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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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      <title>Revisiting international airports security checkpoints through a disability perspective</title>
      <link>https://trid.trb.org/View/2689984</link>
      <description><![CDATA[Airport security checkpoints present many challenges for the inclusion of diverse passengers, including people with disabilities. Due to the complexity of this context, understanding the challenges and solutions for inclusion must be grounded in the real-life experiences of passengers. Therefore, this research aims to explore the experiences of passengers with disabilities, as well as neurodiverse individuals and seniors, when going through the security screening process at international airports. A qualitative multiple-case study was conducted at three international airports. In situ walk-along interviews were conducted with 70 participants. Data was analyzed using a deductive thematic approach grounded in the Canadian Model of Occupational Participation, to document interactions between participants and security checkpoint context. Findings reveal systemic barriers in the physical, social, institutional, and temporal contexts. Notably, challenges with space design, the ability to anticipate procedures, and the quality of interactions strongly influence the experience and create stress that extends beyond security checkpoints.This study helps to highlight the need to reframe the vision of services to promote inclusion for all. Rather than trying to accommodate an infinite number of possible scenarios, we propose adopting a flexible and adaptable approach focused on the needs of the individual, regardless of disability. Inclusion responsibility at security checkpoints must be a shared among all service providers affected by and affecting operations at security checkpoints. Collaborations between service providers and travellers affected by inclusion issues are essential to create solutions that align both with lived experience and operational realities.]]></description>
      <pubDate>Fri, 31 Jul 2026 16:18:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689984</guid>
    </item>
    <item>
      <title>Efficiency versus security in the transportation security agency (TSA’s) public-private partnership program (PPP)</title>
      <link>https://trid.trb.org/View/2685624</link>
      <description><![CDATA[Do public-private partnerships (PPPs) create more efficiency in a public organization? This review examines this issue with respect to Transaction Cost Theory (TCT) and privatization when the Transportation Security Agency started the Screening Partnership Program (SPP), a public-private partnership that hired private screening companies. First, there is an argument that privatization increases efficiency. Although a counterclaim is that privatization brings risks greater than efficiency.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:29:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685624</guid>
    </item>
    <item>
      <title>Rapid trace explosive screening using high-speed GC coupled with tandem DMS for aviation security applications</title>
      <link>https://trid.trb.org/View/2683131</link>
      <description><![CDATA[Rapid and reliable detection of trace explosives is a critical requirement for aviation security and defense applications. Conventional standalone ion mobility spectrometry (IMS) provides portability and high sensitivity but suffers from limited resolving power and susceptibility to ion suppression, which can result in false positives or negatives. In this study, we present a hybrid analytical platform integrating a flow-field thermal-gradient gas chromatograph (FF-TG-GC) with a tandem differential ion mobility spectrometer (DMS-DMS). The FF-TG-GC enables rapid, high-resolution separation of multiple nitroaromatic and peroxidic explosives, including TNT, RDX, PETN, and TATP, within seconds. The tandem DMS configuration enhances selectivity and overall analytical robustness by exploiting unique ion mobility patterns and compensating for matrix effects. The system was rigorously evaluated in the presence of common interferents such as diesel fuel, Javon musk, and lactic acid vapors, simulating realistic airport environments. Chromatographic pre-separation effectively minimizes ion competition and suppression, while tandem DMS allows accurate and reproducible discrimination of target analytes from background compounds. Experimental results demonstrate that the integrated platform achieves high-throughput, reproducible, and selective trace detection, even under challenging operational conditions. This approach provides a reliable and rapid screening methodology suitable for deployment at aviation security checkpoints, offering both operational efficiency and analytical confidence.]]></description>
      <pubDate>Thu, 25 Jun 2026 15:57:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683131</guid>
    </item>
    <item>
      <title>Advancements in X-ray imaging technologies for enhancing airport security and operational efficiency in air transportation</title>
      <link>https://trid.trb.org/View/2680679</link>
      <description><![CDATA[X-ray imaging remains the most popular technology in aviation security screening to date, and recent advances in imaging hardware, particularly in computed tomography (CT) technology, as well as artificial intelligence (AI) assistance in threat analysis, have enhanced screening capabilities while also introducing new challenges. This paper presents a comprehensive and application-focused review of X-ray screening technology and AI-assisted threat analysis in air transport security. The review encompasses the background of X-ray imaging technology, the integration of AI for automated and semi-automated threat analysis, and the characteristics of typical datasets and approaches. Unlike the past reviews, which mainly focused on the algorithmic aspects, this review offers a focus on the operational context through the review of passenger checkpoint screening, checked baggage screening, and cargo screening as individual application domains. Case studies from large international airports and smaller airports across the world are used to illustrate the effect of throughput requirements, infrastructure, and budget on deployment. Finally, the article offers the necessary future research directions to improve robustness, generalization, and human-centric system design for AI-assisted X-ray screening systems. This provides a comprehensive resource for researchers, system developers, and aviation experts to address the existing gap between technological development and operationalization in security.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680679</guid>
    </item>
    <item>
      <title>Leveraging 2D object detection models for accurate biomatter threat identification in 3D CT scans of baggage</title>
      <link>https://trid.trb.org/View/2681185</link>
      <description><![CDATA[The detection of biomatter threats in baggage is a critical task for ensuring biosecurity, especially at international borders. This study introduces a novel methodology for converting 3D computed tomography (CT) volumetric data into 2D representations to enable efficient object detection using state-of-the-art 2D models. Systematic time-series feature engineering techniques were applied to vertical variation sequences from 3D data, transforming complex volumetric structures into compact and informative 2D projections. The proposed method leverages Light Gradient Boosting Machine and Random Forest (RF) estimators, combined with dimensionality reduction techniques such as Principal Component Analysis (PCA) and Partial Least Squares Regression, to select the most discriminative features. Evaluations using the YOLOv10l object detection framework demonstrated high detection accuracy, achieving a mean average precision of 0.851 with RF and PCA. Despite challenges such as class imbalance and computational trade-offs, the methodology offers a scalable, efficient, and highly accurate approach to biomatter detection. This study not only addresses limitations in current 3D image analysis techniques but also highlights potential applications in medical imaging and industrial inspection.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681185</guid>
    </item>
    <item>
      <title>Effects of false alarms and miscues of decision support systems on human–machine system performance: a study with airport security screeners</title>
      <link>https://trid.trb.org/View/2625881</link>
      <description><![CDATA[We tested 115 professional airport security screeners using realistic X-ray images of cabin baggage.Screeners were supported by an explosives detection system for cabin baggage (EDSCB) to help them detect bombs.Besides correct explosives alarms, the EDSCB made false alarms on target-absent images or miscues on images containing guns or knives that were localised elsewhere on the image.Screeners missed more knives when the EDSCB provided miscues than when the EDSCB provided no miscues.Instead of on-screen alarm resolution of EDSCB alarms in primary screening, automated decision or clear instructions to screeners should be considered.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625881</guid>
    </item>
    <item>
      <title>Metro Security Inspection Method Based on Spatial Feature Transformation Network</title>
      <link>https://trid.trb.org/View/2643379</link>
      <description><![CDATA[In order to ensure the safety of urban rail transit, X-ray baggage inspection systems have been widely used. However, at the current, baggage inspection mainly relies on manual judgment, which has the problems of high labor cost, low efficiency and lack of objectivity. This paper aims to realize intelligent and automated baggage inspection and studies and designs a dangerous goods detection method based on spatial feature transformation network. This method adopts target detection technology based on Anchor mechanism, and transforms high-level semantic features spatially spatial transformation network layer. The feature pyramid fusion approach is employed to integrate the transformed high-level semantic features with the low-level detailed features, resulting in the generation of multi-scale features that are rich in semantic information. This innovative method enables accurate prediction of the categories and precise localization of various hazardous items within X-ray images, thereby significantly enhancing the detection accuracy. It proves the feasibility of using intelligence algorithms for automatic security inspection, and provides strong support for the implementation of automatic monitoring of urban rail transit baggage inspection.]]></description>
      <pubDate>Tue, 20 Jan 2026 10:11:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643379</guid>
    </item>
    <item>
      <title>Passengers' baggage operations between airline responsibilities and airport authority – A review</title>
      <link>https://trid.trb.org/View/2610822</link>
      <description><![CDATA[This study critically examines academic literature on air baggage operations to address complex challenges impacting passenger experience, including security, operational efficiency, and economic factors. Through scientometric analysis of 89 Scopus-indexed studies and 37 on Google Scholar. Key research trends in security/tracking technologies, economic impacts of baggage fees, and operational efficiency are identified. Persistent issues like inter-organizational coordination, mishandling, and technological limitations are highlighted. Policy implications suggest integrated systems, advanced RFID/AI implementation, and revised baggage fee structures balancing cost and service quality for enhanced traveler satisfaction and industry streamlining.]]></description>
      <pubDate>Tue, 23 Dec 2025 09:51:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610822</guid>
    </item>
    <item>
      <title>An analysis of X-ray screening performance of newly recruited security officer: an exploratory study on undetected threats by demographic and work environments</title>
      <link>https://trid.trb.org/View/2606478</link>
      <description><![CDATA[This study investigates the screening performance of Newly Recruited airport security screeners at Incheon International Airport, focusing on the occurrence of undetected threats during X-ray screening assessments. A total of 142 newly appointed screeners were evaluated following the completion of initial training. Statistical analyses, including independent samples t-tests, were conducted to examine differences in detection performance based on demographic (age, gender) and operational (terminal assignment) contexts. Results revealed significant disparities by terminal, while differences by age and gender were not statistically significant. Furthermore, the study identified specific categories of prohibited items with high undetected rates, highlighting areas of common difficulty among newly recruited screeners. These findings provide practical insights into early-stage performance and underscore the need for targeted educational interventions to strengthen threat detection capabilities in aviation security.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:23:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606478</guid>
    </item>
    <item>
      <title>Reliability and validity of threat image projection data as a measure of performance in X-ray baggage screening</title>
      <link>https://trid.trb.org/View/2596969</link>
      <description><![CDATA[Passenger baggage is screened using X-ray machines at airports worldwide to ensure transportation security. Many airports use a technology called threat image projection (TIP) to measure detection performance of airport security officers (screeners). TIP projects prerecorded X-ray images of prohibited items into X-ray images of passenger baggage being screened, and each time a TIP image is displayed (a 𝘛𝘐𝘗 𝘦𝘷𝘦𝘯𝘵), the TIP system records whether the screener detected the prohibited item. Because the prohibited items and the location of their placement in bags are randomly selected, the resulting TIP images vary substantially in difficulty and do not always look realistic. It is therefore not clear whether TIP data provides a good measure of screener performance, despite the technology’s long-standing and widespread use at airports. To address this research gap, we conducted a study to estimate TIP’s psychometric properties of reliability and validity by analysing a large set of TIP events from cabin baggage screening of a European airport (1,199,838 TIP events from 728 screeners over four years). We found the reliability of performance measurement to increase with the number of TIP events in accordance with the Spearman–Brown prediction. Approximately 100 TIP events were sufficient to achieve a minimum reliability value of 0.7 when TIP was challenging enough (mean hit rate below 0.9). TIP performance predicted the covert test results (wherein instructed people tried to smuggle real prohibited items through the checkpoint; 1,184 covert tests from 474 screeners), indicating that TIP is a valid measure of detection performance in X-ray baggage screening. The results imply that TIP data provides a reliable and valid performance measure if the TIP images are challenging enough and about 100 TIP events are considered per screener.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:22:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596969</guid>
    </item>
    <item>
      <title>Visual inspection problem-solving strategies at different experience levels</title>
      <link>https://trid.trb.org/View/2499655</link>
      <description><![CDATA[Airport security screening is a visual inspection task comprising search and decision. Problem solving is used to support decision making. However, it is not well understood. This study investigated how airport security screeners employ problem solving during x-ray screening, and how strategies change with experience. Thirty-nine professional security screeners were observed performing x-ray screening in the field at an Australian International Airport. Video and eye-tracking data were collected and analysed to explore activity phases and problem-solving strategies. Less-experienced screeners performed more problem solving and preferred problem-solving strategies that rely on visual examination without decision support or that defer decision making, compared to more-experienced screeners, who performed efficient and independent strategies. Findings also show that screeners need more time to develop problem-solving skills than visual scanning skills. Screeners would benefit from problem-solving support tools and intensified training and mentorship within the first six months of experience to advance problem-solving competencies.]]></description>
      <pubDate>Tue, 11 Mar 2025 09:38:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2499655</guid>
    </item>
    <item>
      <title>Detecting airport luggage dimensions through low-cost depth sensors</title>
      <link>https://trid.trb.org/View/2408354</link>
      <description><![CDATA[A factor that impacts airlines' resources is the verification of luggage’s dimensions during the boarding process. Companies often rely on a human operator to perform this check using a manual template, which can cause delays. As an alternative, companies are investing in self bag drop systems. This process introduces new technological challenges since, in this scenario, checking the conformity of luggage dimensions is delegated to the passenger, which can lead to errors. In addition, current solutions use specific computational devices, such as laser scanners, that are expressive in size and cost, which may require interventions in the airport infrastructure. To overcome this, isolated initiatives are observed with alternative technologies, such as low-cost depth sensors, but they usually come without a scientific investigation. In this sense, this work investigates the technical viability of using such low-cost devices to obtain the dimensions of airport baggage. To do so, the authors developed a model that obtains a 3D point cloud of the luggage surface through a Microsoft Kinect V2 sensor. This cloud is treated and processed to extract the dimensions of the luggage. In order to validate this approach, a full-scale physical prototype was built and tested. The results indicate that the mean absolute error of the obtained dimension by the proposed model is 2.86 cm. Such data suggest that this technology has the potential to become an alternative to detect the dimensions of airport luggage.]]></description>
      <pubDate>Tue, 27 Aug 2024 16:08:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2408354</guid>
    </item>
    <item>
      <title>Enhancing baggage inspection through computer vision analysis of x-ray images</title>
      <link>https://trid.trb.org/View/2310170</link>
      <description><![CDATA[This research work explores the utility of deep learning algorithms in enhancing the accuracy of weapon detection, specifically guns, within x-ray images of travel bags. Utilizing Faster R-CNN as a baseline model, the research aims to augment detection metrics including accuracy, precision, and recall, thereby fortifying security screening procedures. A comparative study was executed between the Faster R-CNN model and a hybrid model that integrated the Segment Anything (SAM) algorithm with Faster R-CNN. Evidently, the hybrid model displayed an edge in performance with the highest accuracy rate of 86.34%, a marked increase from the 72.02% accuracy of Faster R-CNN alone. The fusion model demonstrated superior precision, signaling a decrease in false positive instances, although it faced a higher rate of false negatives, as revealed by its recall rate. This study also unearths data limitations that could potentially be inhibiting maximum model performance, given the discrepancy between available training data and the sheer volume of the comprehensive SIXray dataset. The research concludes by charting avenues for future investigation which include data augmentation, SAM model pre-training, and expansion of detection capabilities to encompass a broader array of weapons. This body of work establishes a framework for advancing security measures through the application of artificial intelligence.]]></description>
      <pubDate>Thu, 11 Jan 2024 11:59:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2310170</guid>
    </item>
    <item>
      <title>Aviation Security: TSA Could Better Ensure Detection and Assess the Potential for Discrimination in Its Screening Technologies</title>
      <link>https://trid.trb.org/View/2270156</link>
      <description><![CDATA[The Transportation Security Administration (TSA) employs passenger and baggage screening technologies to mitigate the threat of terrorism. TSA has faced challenges ensuring these technologies consistently meet detection requirements. The agency has also faced allegations that some of its screening practices, such as the use of advanced imaging technology, may refer certain passengers more frequently to additional screening. This statement discusses TSA’s efforts to (1) ensure passenger and baggage screening technologies continue to meet detection requirements after deployment and (2) assess the extent to which its use of advanced imaging technology refers certain passengers to additional screening more often than others. This statement by Tina Won Sherman, Director, Homeland Security and Justice, is based primarily on reports the Government Accountability Office (GAO) issued in December 2019 and November 2022 on detection requirements for TSA screening technologies (GAO-20-56) and TSA’s efforts to ensure its passenger screening practices do not result in discrimination (GAO-23-105201). To report on actions taken to address recommendations, GAO assessed implementation reports and reviewed agency documents and responses. GAO made a total of nine recommendations in the December 2019 and November 2022 reports. The Department of Homeland Security (DHS) concurred with all nine recommendations. Seven remain open. However, TSA has taken steps to implement them, including the four recommendations to address the issues discussed in detail in this statement.]]></description>
      <pubDate>Tue, 31 Oct 2023 09:24:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2270156</guid>
    </item>
    <item>
      <title>Time on task and task load in visual inspection: A four-month field study with X-ray baggage screeners</title>
      <link>https://trid.trb.org/View/2192775</link>
      <description><![CDATA[Previous studies suggest that performance in visual inspection and typical vigilance tasks depend on time on task and task load. European regulation mandates that security officers (screeners) take a break or change tasks after 20 min of X-ray baggage screening. However, longer screening durations could reduce staffing challenges. The authors investigated the effects of time on task and task load on visual inspection performance in a four-month field study with screeners. At an international airport, 22 screeners inspected X-ray images of cabin baggage for up to 60 min, while a control group (N = 19) screened for 20 min. Hit rate remained stable for low and average task loads. However, when the task load was high, the screeners compensated by speeding up X-ray image inspection at the expense of the hit rate over time on task. The authors' results support the dynamic-allocation resource theory. Moreover, extending the permitted screening duration to 30 or 40 min should be considered.]]></description>
      <pubDate>Wed, 19 Jul 2023 09:38:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2192775</guid>
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