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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>
    <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>Fire Management/Suppression Systems/Concepts Relating to Aircraft Cabin Fire Safety</title>
      <link>https://trid.trb.org/View/2730837</link>
      <description><![CDATA[The purpose of this study was to provide the Federal Aviation Administration (FAA) with a comprehensive review of the applicability of fire protection (management/suppression) system (or concepts) to aircraft cabin fire safety. Both inflight fires and post crash fires were considered by the study. Included in the study were establishment and documentation of the feasibility of each system/concept, determination of costs and benefits for systems judged feasible, and development of test programs to evaluate systems for unknown (undocumented) feasibility. The study included a literature search to document the course and consequences of past accidents, and the degree to which various fire protection concepts had been developed. Fire scenarios were developed from accident histories and engineering analysis, and used to assist in judging the potential of the various systems/concepts examined. The study encompassed fire prevention, detection, confinement, and suppression; handling of combustion products; and escape aids.]]></description>
      <pubDate>Sun, 02 Aug 2026 17:23:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730837</guid>
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    <item>
      <title>Heat Release Rate of Objects Burning in Cargo Compartments</title>
      <link>https://trid.trb.org/View/2693733</link>
      <description><![CDATA[The heat release rate of objects burning in a relatively large, simply ventilated cargo compartment is reconstructed from the oxygen consumption history of the exiting gas stream, assuming perfect mixing of the combustion gases in the compartment. The model was calibrated using a premixed propane gas burner to generate a variety of well-defined heating histories. Qualitative agreement between actual and computed heat release rate histories is obtained when the duration of the burning is on the order of 1/2 of the mixing time of the compartment. This research supports efforts by the Federal Aviation Administration to develop new certification requirements for aircraft cargo compartment fire detectors.]]></description>
      <pubDate>Sun, 26 Apr 2026 17:38:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693733</guid>
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    <item>
      <title>A review of ship fire detection technologies in maritime transportation</title>
      <link>https://trid.trb.org/View/2641258</link>
      <description><![CDATA[Ship fires pose a persistent threat to maritime safety owing to severe consequences, necessitating continuous innovation in detection technologies. This study systematically evaluates the current research landscape in ship fire detection. It analyzes the technological evolution from traditional sensors and image processing to intelligent methods, particularly deep learning(DL) – notably You Only Look Once (YOLO) -series algorithms – and multi-modal fusion, assessing the performance, advantages, and limitations of each. Analysis reveals DL as the predominant research direction, significantly enhancing detection in complex scenarios via model optimizations like lightweight design and attention mechanisms. Furthermore, fusing multi-modal data (e.g., visual, infrared, gas) is recognized for enhancing system robustness. However, critical bottlenecks remain: the scarcity of high-quality, diverse public datasets; poor model generalization in complex maritime environments; and pronounced efficiency-accuracy trade-offs, particularly for edge computing. Future directions include constructing comprehensive datasets, developing models with improved environmental adaptability, and exploring novel paradigms such as multi-agent collaborative detection and early fire signature analysis, aiming for more efficient and reliable ship fire detection and warning.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:41:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2641258</guid>
    </item>
    <item>
      <title>Effects of the Layout of Exhaust Port on the Performance of Smoke Control for Ultra-Wide Immersed Tube Tunnel</title>
      <link>https://trid.trb.org/View/2408067</link>
      <description><![CDATA[Smoke control is a crucial issue in tunnel fire prevention and control, especially in the ultra-wide tunnels, which is arousing more and more attentions. In this paper, the performance of three different exhaust port setting methods on the fire smoke control in the four-lane tunnel of immersed tube is compared and analyzed based on CFD numerical simulation. In the super wide immersed tunnel, increasing the number of smoke outlets on the transverse side of the tunnel can effectively reduce the thickness of the smoke layer and the maximum temperature of the vault under the top centralized smoke exhaust strategy. The efficiency of smoke exhaust of the outlet along the tunnel obviously decreased with its distance far away from the fire. In the design of smoke prevention and exhaust for super wide section immersed tube tunnel, the authors can consider reducing the gap between the exhaust fume and increasing the number of horizontal exhaust fume to improve the effect of smoke control in tunnel.]]></description>
      <pubDate>Mon, 18 Aug 2025 08:51:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2408067</guid>
    </item>
    <item>
      <title>Advanced Early Fire Detection in Aircraft Cargo with MACD And Passive UHF RFID Temperature Sensing While Maintaining False Alarm Resistance</title>
      <link>https://trid.trb.org/View/2550934</link>
      <description><![CDATA[This study introduces a novel approach to enhance fire protection in aircraft cargo compartments, motivated by the urgency to address catastrophic in-flight fires recorded between 2006 and 2011. The method uses ultra-high frequency (UHF) radio frequency identification (RFID) temperature sensing tags and advanced algorithmic analysis to enhance fire detection capabilities within unit load devices (ULDs). This approach significantly reduces detection times while minimizing false alarms. The first objective was to create an economical, battery-free fire detection system with UHF RFID temperature sensing tags installed within ULDs. This positions the temperature sensing tags closer to potential fire sources than traditional cargo compartment ceiling-mounted smoke detectors. Wireless temperature sensing tags allow the ULDs to move in and out of aircraft. Passive sensors address the challenges of battery-powered systems, such as battery changes and thermal runaway risks. The second objective sought to enhance the RFID-based system with near real-time temperature monitoring capabilities within ULDs. The system provides accurate temperature trend analysis by incorporating a moving average convergence divergence (MACD) algorithm adapted from financial markets. This significant advancement improves fire detection times and supports communication of conditions within ULDs to flight crews, enabling quicker response actions.]]></description>
      <pubDate>Thu, 05 Jun 2025 11:59:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550934</guid>
    </item>
    <item>
      <title>A Comprehensive Guideline for GDOT Bridges Fire Hazard Assessment</title>
      <link>https://trid.trb.org/View/2508941</link>
      <description><![CDATA[The primary goal of this research is to develop a comprehensive guideline for the Georgia Department of Transportation (GDOT) to assess fire hazards in bridge structures.]]></description>
      <pubDate>Tue, 11 Feb 2025 16:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2508941</guid>
    </item>
    <item>
      <title>Experimental investigation on effect of fire source location on compartment fire phenomena with a single natural ceiling vent</title>
      <link>https://trid.trb.org/View/2434002</link>
      <description><![CDATA[The effect of fire source location (FSL) on compartment fire phenomena with a single natural ceiling vent was experimentally investigated under various vent area (VA) and heat release rate (HRR) conditions. Two FSL cases, where the fire source was located at the centre (FSLC) and on the side (FSLS) of the floor, were tested. In the bidirectional vent flow patterns of FSLC and FSLS, the detailed locations where the smoke outflow and the fresh air inflow occurred were different. Empirical correlations were proposed for predicting the smoke outflow areas. FSLC exhibited a higher central velocity of the outflow, larger estimated mass flow rate of the outflow, and lower temperatures inside the compartment than FSLS. The estimated mass flow rate of the outflow depended more on VA than on HRR. A previous correlation was more suitable for FSLS than FSLC in predicting the mass flow rate of the outflow.]]></description>
      <pubDate>Tue, 22 Oct 2024 09:07:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2434002</guid>
    </item>
    <item>
      <title>Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2307058</link>
      <description><![CDATA[Rapid developments in deep learning (DL) and the Internet-of-Things (IoT) have enabled vision-based systems to efficiently detect fires at their early stage and avoid massive disasters. Implementing such IoT-driven fire detection systems can significantly reduce the corresponding ecological, social, and economic destruction; they can also provide smart monitoring for intelligent transportation systems (ITSs). However, deploying these systems requires lightweight and cost-effective convolutional neural networks (CNNs) for real-time processing on artificial intelligence (AI)-assisted edge devices. Therefore, in this paper, the authors propose an efficient and lightweight CNN architecture for early fire detection and segmentation, focusing on IoT-enabled ITS environments. They effectively utilize depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy with an optimal number of convolution kernels per layer, significantly reducing the model size and computation costs. Extensive experiments on their newly developed and other benchmark fire segmentation datasets reveal the effectiveness and robustness of their approach against state-of-the-art fire segmentation methods. Further, the proposed method maintains a balanced trade-off between the model efficiency and accuracy, making their system more suitable for IoT-driven fire disaster management in ITSs.]]></description>
      <pubDate>Mon, 11 Mar 2024 09:10:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2307058</guid>
    </item>
    <item>
      <title>FlameNet: a lightweight convolutional neural network for flame detection and localisation</title>
      <link>https://trid.trb.org/View/2185976</link>
      <description><![CDATA[Accurately and efficiently detecting and localising the flame is critical for preventing fire disasters. However, most high-performance deep models require high hardware resources, making them hard to deploy on edge or mobile intelligent devices. This paper proposes a lightweight deep convolutional neural network, called FlameNet, for flame detection and localisation. The proposed FlameNet is derived from YOLOv4 with the following modifications: First, MobileNetV2 replaces the CSPDarknet53 as the new backbone network of YOLOv4; Second, the Coordinate Attention module is added to the inverted residual linear bottleneck of MobileNetV2; Third, the Depthwise Separable Convolutions is used in PANet of YOLOv4. There is no large-scale public dataset available, and the authors produced a real-world flame (RWF) dataset containing 13,129 images. Qualitative and quantitative analysis and comparisons of experimental results demonstrate that the accuracy and efficiency of the proposed FlameNet for flame detection out-perform the original YOLOv4 and the other relative models.]]></description>
      <pubDate>Mon, 20 Nov 2023 09:12:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2185976</guid>
    </item>
    <item>
      <title>Operational System Modelling in a Focused Fire Alarm System with an Open and Signal Detection Circuit Supervising Railway Station Premises</title>
      <link>https://trid.trb.org/View/1972696</link>
      <description><![CDATA[Transport facilities commonly operate safety systems, fire alarm systems (FAS) in particular. According to a Regulation of the Minister of Interior and Administration of 7/6/2010 (Dz.U. No. 109, item 719), the use of FAS in Poland is required at railway stations and ports, designed for the presence of more than 500 people. Currently, designers and supervisors of operation of FAS located in railway areas do not include reliability indices, as no such requirements are present in the applicable standards and acts. The authors recommend taking selected safety indices into account already at the system design and operation stages, based on the methodology using Markov chains continuous over time and on the use of computer simulation in the Reliasoft Blocksim/Reno software. The example and calculations presented in the paper prove that this is possible. The developed FAS analysis method using the Markov process enables decision-making already at the design stage.]]></description>
      <pubDate>Thu, 16 Nov 2023 14:45:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1972696</guid>
    </item>
    <item>
      <title>A multi-agency coordination resource allocation and routing decision-making problem: A coordinated truck-and-drone DSS for improved wildfire detection coverage</title>
      <link>https://trid.trb.org/View/2256470</link>
      <description><![CDATA[This study proposes a novel coordinated truck-drone system as a decision support system to improve fire suppression operations. The problem in this study is formulated as a bi-objective mathematical model to minimize total monitoring cost and time, considering trucks as mobile drone depots and that drones can fly at various altitudes and access hard-to-reach areas. In addition, time and cost parameters in the mathematical model are deemed uncertain, rendering the model more realistic. Accelerated Benders' decomposition (ABD) is utilized to solve the model rapidly. Furthermore, a column-and-constraint generation (CCG) algorithm is employed, which is an effective method for solving scenario-based models under uncertainty. Two criteria are subsequently used to evaluate and compare proposed robust optimization approaches (risk averse and min-max relative regret). The results indicate that the proposed system can assist firefighters in determining the optimal number of drones and trucks to patrol and the time and cost required to visit all areas. Moreover, the findings demonstrate that the min-max relative regret approach can outperform other methods during the hot season when the risk of wildfire is elevated. In contrast, when the fire risk is lower during the cold season, time and cost can be effectively managed, and a risk-averse strategy can be implemented. Finally, the proposed solution framework can facilitate optimal strategic organizational decision planning.]]></description>
      <pubDate>Wed, 15 Nov 2023 09:19:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2256470</guid>
    </item>
    <item>
      <title>A proposed design for 5-G fire detecting and extinguishing system: ultra-large container ship case study</title>
      <link>https://trid.trb.org/View/2250770</link>
      <description><![CDATA[The containerized cargo volume reaches 168.2 million TEU. There have recently been a staggering number of fire catastrophes onboard container ships. Investigations have identified that a rapidly developing fire is one of the main challenges in container fires. On the other hand, firefighting regulations had disregarded container vessel capacity increase. The resulting financial losses and environmental catastrophes paid astronomical bills. This research elaborates on an innovative solution utilizing a 5 G wireless self-detecting and extinguishing fire-control system. The system was applied in a case study of 19,870 TEU ultra large-container ships; The results demonstrate the number and locations of the system components. The system can reduce human interference with the fires onboard. The feasibility analysis output shows a low initial cost compared to the losses of such incidents. Moreover, the time of radio spectrum signals of the proposed system is high speed; remarkably, the internal container monitoring needs around 30 seconds to detect heat and another few seconds to activate the CO₂ and smother the suspicious container in its early intuition with allocation bothered alarm. The firefighting improvement technology could be a promising solution for monitoring and controlling dangerous cargo containers fire.]]></description>
      <pubDate>Wed, 18 Oct 2023 09:26:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2250770</guid>
    </item>
    <item>
      <title>Application Perspective of Digital Neural Networks in the Context of Marine Technologies</title>
      <link>https://trid.trb.org/View/2150908</link>
      <description><![CDATA[This study is focused on the issue of digital neural networks’ implementation in the context of maritime industry. Various algorithms of such networks in the terms of the marine technologies have been reviewed in the current study in order to evaluate the effectiveness of the methodology and to propose a new concept of an artificial neural network’s application in this way. Fire-detection system simulation based on the thermal imagers’ data input had been developed to assess the efficiency of the concept suggested with a multi-layer perceptron (MLP) algorithm integrated into the designed 3d-model.]]></description>
      <pubDate>Tue, 25 Apr 2023 09:49:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2150908</guid>
    </item>
    <item>
      <title>Intelligent Wireless Power Transfer (IWPT) for Safe Electric Power Charging of Rolling Stock</title>
      <link>https://trid.trb.org/View/2147083</link>
      <description><![CDATA[To improve the safety of the electric power charging system for rolling stock, including both propulsion and ancillary use of stored electric power, this project proposes the use of Intelligent Wireless Power Transfer (IWPT) technology for power charging. The proposed IWPT technology removes humans from the process of locomotive battery charging, thus potentially reducing the chance of electrical shock to zero and improving the safety of the railroad environment from electrical fire. The IWPT system can assess rolling stock in motion and detect faulting and fire issues within the power circuits to significantly enhance rolling stock fire and electric safety.]]></description>
      <pubDate>Tue, 11 Apr 2023 17:04:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2147083</guid>
    </item>
    <item>
      <title>A Data-Driven Danger Zone Estimation Method Based on Bayesian Inference for Utility Tunnel Fires and Experimental Verification</title>
      <link>https://trid.trb.org/View/2083689</link>
      <description><![CDATA[The ongoing challenge is to find an effective and precise fire detection method for safety concerns of utility tunnels to predict the fire danger zone and take measures for firefighting and intervention promptly. A data-driven danger zone estimation method was established based on Bayesian inference. The probability distribution of the fire danger zone can be obtained by this method. In particular, the governing equation is a simplified physical model, in which only crucial parameters of the fire states are referred to, including the fire source location, the maximum temperature, and the attenuation coefficient. This task shows superiority because it can avoid additional workload to provide the forward database for Bayesian inference. A prototype experiment was conducted in the largest utility tunnel experimental platform in China to verify the validity of the proposed method. Results demonstrated that the fire danger zone could be estimated with high accuracy merely based on several sensor data, which are not limited to specific scenes. The temperature distribution of the whole tunnel could also be predicted according to the fire parameters. Moreover, analysis of the measurement noise and disturbance conditions shows the robustness of the proposed method. Low costs, low consumption, and universality make it have broad engineering application prospects.]]></description>
      <pubDate>Tue, 24 Jan 2023 09:28:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2083689</guid>
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