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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>A Drone-Integrated Safety Framework for Sustainable Rail Infrastructure Management and Accident Prevention</title>
      <link>https://trid.trb.org/View/2694409</link>
      <description><![CDATA[The rail accident statistical data highlights the core reasons related to structural safety are ageing, high-density network, infrastructure defect, environmental hazard and human error. This study proposes a socio-technical architecture pivoting around UAVs within an intelligent transport ecosystem. A three-pillar framework is introduced, consisting of Monitoring, Analysis and Decision-Making, and Response and Mitigation. Data from multiple sources and different sensor types are utilized in machine-actionable safety intervention. Scenario-based assessments demonstrate the framework's impact. The proposed approach offers quantifiable benefits, such as reconnaissance flights adjusting early warning thresholds according to operational context, optimizing field deployment and resource allocation. This guide intends to achieve two primary objectives: firstly, to meaningfully reduce the risk of accidents; and secondly, to support sustainable mobility goals. Additionally, the framework is intended to align with evolving aviation and data governance standards.]]></description>
      <pubDate>Wed, 13 May 2026 17:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694409</guid>
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    <item>
      <title>Cause Analysis and Evolution Characteristics of Different Types of Railway Accidents Based on System Dynamics Theory</title>
      <link>https://trid.trb.org/View/2665155</link>
      <description><![CDATA[Railway accidents occur frequently worldwide and pose serious risks. On the basis of 547 railway accidents that occurred in the USA, Germany, Japan, and the UK between 2013 and 2023, the key causes and accident evolution characteristics of the railway operation accidents are analyzed using the System Dynamics method. First, the high-frequency accident chains and high-hazard accident chains for the different accidents are determined, and the causal loop diagram and stock–flow diagram of different accidents are established by the system dynamics method. Railway conflict, derailment, and train collision accidents are associated with “human–machine coupling failures”, whereas fire and explosion accidents are related to the operational failures of railway equipment and facilities. Moreover, on the basis of the constructed stock–flow diagram, the evolution of railway accidents can be divided into latent, acceleration, critical and outbreak periods, and each period has unique dominant effects. Finally, a “false safety period” term is defined to reflect the impact of the delayed implementation of control measures on the accumulation of system risks and the evolution of different accidents. The evolution of railway conflict accidents is least affected by the delay effect, whereas that of derailment accidents is most strongly affected.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665155</guid>
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    <item>
      <title>Railroad Investigation Report: R.J. Corman Railway Group Conductor Injury, Guthrie, Kentucky, June 26, 2025</title>
      <link>https://trid.trb.org/View/2694404</link>
      <description><![CDATA[On June 26, 2025, about 5:00 p.m. local time, an R.J. Corman Railway Group conductor was seriously injured during a railcar switching operation at Guthrie Yard in Guthrie, Kentucky. The conductor was working with an engineer to separate a damaged railcar from a block of 13 railcars and move it onto a siding track east of the main track. During the switching operation, the conductor suffered a serious injury to his foot when he placed it between equipment to push the knuckle of a coupler into alignment. The National Transportation Safety Board (NTSB) determined that the probable cause of the June 26, 2025, R.J. Corman employee injury at Guthrie Yard was the conductor circumventing established safety rules during coupling operations, resulting in the employee’s foot being crushed between two railcars. Contributing to the incident was the extreme heat, which influenced the conductor’s decision to expedite the work.]]></description>
      <pubDate>Thu, 30 Apr 2026 09:06:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694404</guid>
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    <item>
      <title>Effective Railway Infrastructure Planning Using GIS for Data Mapping and Analysis: Case Study Level Crossings in Zagreb, Croatia</title>
      <link>https://trid.trb.org/View/2579519</link>
      <description><![CDATA[Accidents at level crossings account for a significant percentage of railway accidents worldwide. The level crossing is the point where two modes of transport meet, namely road and rail, which means that it poses a safety problem for both modes of transport. Even though more attention is paid to passive level crossings, serious accidents and collisions with half-barriers or barriers still occur at active level crossings. This study aims to determine what factors and data are required for effective infrastructure monitoring and planning for active and passive level crossings. QGIS (Quantum Geographic Information System) is used to collect, map and analyse the data. Data on traffic accidents at level crossings was gathered for each level crossing within the study area. The study area was limited to the city of Zagreb, Croatia. The result of the study is a set of parameters for GIS-based data collecting that can be easily implemented in a large-scale railway network. It can be used in various areas, such as determining accident black spots, infrastructure maintenance or planning safety improvements and new railway infrastructure. Future work will focus on the expansion of the network and the development of preventive measures to reduce traffic accidents at level crossings.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579519</guid>
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    <item>
      <title>Assessing the Impact of Human Error Factors on Railway Accident Severity: Evidence from Accident Investigation Reports in Korea</title>
      <link>https://trid.trb.org/View/2691610</link>
      <description><![CDATA[This study investigates the role of human error factors in shaping the severity of railway accidents. Using a structured coding scheme to transform qualitative accident investigation reports into quantitative variables, the analysis reveals that deficiencies in managerial oversight, shortcomings in maintenance practices, and failures in equipment and system reliability are consistently associated with higher accident costs. These findings underscore the organizational and technical dimensions of human error as critical factors linked to accident severity, rather than merely front line worker mistakes. At the modeling level, the explanatory variables collectively account for a meaningful portion of the variation in accident costs, indicating that such outcomes are not purely random but systematically related to underlying human and organizational conditions. By identifying the relative importance of managerial, maintenance, and equipment-related deficiencies, this study provides empirical evidence for prioritizing these factors in railway safety management. The results highlight the need to strengthen organizational accountability, improve maintenance regimes, and ensure the reliability of technical systems as essential strategies for mitigating high-consequence accidents. These insights contribute to a deeper understanding of the organizational and systemic correlates of accident severity and can inform targeted interventions aimed at enhancing overall railway safety.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691610</guid>
    </item>
    <item>
      <title>Analysis on Operation Mechanism of Emergency Rescue for Rail Passenger Train Traffic Accidents</title>
      <link>https://trid.trb.org/View/2113847</link>
      <description><![CDATA[The high-speed railway has a large passenger flow, so it is necessary to ensure the public travel safety and improve the effectiveness of emergency rescue methods. The feasible solutions to improve the emergency rescue effect of rail passenger train traffic accidents are as follows: 1. Establish the government led emergency rescue coordination mechanism, improve the high-speed railway emergency rescue system, mainly establish and improve the authoritative and efficient flexible emergency rescue regional linkage system. 2. Improve the accident emergency response mechanism, build the emergency rescue force system centered on the fire department, including the accident emergency response mechanism, formulate the emergency rescue plan, and quickly mobilize the rescue force system. 3. Improve the application mechanism of the rescue team, and realize the coordinated and unified rescue force, including the construction of professional rescue team and the volunteer rescue force, with the fire force as the main force and the volunteer rescue force as the auxiliary force. 4. Improve the high-speed railway emergency rescue dispatching command system, improve the high-speed railway emergency rescue dispatching command information construction system, establish the emergency command system, including digital information network system, emergency rescue early warning platform, emergency communication guarantee system.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113847</guid>
    </item>
    <item>
      <title>Data-Driven Risk Analysis and Management Framework for Rail Hazmat Transportation in Canada: Machine Learning Approach</title>
      <link>https://trid.trb.org/View/2652885</link>
      <description><![CDATA[Rail transportation of hazardous materials (hazmat) is essential to Canada’s economy but carries significant safety and environmental risks. This study develops a data-driven predictive risk assessment framework for hazmat release following railway accidents, integrating multi-year incident records with operational, environmental, and geographic variables from multiple public sources. Supervised machine learning models—logistic regression, decision trees, and neural networks—are applied to classify hazmat release outcomes following rail incidents. Key predictors include track type, hazmat class, weather conditions, train configuration, and operational density. The best-performing models demonstrated competitive predictive performance, with metrics such as AUC-ROC, F1-score, and balanced accuracy indicating consistent behavior despite substantial class imbalance, while also offering interpretable insights into key risk factors. We also developed the Rail HAZMAT Release Predictor, a web-based tool that applies our models to assess hazmat release risks in rail incidents. Findings inform targeted mitigation strategies aligned with Public Safety Canada’s emergency management framework, including limiting hazmat car counts, implementing predictive maintenance, and tailoring emergency protocols to regional risk profiles. By combining multi-source data integration with advanced modeling, this research advances proactive, evidence-based decision-making for safer hazmat rail operations.]]></description>
      <pubDate>Mon, 06 Apr 2026 08:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652885</guid>
    </item>
    <item>
      <title>Hybrid Ensemble Learning Model Combining BERT and CNN for Predicting Urban Rail Transit Accident Consequences</title>
      <link>https://trid.trb.org/View/2591337</link>
      <description><![CDATA[Urban Rail Transit (URT) accidents not only seriously affect the safety and reliability of its operations, but also reduce service level to passengers. Based on historical URT accident data, this study develops a hybrid ensemble learning model based on a Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) for predicting accident consequences in URT. The CNN is employed to capture spatial patterns from the diverse accident data, while the BERT is applied to learn complex relations in accident text descriptions. The results of the two models are combined for classifying accident consequences. The proposed hybrid ensemble learning model was applied to predict accident consequences in Chongqing’s URT using historical accident records. It achieved a prediction accuracy of 0.805 on testing data set, which is at least 20% higher than that of commonly used machine learning models, including multilayer perceptrons, support vector machines, and Bayesian networks. Furthermore, the reapplication of the proposed model to historical accident records of the URT in Chengdu demonstrates the generalizability and reusability of the model. This study forecasts the consequences of URT accidents with high accuracy using limited historical data, which supports operators in identifying high-frequency and high-impact accidents. Consequently, targeted maintenance and timely emergency response strategies can be developed to decrease accident rates and mitigate the impacts.]]></description>
      <pubDate>Tue, 31 Mar 2026 16:34:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591337</guid>
    </item>
    <item>
      <title>Operation Environmental Risk Warning of Beijing-Zhangjiakou High-Speed Railway Based on Bayesian Networks</title>
      <link>https://trid.trb.org/View/2113830</link>
      <description><![CDATA[The Beijing-Zhangjiakou high-speed railway is an important transportation facility for the 2022 Beijing Winter Olympics. The Winter Olympics is about to be held, and there is an urgent need for the management, control and early warning of operation environmental risks in the Beijing-Zhangjiakou high-speed railway. This paper sorts out the causal relationship between hazards, hidden dangers and accidents in the Beijing-Zhangjiakou high-speed railway operation environment, and builds a Bayesian network model to calculate the probability of safety accidents. Also, this paper used the probabilistic safety evaluation method to calculate the risk coefficients in the corresponding scenarios, and achieved graded early warning of risks in the operating environment of the Beijing-Zhangjiakou high-speed railway.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:15:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113830</guid>
    </item>
    <item>
      <title>RecoMap – a semi-automated tool for analysing railway accident recommendations across jurisdictions and over time</title>
      <link>https://trid.trb.org/View/2669895</link>
      <description><![CDATA[Despite many (sometimes similar) recommendations made by independent railway accident investigators across jurisdictions, practitioners continue to suffer from a lack of synthesised recommendations due to the high complexity of analysing textual data. To fill the gap, a semi-automated tool for analysing accident report recommendations, RecoMap, is developed as a framework to help practitioners learn from previous experience. Empirical data is retrieved from official railway accident reports published by Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB). By comparing experiences across countries, this study also identifies a transition from making interfering recommendations addressing operational issues to making supportive recommendations addressing organisational issues in the railway industry. Findings imply that current practices might continue to result in railway accidents that could have been prevented by learning from other jurisdictions and implementing corresponding mitigation measures in advance.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669895</guid>
    </item>
    <item>
      <title>Analysis and comparison of train accidents in Japan and the United States</title>
      <link>https://trid.trb.org/View/2657979</link>
      <description><![CDATA[Japan and the United States have significant railroad systems with distinct characteristics. Examining how these differences are related to train accidents can guide the adaptation of safety improvements from one system to the other. This study conducts statistical and causal analyses of train accident data from both countries for 2001–2023. The results clarify how accident characteristics correspond to differences in infrastructure, equipment, and operational features. Similarities and differences in train accident characteristics between the railroad systems of the two countries were compared, and the adaptation of successful methods, technologies, and practices from one country to improve rail safety in the other was discussed. The findings of the study will benefit the railroad systems of both countries by encouraging cross-country research, industry collaboration, and technology transfer. The quantitative risk assessment framework developed in this study can be applied to other railway safety and risk benchmarking efforts.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657979</guid>
    </item>
    <item>
      <title>A study of the collision derailment behaviour of trains based on rail vehicle collision dynamics</title>
      <link>https://trid.trb.org/View/2643042</link>
      <description><![CDATA[The derailment behaviour of trains following a collision is a critical factor in driving safety. This paper builds upon the existing train collision dynamics model by integrating a finite-length Euler beam track model with elastic point support and a wheel-rail interaction solver. It employs a modified explicit double-step method for computation. The enhanced model and algorithm are utilized to examine train derailment post-collision. A specialized program for collision calculations is developed, with its accuracy and stability confirmed through comparison with finite element analyses. In determining train derailment, the dynamic model adopts a criterion from finite element simulations. It sets a threshold where the lifting height of at least two wheelsets on the same vehicle must not exceed 50% of the nominal flange height, and these wheelsets must not be on the same bogie. The model further investigates the impact of four factors on derailment behaviour in a collision: initial vertical height difference, lateral displacement, initial pitch angle, and initial yaw angle, across various speeds. This research offers significant insights into enhancing the safety measures against train collision derailments.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:01:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643042</guid>
    </item>
    <item>
      <title>Crash occurrence and severity at railway level crossings in Bangladesh</title>
      <link>https://trid.trb.org/View/2667074</link>
      <description><![CDATA[Although train accidents are less frequent than road accidents, their consequences are often detrimental, especially at Railway Level Crossings (RLCs), the intersection points between rails and roads. RLC safety in developing countries like Bangladesh has received limited research attention despite the unique characteristics of these crossing types. This study aims to identify factors of occurrence and severity at RLCs in Bangladesh Railway East Zone from 2014 to 2021, during which crashes injured 89 and killed 54. An analysis of 121 RLCs across 18 districts examined how crossing, roadway, environmental, and exposure factors and crash reasons affect crash likelihood and severity. An extensive survey was conducted to build a structured database by collecting RLC-specific information. After accounting random effects, no significant heterogeneity was detected in either the occurrence or severity models. Logistic modeling revealed that train speed, C-type and illegal RLCs, warning sign-to-barrier distance, poor gate lodge visibility, track-to-lodge distance, divisional community areas, heavy vehicle percentage, geometric orientation, and the Sholoshohor-Fatehbad segment increased crash likelihood. In contrast, A-type RLCs, crossing width/length ratios, four-half barriers, speed bumps, crossing angles (<45°), residential areas, and standard straight sections decreased occurrence. Additionally, the ordered logit model demonstrated that train speed, truck involvement, traffic-controlled RLCs, warning sign distance (<28 feet), off-peak timings, train-hitting-vehicle collisions, gatemen operational failures, law violations, and reckless traversing significantly impacted severity. Policy priorities include standardizing warning sign placement (<28 feet), improving lodge visibility with paint and clear sightlines, installing signs at T-intersections, and utilizing four-half bamboo gates for safer RLC design.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667074</guid>
    </item>
    <item>
      <title>WildlifeRailGuard: A novel conservation technology to mitigate train-animal collisions in forest regions</title>
      <link>https://trid.trb.org/View/2640786</link>
      <description><![CDATA[The escalating incidences of train-wildlife collisions, especially with elephants, are a real blow to forest-based wildlife conservation efforts. To enable real-time detection of wild animals such as elephants and promptly alert train operators, this study utilizes advanced deep learning algorithms integrated with camera systems installed at strategic locations along the railway tracks. The research proposes the use of WildlifeRailGuard as an innovative solution to address this issue effectively. To enable real-time detection of wild animals, such as elephants, and promptly alert train operators, this study utilizes advanced deep learning algorithms integrated with camera systems installed at strategic locations along railway tracks. Upon receiving alerts, train operators can immediately reduce speed, ensuring the safety of both passengers and wildlife. The proposed WildlifeRailGuard system also contributes to wildlife conservation by leveraging data analytics to generate valuable insights into animal behavior and movement patterns. The use of data analytics tools helps mitigate the negative effects of railway expansion on various animal species, fostering hope for achieving a sustainable balance between railway development and forest conservation.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640786</guid>
    </item>
    <item>
      <title>Method for Evaluating Crashworthiness of Railway Vehicles Based on Correlation with Injury Severity of Passengers Occupying Longitudinal Seats</title>
      <link>https://trid.trb.org/View/2635987</link>
      <description><![CDATA[Improving safety for train passengers in the event of a collision is a priority. To this end, this paper proposes safety indexes. The severity of injury for these indexes was estimated for a level crossing accident using numerical simulation. We compared the correlation between the severity of injury of a human model and the safety index of vehicles, which is the integral of deceleration waveforms, mean deceleration waveforms and maximum deceleration waveforms. It was found that the integral of deceleration values had the highest correlation with the injury values. We therefore propose using the integral of deceleration as a method for evaluating crashworthiness design.]]></description>
      <pubDate>Wed, 25 Feb 2026 16:28:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635987</guid>
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