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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>Enhancing Rail Obstacle Detection Systems: Optimizing Accuracy in Adverse Weather Conditions</title>
      <link>https://trid.trb.org/View/2711639</link>
      <description><![CDATA[Railway safety is paramount, especially with the increasing reliance on rail transport and the potential for catastrophic consequences from train colliding with obstacles. This paper introduces a novel obstacle detection methodology using Convolutional Neural Networks (CNNs) to enhance detection accuracy, particularly for diverse and unforeseen obstacles, including wildlife intrusion, under challenging environmental conditions. We employ the state-of-the-art (You Only Look Once) YOLOv11-Seg algorithm for simultaneous rail segmentation and obstacle detection, defining a critical safety margin around the tracks. A key contribution of this work is a novel synthetic image generation algorithm designed to address the critical scarcity of real-world obstacle data, particularly for rare and unpredictable hazards such as animals and uncharacterized debris. This algorithm strategically places various obstacles, extracted from diverse sources, at random locations on the rail or within the safety margin. Crucially, it incorporates diverse and realistic environmental conditions, such as train vibrations, rain, snow, dust, fog, and varying light intensities to augment the training data and improve the model’s robustness against these highly transient events. Experimental results demonstrate the effectiveness of the YOLOv11-Seg network, trained on our synthetically augmented data set, in accurately performing both segmentation and obstacle detection in a single step, paving the way for improved railway safety systems.]]></description>
      <pubDate>Fri, 05 Jun 2026 11:27:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711639</guid>
    </item>
    <item>
      <title>Analytical Investigation of Time Correction in Alpha-Beta Tracking Filters with Application to En Route Tracking</title>
      <link>https://trid.trb.org/View/2701095</link>
      <description><![CDATA[In the analysis of the α-β tracking filter, it is normally assumed that the tracking filter and data source operate in synchronism at a constant data rate. An analytical solution is obtained for the case in which the tracking filter and data source operate asynchronously, thus violating the standard assumptions. To compensate for the asynchronous operation of the filter, the technique of time correction is used to adjust the measured data point via the estimated velocity which approximates the synchronous operation of the filter and data source. The tracking filter performance in the steady-state case where time correction is used is better than that obtained from a fixed-parameter tracking filter in which the actual random time intervals between measurements are used as the temporal basis of filter operation. To ensure no degradation in system performance for purposes of air traffic control, a system timing accuracy on the order of 0.05 second is required to preserve the position measurement accuracy rather than the presently used technique which yields a timing accuracy on the order of 0.8 second. If the specified level of timing accuracy is not achieved, then it's postulated that significant errors will be introduced in the predicted position for maneuvering targets. System timing errors are presently the limiting factor in providing accurate position measurements for en route purposes and will partially nullify the data accuracy which will be available in the future.]]></description>
      <pubDate>Sun, 31 May 2026 16:45:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701095</guid>
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    <item>
      <title>Level of detail in visualization for human autonomy teaming: Speed, accuracy, and workload effects</title>
      <link>https://trid.trb.org/View/2680925</link>
      <description><![CDATA[For human autonomy teaming, information for promoting transparency could lead to information overload, negatively impacting performance and workload. This paper presents an empirical study investigating how different levels of detail (LODs) about the autonomy represented on the user interface would influence speed, accuracy, and workload. Specifically, we compared visualizations of a lost person model at four different LODs to aid in directing human and unmanned aerial vehicles searchers in search and rescue missions. The lowest LOD was found to support higher accuracy but at the expense of speed. The highest LOD induced the highest workload, while the other three LODs induced lower and similar levels of workload. The results indicate that the LOD in transparent displays could induce a speed and accuracy tradeoff.]]></description>
      <pubDate>Sat, 02 May 2026 15:47:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680925</guid>
    </item>
    <item>
      <title>Towards Robust Global VINS: Innovative Semantic-Aware and Multi-Level Geometric Constraints Approach for Dynamic Feature Filtering in Urban Environments</title>
      <link>https://trid.trb.org/View/2659107</link>
      <description><![CDATA[In real-world scenarios with predominant dynamic objects, achieving robust and accurate positioning using Visual-inertial navigation systems (VINS) poses a challenge because these objects dislocate visual features, resulting in degraded feature tracking accuracy, pose deviations, and trajectory drift. Thus, static scene assumption, as proposed in some existing studies, fails in such scenarios. Meanwhile, directly removing potential dynamic objects (either stationary or moving) using deep learning methods degrades accuracy in low-texture scenes, while using image geometric constraints poses challenges when moving objects dominate the scene. To address this, we introduce a real-time global VINS that incorporates an innovative semantic-aware and multi-level geometric constraint approach for better handling of moving objects. Precisely, a feature grading module combining the power of scene cognition and spatial feature extraction is developed to categorize tracked features. This module integrates multi-geometric constraints and semantic information to effectively eliminate dynamic features and employs VI-based motion consistent constraint to eliminate missed detection of the moving objects. Then, a descriptor-matching tracker module is applied to reject mismatches and enhance feature-matching reliability. Uneven feature distribution issue resulting from intensive dynamic feature elimination is addressed by a proposed geometry feature distribution-based auto-adaptive covariance estimation Algorithm. The backend handles long-term pose estimation drift by developing an adaptive multilayer VI-GNSS optimization framework that integrates a subsystem failure detection mechanism. The system performance demonstrates efficient identification and exclusion of dynamic features while retaining static ones. Experimental validation conducted on various datasets in urban dynamic areas reflects the superiority of the proposed method in accuracy and robustness.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659107</guid>
    </item>
    <item>
      <title>Discrepancies in media reporting of fatal road crashes and official data in India</title>
      <link>https://trid.trb.org/View/2611421</link>
      <description><![CDATA[Road traffic crashes pose a significant global public health challenge, influenced by media representations that shape public perceptions and policymaking. This study examines discrepancies between media-reported and officially documented fatal crash data to understand these differences and their impact on road safety. Utilizing statistical comparisons and qualitative content analysis, the research contrasts fatal road traffic crash (RTC) data from India's Times of India (TOI) with official statistics from the Ministry of Road Transport and Highways (MORTH). Statistical analyses reveal that media reports disproportionately emphasize crashes involving younger victims and four-wheel vehicles, in contrast to official records identifying two-wheelers as the most affected road users. Additionally, visually dramatic crash types, such as head-on collisions and vehicle overturns, are significantly overrepresented in media coverage compared to their true frequency. Further content analysis of news articles illustrates inconsistent and often biased attribution of responsibility, frequently influenced by the victim's road user category. Vulnerable groups, particularly pedestrians and cyclists, are disproportionately blamed, whereas occupants of larger vehicles receive more detailed coverage with lesser attribution of crash responsibility. Importantly, media narratives frequently deviate from the World Health Organization's recommendations, failing to highlight systemic and preventable aspects of RTCs. These findings highlight the critical need for media adherence to factual, balanced reporting guidelines to enhance public awareness and promote effective, evidence-based road safety interventions.]]></description>
      <pubDate>Tue, 21 Apr 2026 09:29:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611421</guid>
    </item>
    <item>
      <title>A Train Positioning Method Based on Satellite Navigation System and Electronic Map</title>
      <link>https://trid.trb.org/View/2113861</link>
      <description><![CDATA[Due to the characteristic that the measurement error diverges with the distance increases, the train positioning method based on the odometriche sensor often needs to arrange more physical balise along the railway to achieve the convergence of the distance measurement error. The train positioning method based on satellite navigation system and electronic map can ensure the real-time convergence of distance measurement error, and can greatly reduce the laying of physical balise. While improving the real-time accuracy of train positioning, it reduces the labor of maintenance personnel in harsh environments. However, in the train positioning application of satellite navigation system, since only a straight-line distance can be calculated between two points, there is a large error in calculating the train position at the curved track. To solve this problem, this paper proposes a train positioning method based on satellite navigation system and electronic map. This method can calculate the precise position of the train at the curved track based on the information of the curved track, thereby solving the precise positioning of the train at the curved track.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113861</guid>
    </item>
    <item>
      <title>Evaluation of Ice Warning Systems</title>
      <link>https://trid.trb.org/View/2680641</link>
      <description><![CDATA[Two ice warning systems were installed on the Interstate highway system in the Oklahoma City area. A Boschung Company, Inc. Ice Early Warning System was installed on three elevated structures or bridges. A Surface Systems Inc. SCAN System was installed on one of the structures. The systems were evaluated for two winter seasons. The primary objective of the evaluation was to determine the data accuracy and reliability and the durability of the equipment in the operational environment. The Boschung system was used by a field maintenance division to augment their weather surveillance methods and as an aid to gain knowledge on icing conditions. The relatively few icing events which occurred during the evaluation period limited the amount of data available for analysts and equipment evaluation. The basic sensor responses to weather and surface conditions appeared to be accurate and reliable. The logic derived early warning alarms or surface condition status alerts were not as easy to interpret or verify due to the lack of repeatable weather conditions. The systems demonstrated the potential to be of value as a decision support tool. Further operational experience and evaluation by the maintenance supervisors responsible for deicing operations would be beneficial in order to better assess the feasibility of future system expansion.]]></description>
      <pubDate>Tue, 07 Apr 2026 10:08:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680641</guid>
    </item>
    <item>
      <title>Feature Map Aided Robust High Precision GNSS Positioning in Harsh Urban Environments</title>
      <link>https://trid.trb.org/View/2610684</link>
      <description><![CDATA[In this contribution, we propose the GNSS Feature Map-aided robust extended Kalman filter, which can provide centimeter-to-decimeter-level GNSS RTK position accuracy in urban environments without the need of additional sensors, city model information or computational intensive ray tracing methods. In this approach, the information on the predicted observation error magnitudes from the generated GNSS Feature Map is combined with the concept of robust estimation. The RTK positioning performance comparison for a dynamic experiment under harsh signal propagation conditions reveals that GNSS Feature Map-aided weighting using the Geman-McClure loss function shows the best overall performance. The RMS of the horizontal position error is improved by 17% compared to C/N0 weighting, while 3DMA NLOS exclusion even degrades the solution. Furthermore, the combination of Feature Map information with the robust Geman-McClure loss function is effectively enhancing the float solution and reducing the number of falsely fixed ambiguities.]]></description>
      <pubDate>Fri, 27 Mar 2026 17:03:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610684</guid>
    </item>
    <item>
      <title>Real-time traffic incident data-based perimeter control threshold estimation method</title>
      <link>https://trid.trb.org/View/2643240</link>
      <description><![CDATA[Macroscopic fundamental diagram (MFD) is susceptible to traffic flow heterogeneity under incidents, which makes it hard to obtain an accurate estimation of the perimeter control threshold for effective traffic incident management. This study thus proposed a real-time MFD estimation method considering the impact of incidents. By incorporating newly defined variables, the road vulnerability index and incident parameters, an extended MFD model was developed to capture and quantify the impact of traffic incidents on traffic flow dynamics. Simulation experiments were conducted, and an estimation accuracy of more than 97% could be obtained for threshold estimation, with obvious superiority as compared to estimation methods without regard to the impact of incidents. The efficiency of using the proposed method for perimeter control was also validated, showing an improvement of 9% and 11.6% in average delay and number of stops respectively, as compared with perimeter control without considering the impact of traffic incidents.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643240</guid>
    </item>
    <item>
      <title>Problems in Assessing the Accuracy of Mass Comparators AVK1000: Analysis of the Impact of External Factors</title>
      <link>https://trid.trb.org/View/2665912</link>
      <description><![CDATA[The redefinition of the kilogram introduced new challenges for National Metrology Institutes (NMIs), particularly in ensuring accuracy and repeatability in mass transfer measurements. This study focuses on a comparative analysis of various mass comparators, with particular emphasis on vacuum conditions and the impact of vibrations on measurement stability. The relevance of this study to transport lies in improving vehicle weighing accuracy, which is essential for road safety and fuel efficiency. The research highlights the performance of the AVK1000 comparator compared to previous models, identifies discrepancies, and discusses modifications aimed at improving its functionality. The study also underscores the necessity of monitoring environmental factors to maintain high measurement reliability.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665912</guid>
    </item>
    <item>
      <title>Advanced 7/24 Taper Toolholder/Spindle Interfaces for High-Speed CNC Machine Tools</title>
      <link>https://trid.trb.org/View/1781397</link>
      <description><![CDATA[The paper formulates basic requirements to the tool-holder/spindle interface for different machining operations. A comparison of some alternative and commercially available designs is presented. Two advanced interface designs which are fully compatible with existing 7/24 taper spindles and toolholders while providing enhanced accuracy in axial and radial directions, higher stiffness, insensitivity to high rpm, and reliability at high rpm are described and results of their evaluation are presented.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:47:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1781397</guid>
    </item>
    <item>
      <title>Game-theoretic incentives for federated learning in traffic prediction: Balancing resource allocation and prediction accuracy via Stackelberg contracts</title>
      <link>https://trid.trb.org/View/2636290</link>
      <description><![CDATA[Federated learning-based traffic flow prediction has attracted growing interest in the field. Federated Learning (FL) provides a novel solution for privacy-preserving distributed training. However, designing a fair and efficient incentive mechanism to encourage collaboration among diverse participants remains a key challenge. This paper proposes an incentive mechanism for FL based on contract theory and the Stackelberg game. More specifically, our proposed method quantifies and differentiates rewards for participant contributions through contract design while using the Stackelberg game to balance resource allocation and profit competition between the server and participants. Additionally, this paper integrates an efficient local prediction model, WL-Transformer (Weighted Layer Transformer), to enhance participants’ local data modeling capabilities, thereby improving the accuracy and adaptability of the global model in traffic flow prediction tasks. Finally, experiments on the License Plate Recognition (LPR) dataset from Changsha, China, demonstrate the effectiveness of the proposed incentive mechanism in achieving high-accuracy traffic flow prediction.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636290</guid>
    </item>
    <item>
      <title>GPT4-Vision Multimodal Model-Powered Query-Answering Chatbot for Bridge PDF Drawings</title>
      <link>https://trid.trb.org/View/2640519</link>
      <description><![CDATA[Managing multiple sets of bridge drawings presents a significant challenge for the state Departments of Transportation (DOTs). For example, state DOTs need to encode each bridge in their jurisdiction according to the new Federal Specifications for the National Bridge Inventory (SNBI). Finding information within these drawings typically involves manual inspection or utilizing search functions within the documents, which is labor-intensive and time-consuming. Introducing a query-answering chatbot as a virtual assistant could significantly help with this information retrieval process. In pursuit of that, in this paper, a Python-based chatbot was created by integrating with the advanced multimodal model, GPT4-Vision, in the proposed system architecture. This program underwent testing using five sets of bridge drawings, comparing its responses against manually retrieved information to assess accuracy. The outcome indicates that the chatbot delivers swift responses with an accuracy of around 58% with information from not only textual data but also graphics.]]></description>
      <pubDate>Fri, 27 Feb 2026 11:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640519</guid>
    </item>
    <item>
      <title>Dynamic Process Noise Covariance Adjustment in GNSS/INS Integrated Navigation Using GRU-SAC for Enhanced Positioning Accuracy</title>
      <link>https://trid.trb.org/View/2561806</link>
      <description><![CDATA[The Kalman filter is widely used in Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated navigation systems to fuse information, resulting in high precision and robust positioning performance. In the Kalman filter, the accuracy of the process noise covariance matrix directly affects the precision of the positioning results. We propose a Soft Actor-Critic (SAC) algorithm based on Gated Recurrent Unit neural networks (GRU-SAC) to dynamically adjust the process noise covariance matrix online using sequential observation data to improve positioning accuracy. We model the decision-making process as a Partially Observable Markov Decision Process (POMDP) and incorporate multiple information sources as the system state. The GRU network is used to extract temporal features from the navigation data and to address memory consumption issues commonly associated with POMDPs. And the SAC algorithm continuously adjusts the process noise covariance based on observations from the Kalman filter, allowing the algorithm to perform better in complex, dynamic, and changing navigation environments. Additionally, we provide detailed design and deployment strategies for both loosely-coupled and tightly-coupled systems. Extensive experiments have been conducted to validate the effectiveness of our method. The results show that our approach generalizes well across a wide range of preset process noise covariance matrices and performs excellently in suppressing error drift during GNSS outages.]]></description>
      <pubDate>Fri, 27 Feb 2026 11:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561806</guid>
    </item>
    <item>
      <title>Co-training framework for enhancing survey accuracy while reducing respondent burden in travel data collection</title>
      <link>https://trid.trb.org/View/2627576</link>
      <description><![CDATA[A major bottleneck in travel behavior analysis is the need for a substantial amount of labeled data, which typically places a burden on survey respondents for collecting travel behavior data. Our study addresses this issue by leveraging semi-supervised learning, specifically utilizing the co-training algorithm, which effectively incorporates both labeled (active) and unlabeled (passive) data. We extend the semi-supervised learning concept to be a part of the survey scheme that involves both data collection process and enrichment process of travel attributes. Our experiments, focusing on travel mode identification using GPS data from Hiroshima, Japan, demonstrate that our proposed method outperforms existing conventional supervised learning methods such as neural networks, KNN, and SVM, particularly when incorporating an increased proportion of unlabeled data. This strategic use of unlabeled data achieves two apparently conflicting goals: (1) reduces the reliance on extensive manual labeling, thereby alleviating respondent burdens, and (2) increases the accuracy of the prediction. The results of our experiments also reveal that the delicate balance between labeled and unlabeled data proportions plays a pivotal role in co-training performance. Beyond serving as a mode identification tool, our findings underscore the transformative potential of co-training as a valuable data filtering method: By optimizing the interplay between labeled and unlabeled data, co-training efficiently filters noise and refines the dataset. This contributes to enhanced survey accuracy while minimizing labeling burdens. Our results provide useful information to design an adaptive scheme that dynamically tailors the information solicited from respondents to optimize the balance between data quality and respondent burden.]]></description>
      <pubDate>Thu, 26 Feb 2026 14:51:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627576</guid>
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