehicles. Current hurricane simulations are slow and lack probabilistic modeling, making large-scale traffic evacuation management challenging due to incomplete risk profiles. This study presents a novel approach to optimize evacuation orders under uncertain weather conditions using the Pangu weather forecasting model and reinforcement learning. By perturbing Hurricane Irma’s forecast with the Pangu model, the authors create realistic decision-making scenarios. Reinforcement learning algorithms then optimize evacuation orders, considering factors such as travel time, sheltering risks, and overall safety. The authors' approach shows an 8% improvement in traffic system performance compared to traditional fixed evacuation orders. This work highlights the potential benefits of enhanced weather forecasting accuracy in improving evacuation strategies, offering a more adaptive and effective response to future tropical cyclones.]]></description>
      <pubDate>Mon, 28 Jul 2025 13:51:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573738</guid>
    </item>
    <item>
      <title>Creation of a Rapidly-Updating Road Weather Impacts Prediction System (RWIPS)</title>
      <link>https://trid.trb.org/View/2548657</link>
      <description><![CDATA[This proposal lays the framework for an end-to-end decision-support capability for the 
Missouri Department of Transportation (MODOT) that provides potentially impactful weather information coupled with a routing decision tool to aid in effective response to road weather hazards.  The project is a multi-stage one that will leverage expertise within NOAA and OU/CIWRO for real-time weather monitoring and forecasting and then expand upon capabilities developed and demonstrated by Missouri University Science and Technology.]]></description>
      <pubDate>Wed, 30 Apr 2025 09:20:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548657</guid>
    </item>
    <item>
      <title>Feature Selection-Based Irradiance Forecast for Efficient Operation of a Stand-Alone PV System</title>
      <link>https://trid.trb.org/View/2534081</link>
      <description><![CDATA[Solar irradiance (SI) forecasting and determination of the optimum tilt angle (OTA) of photovoltaic (PV) panels are key strategies for improving the power output of PV systems. Precise SI predictions offer valuable information regarding the predictable accessibility of solar energy, empowering PV system operators to make informed decisions for PV system optimization. This research uses a bi-directional long short-term memory (Bi-LSTM) hybrid network to forecast SI. Then, the OTA of the PV module is estimated by applying the forecasted SI data to the ASHRAE (American Society of Heating, Refrigerating and Air-conditioning Engineers) SI model. The performance of the Bi-LSTM hybrid network to estimate SI is compared with the observed data and the other existing forecasting models in the literature. The impact of OTA in improving PV power output is evaluated by comparing the solar irradiance received on both tilted and horizontal surfaces. This work has been experimentally implemented using the PV module setup at Thiagarajar College of Engineering, Madurai, Tamil Nadu, India. The OTA obtained by the proposed method yielded increased output PV power compared to all other tilt angle approaches in the literature.]]></description>
      <pubDate>Wed, 23 Apr 2025 16:10:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534081</guid>
    </item>
    <item>
      <title>Robust trajectory optimization under arbitrary uncertainty using ensemble weather forecasting and statistical aircraft performance model</title>
      <link>https://trid.trb.org/View/2495441</link>
      <description><![CDATA[Aircraft trajectory optimization can improve delay absorption and fuel efficiency; however, existing methods suffer from problems such as insufficient adaptability to uncertainty. This paper introduces a robust trajectory optimization algorithm for optimizing flight profiles in the presence of arbitrary uncertainties in weather forecasting and aircraft performance models. Ensemble forecast data are used to provide the uncertainty in the weather forecasts, and a statistical aircraft performance model based on flight data is utilized to provide the uncertainty in the aircraft performance model. Arbitrary polynomial chaos expansion is applied to efficiently quantify arbitrary uncertainties in aircraft dynamics, and incorporated into the trajectory optimization algorithm. The β-hill climbing algorithm, a stochastic local search algorithm developed recently, is employed to determine the optimal flight profiles. By conducting numerical simulations on a popular domestic flight route in Japan during time-based metering operations, this study assesses potential fuel savings by the optimal trajectories with speed control and step descent along the planned route, as opposed to radar vectoring. Actual flight data from past flights are utilized for evaluation, and average fuel savings of approximately 2% are expected. Based on the analysis conducted in this study, the effectiveness of the robust trajectory optimization algorithm and fuel savings for time-based metering operations are evaluated and demonstrated.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2495441</guid>
    </item>
    <item>
      <title>The possibilities of applying artificial intelligence in maritime navigation</title>
      <link>https://trid.trb.org/View/2488979</link>
      <description><![CDATA[This article discusses the application of artificial intelligence (AI) in maritime navigation. AI can significantly improve the navigation of ships using various technologies and algorithms. Several ways in which AI can be used in ship navigation have been explored, including autonomous navigation, pattern recognition, weather forecasting, fuel consumption optimisation, and condition monitoring. The implementation of AI in ship navigation has the potential to enhance safety, efficiency, and performance in maritime transport, whilst simultaneously reducing exhaust emissions and transportation costs.]]></description>
      <pubDate>Tue, 28 Jan 2025 14:52:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2488979</guid>
    </item>
    <item>
      <title>Information technology systems as both contributor to risk perception and adaptive behavior: Public transit agencies' response to extreme weather events</title>
      <link>https://trid.trb.org/View/2471461</link>
      <description><![CDATA[This paper aims to better understand how information strategies matter for the management of extreme weather events in public transit agencies. It examines how past extreme weather events and the availability of information on extreme weather impacts influence risk-related cognitions, which influence planning for future extreme events and eventually lead to investment in Information Technology (IT) systems as a protective measure. The theoretical framework integrates prior work on perceived risk, protection motivation theory (PMT), and theory of planned behavior (TPB) to develop hypotheses, which are tested using structural equation modeling with data from national surveys of transit agencies conducted at two points in time, 2019 and 2023, merged with other institutional data. The paper demonstrates how access to critical information about the impacts of past extreme weather events influences threat and coping appraisal, planning, and future investment in information technology systems. Findings show support that organization experience with extreme weather, risk perception and capacity are associated with more extensive planning for extreme weather in 2019, and that greater emphasis on planning leads to more investment in IT systems in 2023. The paper contributes to the broader discourse on the role of information and planning in strengthening organizational preparedness and adaptive capacity in the face of increasing climate challenges.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:11:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2471461</guid>
    </item>
    <item>
      <title>Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols</title>
      <link>https://trid.trb.org/View/2480733</link>
      <description><![CDATA[During winter, hazardous black ice can form anywhere on roads under various weather conditions, including fog, frost, rime ice, freezing rain, and snow. To prevent accidents caused by black ice, nighttime road maintenance patrols have been conducted in Korea since December 2019, following a tragic accident on slippery pavement. However, patrolling the entire road network on a daily basis requires substantial human and equipment resources. To address this issue, an approach to identify high-risk road sections and prioritize patrolling efforts on these selected sections needs to be established. The main challenge lies in identifying dangerous sections where road weather sensors have not been deployed. One potential solution is to forecast nighttime black ice using atmospheric data. In this context, the present study investigates machine learning techniques, including Random Forest, CatBoost, and Deep Neural Networks, for forecasting nighttime icing on rural highways in Korea. The models use air temperature, humidity, dew point temperature, precipitation probability, and wind speed as input variables. Data analysis indicates that nighttime icing occurs when the atmospheric temperature falls below 4 °C and the relative humidity exceeds 75 %. Furthermore, black ice is more likely to form when temperatures are rising rather than falling, particularly in the absence of precipitation. To evaluate the predictive models, reference data were obtained based on the physical principle that black ice forms when the road surface temperature drops below both the freezing point and the dew point temperature. The results show that all the models achieved similar performance, with an accuracy of approximately 85–90 %. The novelty of this study lies in predicting road icing using only readily available atmospheric data, which eliminates the need for costly road weather sensors. As a result, this approach allows for more efficient nighttime maintenance patrols, reducing resource usage by up to 60 % while still ensuring road safety.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:11:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2480733</guid>
    </item>
    <item>
      <title>Analyses of European Terminal Aerodrome Weather Forecasts in 2022 and 2023</title>
      <link>https://trid.trb.org/View/2470758</link>
      <description><![CDATA[Terminal Aerodrome Forecasts (TAFs) are essential components of aviation meteorology, providing critical information for flight safety and operational decision-making. This study conducts a comprehensive analysis of TAF for European airports during the years 2022 and 2023, leveraging Python functions accessible via a dedicated GitHub repository. The complexity inherent in TAF, characterized by diverse change groups, header formats, and regional variations, presents challenges for accurate interpretation. The analysis focuses on key parameters within TAF, including the count of corrected messages and the frequency and types of change groups. The count of corrected messages serves as a metric for evaluating the quality of service provided, while the examination of change group utilization reveals distinct patterns and tendencies specific to each airport. The findings underscore the significance of regional regulations, meteorologist decision-making, and adherence to International Civil Aviation Organization (ICAO) standards in shaping TAF. The GitHub repository and associated Python functions presented in this study provide valuable resources for meteorologists, researchers, and aviation personnel to conduct in-depth analyses and derive insights from TAF. Ultimately, this study identifies local differences and inconsistencies in the publication of TAF, laying the groundwork for enhancing their consistency and uniformity.]]></description>
      <pubDate>Fri, 27 Dec 2024 15:27:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2470758</guid>
    </item>
    <item>
      <title>Tourists’ Trip Adaptation Subjected to Advance Information on Impending Cyclone Affecting their Destination</title>
      <link>https://trid.trb.org/View/2475865</link>
      <description><![CDATA[Investigating the arrival of tourists subject to the forecast of an impending cyclone can help to understand the possible evacuation demand. This study focused on tourists’ decisions to either cancel, reduce stay duration, or continue entire stay duration of their upcoming trip after receiving information about an impending cyclone expected to affect their destination. The objective is to decipher the arrival of tourists and the potential evacuation demand. A stated preference survey, designed using the scenario variables 1) cyclone intensity, 2) spendable stay duration, and 3) days left to initiate the outward journey from home was conducted at Puri, the cyclone-susceptible tourist location in Odisha, India. Multinomial logit and nested logit models are developed. The tourists’ propensity to continue entire stay duration is significantly influenced by lower average age, male dominance, low income, and education of the group head. Tourists’ decisions are sensitive to the intensity of the impending cyclones and the sunk costs involved in the travel plan. Their inclination to reduce stay duration is observed to be predominant when they can spend at least two-thirds of their planned stay duration. Assessment of various possible evacuation demand levels and other evacuation pre-planning inputs are derived by investigating the impact of influential variables.]]></description>
      <pubDate>Mon, 16 Dec 2024 16:17:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475865</guid>
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