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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>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Volatility spillover between financial assets and transport energy</title>
      <link>https://trid.trb.org/View/2623160</link>
      <description><![CDATA[This study examines the dynamics of volatility spillover between financial assets and transport energy. Such spillovers are critical for implementing risk management strategies, supporting energy policymaking, and enabling more effective investment decisions in today's globally integrated financial-energy system. Our study includes financial assets (both non-green and green) and transport energy (fossil fuels and biofuels). We employ Time-Varying Parameter Vector Autoregressive (TVP-VAR) approaches to investigate the volatility between financial assets and transport energy from 2018 to 2023. Our findings show that: (1) most financial assets unilaterally and positively affect the volatility in transport energy, except for biodiesel; (2) long-term volatility contributes significantly to overall volatility spillover. Besides, fossil fuel shows more long-term volatility transmission, while ethanol shows more short-term volatility; (3) the level of volatility connectedness experienced an abrupt and significant spike during the COVID-19 pandemic and the Russo-Ukrainian war. The abrupt spike is much greater during the COVID-19 pandemic; (4) greater volatility spillovers occur at extreme settings (i.e., 5th and 95th percentiles). We use these findings to offer insights for policymakers and stakeholders to develop strategies that mitigate volatility risks in both financial and energy markets.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623160</guid>
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    <item>
      <title>Dynamic relationships, investment performance, and hedging efficacy: Insights from marine shipping equities and major financial asset classes during black swan events</title>
      <link>https://trid.trb.org/View/2590208</link>
      <description><![CDATA[Amid the turbulent periods of the COVID-19 outbreak and the war in Ukraine, this study investigates the dynamic relationships, portfolio performance, and hedging effectiveness of marine equities in relation to key asset classes—namely commodities, stocks, foreign currencies, cryptocurrencies, and bonds. To achieve this, the authors employ a Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness framework. The empirical results reveal a notable increase in average interconnectedness during both black swan events, with dynamic interdependencies peaking during the COVID-19 outbreak and remaining elevated throughout the Ukraine war. Furthermore, the findings indicate that the inclusion of marine equities enhances portfolio performance during the COVID-19 pandemic, although this benefit diminishes over the entire sample period, particularly during the Russia-Ukraine conflict. The hedging analysis demonstrates that marine equities possess strong hedging capabilities. However, the bivariate portfolio analysis shows that pairing marine equities with crude oil futures, natural gas futures, or the Baltic Dry Index increases the cost of hedging against marine equity risks. Collectively, these findings support the strategic inclusion of marine equities alongside other asset classes, particularly during pandemics. Marine equities emerge as a resilient option for investors seeking to construct robust portfolios in response to health crises, though their effectiveness is less pronounced during periods of geopolitical or military conflict.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590208</guid>
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    <item>
      <title>YOLO-RAPD: Enhanced YOLOv8s-Based Automated Detection of Road Assets and Pavement Distress</title>
      <link>https://trid.trb.org/View/2582842</link>
      <description><![CDATA[This paper introduces an object detection algorithm called you only look once-road assets and pavement distress (YOLO-RAPD), developed to automatically identify road assets and pavement distresses. YOLO-RAPD is an improvement based on you only look once version 8 small (YOLOv8s). Unlike YOLOv8s, YOLO-RAPD utilizes a backbone network with multilayer fusion and an enhanced feature extraction network that autonomously selects the number of cycles to optimize feature extraction. Additionally, a linking method is employed to aggregate results from multiple loop layers. In tests using 6,333 images, YOLO-RAPD-1 (the smallest configuration) achieved a mean average precision (mAP) of 68.06% at a confidence level of 0.5, with a processing speed of 102  frames/s (FPS). YOLO-RAPD-4 (the larger configuration) achieved mAP of 75.36% at a confidence level of 0.5, with a processing speed of 40 FPS. Compared with several advanced models [you only look once version 8 nano (YOLOv8n), YOLOv8s, you only look once version 8 medium (YOLOv8m), you only look once version 8 large (YOLOv8l), you only look once version 8 extra-large (YOLOv8x), and fully convolutional one-stage object detection (FCOS)], YOLO-RAPD demonstrated superior detection accuracy on the same test set. To further validate the model’s generalizability and performance under complex conditions, the authors trained and tested it on the publicly available CeyMo data set. The results showed that YOLO-RAPD maintains high detection efficiency across different scenarios, highlighting its strong generalization capability. Notably, the detection accuracy and speed of YOLO-RAPD can be fine-tuned by adjusting the number of autonomously selected loop layers, suggesting that this model holds significant potential for the automated detection of road assets and pavement distresses.]]></description>
      <pubDate>Wed, 13 Aug 2025 09:25:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582842</guid>
    </item>
    <item>
      <title>Benchmarking of Intangible Assets in the Shipping Industry</title>
      <link>https://trid.trb.org/View/2577042</link>
      <description><![CDATA[The purpose of the paper is to develop a methodical approach to benchmarking of intangible assets on the example of the shipping industry in the context of the influence of intangibles on the financial results and market capitalisation of companies. The methodology of the research is based on a systematic approach, theory of benchmarking, financial analysis and assets valuation, theoretical foundations of maritime economics, and finance. The following scientific methods were used in the study: analysis and synthesis of results and retrospective, methods of econometrics, comparative, logic, and analytical methods. A methodical approach to comparative analysis of financial indicators, characterising intangible assets and their interpretation, has been elaborated. The proposed approach is illustrated on the example of container companies. The approach to benchmarking intangibles in the shipping industry makes it possible to perform a statistical analysis of indicators characterising the impact of the value of intangible assets on the financial results of shipping and logistics companies and their market capitalisation. The shipping industry is characterised by a low share of intangible assets, however, the growth in the share of intangibles has shown their importance for improving business efficiency of shipping companies in terms of financial indicators. The growth dynamics of goodwill to assets ratio has demonstrated the intensification of merger and acquisition processes in container sector of liner shipping. The multiples of financial results to the value of intangible assets were supplemented by correlation-regression analysis of dependence of the net profit on the value of intangibles. Analysis of the share of intangible assets in the market capitalisation and Tobin’s Q make it possible to assess the impact of the value of intangibles on the market value of companies. The results of analysis of annual Tobin’s Q showed the assets of container companies to be slightly undervalued by capital markets.]]></description>
      <pubDate>Mon, 21 Jul 2025 08:56:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577042</guid>
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    <item>
      <title>Mathematical Model of Intellectual Capital Management as the Basis for the Development of a Transport Company</title>
      <link>https://trid.trb.org/View/2407781</link>
      <description><![CDATA[Intellectual capital is a property of human activity that characterizes the ability of a person to receive added value guaranteed on the basis of the realization of his intelligence. To obtain a certain added value, it is necessary to solve certain tasks. If the authors consider activity as a random process, then it is advisable to define the indicator of intellectual capital as the probability that each task that arises before a person is identified and solved in the interests of achieving the required level of added value. Intellectual capital management is a purposeful activity to form a mathematical model of one’s decision on the use of company assets in the interests of obtaining the required added value. Without a methodology for solving intellectual capital management problems, in the form of conditions for the existence of the process, the authors cannot guarantee the achievement of the activity goal. The article presents the necessary mathematical apparatus to make informed decisions under managing the intellectual capital of a transport company, in the process of achieving its main goal – making a profit. The simulation results confirmed the main trends in the intellectual capital management process.]]></description>
      <pubDate>Fri, 21 Mar 2025 09:36:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407781</guid>
    </item>
    <item>
      <title>What Is Life-Cycle Management and What Can It Do for Me and My Port Structures?</title>
      <link>https://trid.trb.org/View/2218016</link>
      <description><![CDATA[Life-cycle management (LCM) is a term familiar to many design engineers, but is understood by few. Some even dismiss it as the latest hollow buzzword with little practical value. Nothing could be further from the truth. In fact, LCM at its best represents a structured approach to asset planning, design, construction, and operation that maximizes functionality while minimizing life-cycle costs. This paper summarizes the recent work of the International Navigation Association (formerly known as PIANC) Working Group 42, of which the author is a U.S. Delegate. The LCM concept is generally in a primitive stage of implementation worldwide, with European nations somewhat ahead of their counterparts in other nations.]]></description>
      <pubDate>Mon, 16 Dec 2024 11:59:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2218016</guid>
    </item>
    <item>
      <title>Optimal operation strategies of an urban crowdshipping platform in asset-light, asset-medium, or asset-heavy business format</title>
      <link>https://trid.trb.org/View/2450671</link>
      <description><![CDATA[This paper investigates the operation strategies of an urban crowdshipping platform, which utilizes the latent capacity of the traveling ‘crowd’ in the transportation system to facilitate parcel delivery. The authors develop an analytical model to characterize the decision-making and operation strategies of a crowdshipping operator in alternative business formats (asset-light/medium/heavy). Asset-light platforms connect customers with potential carriers in the crowd without involving delivery assets, whereas asset-medium and asset-heavy operators integrate crowd carriers with outsourced or owned delivery fleets, respectively. In particular, the authors firstly formulate the two-sided market equilibrium of crowdshipping system on account of customers’ willingness to use and crowds’ willingness to serve. Based on the market equilibrium, the crowdshipping operator’s optimal strategies in terms of pricing and/or fleet sizing are identified for profit-maximization or social welfare-maximization in alternative business formats. The authors show that the introduction of crowdshipping can simultaneously improve the benefits of logistics customers, the crowd, and the crowdshipping platform operator, leading to a win-win-win outcome. Furthermore, the authors establish analytical conditions for one business format being superior to another. The authors find that if the externality (or marginal social cost) of an unmatched order is smaller in a particular business format, it will result in larger consumer surplus for customers, greater net benefit for crowd carriers, and more profit for crowdshipping operator. Under mild conditions, the crowdshipping operator adopting the asset-light or asset-medium format can earn a positive profit at the social optimum.]]></description>
      <pubDate>Wed, 20 Nov 2024 12:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2450671</guid>
    </item>
    <item>
      <title>Impact of the Use of Intellectual Assets on the Economic Growth of Russian Railways</title>
      <link>https://trid.trb.org/View/1972917</link>
      <description><![CDATA[The proposed paper is relevant because of the significant increase in the role of intellectual assets in ensuring the competitiveness of companies, especially companies with complex technological processes and extreme technologies. The object under the study, JSC Russian Railways (JSC “RZD”), belongs to such companies. The highest level of capital intensity of the company causes the need to balance it with the level of intelligence capacity, which will ensure its smooth economic growth. It should be noted that the assessment of the intellectual capital of organizations is a significant problem, and a serious problem is the consideration of assets in a quantitative assessment of the organizations’ efficiency. The aim of the study is a quantitative assessment of the nature of intellectual assets of JSC “RZD” and their impact on economic growth. The research methodology includes classical methods of scientific cognition (observation, analysis, synthesis, formalization, logic) and special methods, such as: the formation of a system of indicators for the quantitative and qualitative assessment of the main parameters of the activity of the object under consideration, analysis of time series, and correlation and regression analysis of factor systems. Analysis of the impact of the company’s intellectual assets on its economic growth showed a very close direct connection between its intelligence capacity and economic growth, as well as the positive dynamics of the effectiveness of the use of intellectual assets.]]></description>
      <pubDate>Fri, 01 Nov 2024 08:50:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1972917</guid>
    </item>
    <item>
      <title>The Intellectual Capital Impacts on Logistics Business Performance</title>
      <link>https://trid.trb.org/View/2282764</link>
      <description><![CDATA[This study establishes an integrated cause-and-effect model to investigate the impacts of intellectual capital on logistics business performance and examines the relationships among intellectual capital elements. The performance measures include accounting-based and market-based performance factors. The partial least squares approach is applied to examine the collected data. The results show that intellectual capital does affect logistics business performance and each intellectual capital element not only directly influences performance, but also indirectly affects performance through the cause-and-effect relationship between intellectual capital elements. Human capital positively affects both innovation capital and process capital, innovation capital effects on process capital which, in turn, affects customer capital, and the customer capital ultimately affects logistics business performance.]]></description>
      <pubDate>Mon, 16 Sep 2024 08:55:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282764</guid>
    </item>
    <item>
      <title>Assessing the Financial Stability of Warsaw-Listed Transport and Logistics Firms</title>
      <link>https://trid.trb.org/View/2370866</link>
      <description><![CDATA[Purpose: The primary objective of this paper is to evaluate the financial security of transport and logistics companies listed on the Warsaw Stock Exchange. Methodology: The research employs various financial security indicators such as liquidity, capital coverage of assets, working capital, and others. The study focuses on 11 companies in the transport and logistics sector listed on the Warsaw Stock Exchange in the third quarter of 2023. Results: The findings reveal that companies in the transport and logistics sector are highly diverse and challenging to compare on non-financial security aspects. Average data do not reflect the real situation in the sector, and individual companies significantly influence average results. The importance of maintaining an optimal structure of assets and capital for ensuring financial security is also highlighted. Theoretical contribution: This paper contributes to financial security in the transport and logistics sector by analysing companies listed on the Warsaw Stock Exchange. It offers a new perspective on the financial stability of these companies and the factors influencing it. Practical implications: The research calls for caution when assessing the financial security of enterprises in the transport and logistics sector. It also suggests expanding the research group and checking whether the entire sector has any common patterns regarding financial security. This could provide valuable insights for companies in the sector and policymakers.]]></description>
      <pubDate>Tue, 28 May 2024 10:43:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2370866</guid>
    </item>
    <item>
      <title>A freight asset choice model for agent-based simulation models</title>
      <link>https://trid.trb.org/View/2223092</link>
      <description><![CDATA[Agent-based models (ABM) for transportation operations have been largely focused on passenger trips, while recent developments in the field began the incorporation of freight-related operations. However, these models rarely incorporate freight-related strategic asset decisions: fleet and distribution center (DC) ownership. These attributes are important for modeling freight transportation behavior in ABMs. This research develops behavioral models that jointly predict fleet ownership and distribution center control for freight-related firms in an ABM framework. A seemingly unrelated Tobit regression is estimated using large-scale data from more than 11 million establishments. The model is estimated using a Bayesian approach that allows for the quantification of the coefficients’ variability. Model results indicate that firms with higher revenue have an increased propensity to own fleet and (or) DC and prefer larger fleets and more DC space. Furthermore, Transportation firms generally have more heavy-duty trucks and much fewer medium-duty trucks, while firms in all other sectors strongly prefer medium-duty fleets. Food Services and other Retail firms have the greatest preference for owning or leasing their own DCs, followed by Manufacturing, Wholesale, and Transportation firms. A case study is developed for the city of Chicago using a high-performance ABM framework designed for simulating large-scale transportation systems. Transportation modelers and policymakers can use findings and methods from this research to study freight operations in large cities.]]></description>
      <pubDate>Mon, 04 Sep 2023 18:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2223092</guid>
    </item>
    <item>
      <title>Assessing Criticality in Transportation Adaptation Planning</title>
      <link>https://trid.trb.org/View/2162247</link>
      <description><![CDATA[Before initiating a climate change vulnerability assessment, transportation agencies need to decide which assets they wish to evaluate. Identifying the relevant assets for a vulnerability study and determining which characteristics of these assets to examine can help agencies narrow the scope of the study, making it more manageable and affordable while allowing more in-depth assessment of the selected group of assets. One way to narrow the range of assets to be evaluated is to conduct a criticality assessment, which involves identifying the most critical elements of the transportation system for analysis, using quantitative or qualitative criteria. A criticality assessment provides a structured way to focus on assets that are most important for the functioning of the transportation system. This memorandum discusses common challenges associated with assessing criticality, options for defining criticality and identifying scope, and the process of applying criteria and ranking assets. It uses examples from the Federal Highway Administration (FHWA) pilots and the Gulf Coast 2 study to illustrate a variety of approaches that have been used for assessing criticality. The Appendix lists criticality criteria developed under the Gulf Coast Study, Phase 2, along with brief explanations for why each criterion was chosen.]]></description>
      <pubDate>Tue, 02 May 2023 18:40:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2162247</guid>
    </item>
    <item>
      <title>Resilience</title>
      <link>https://trid.trb.org/View/2006425</link>
      <description><![CDATA[Through the lens of civil engineering and infrastructure, resilience is generally considered on three scales: community or urban, organizational, and individual assets. For human or ecological populations, maintaining population levels above mortality levels and the overall condition or health of the population is often the focus of resilience thinking. A resilient organization can continue to exist and deliver services during and after major external or internal disturbances. The challenge of addressing resilience increases in scope and complexity from the scale of individual assets through to organizations and communities. From the perspective of the civil engineer, implementing resilience on the scale of infrastructure demands consideration of more than engineering design attributes and encompasses, for example, organizational attributes of the institutions charged with owning and operating civil infrastructure.]]></description>
      <pubDate>Mon, 26 Sep 2022 09:12:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2006425</guid>
    </item>
    <item>
      <title>Impact of informal assets and informal liabilities on the determined market value of a company (case study of a transport company)</title>
      <link>https://trid.trb.org/View/1993410</link>
      <description><![CDATA[Research Objectives. This article examines the impact of informal assets and informal liabilities held by a company on the determinable value of the business. Methodology and research. As part of the study of the process of identifying and accounting for informal assets and liabilities in the valuation of the company the works on valuation activities, as well as an analysis of regulatory and legal sources were studied. The authors considered the regulatory framework of the concept of "asset" and "liability" and analysed the possibility of accounting in the valuation activities (and other analytical procedures) of informal assets and liabilities, i.e. economic benefits and obligations that do not have all the formal qualifying characteristics (according to accounting rules). Hidden, imaginary and probable assets as well as economic analogues of assets are considered as part of informal assets. The authors considered the specifics of accounting and valuation of assets, the alienation of which from the organisation is impossible or unlikely. Hidden, imaginary and probable liabilities as well as economic analogues of liabilities are considered as part of informal liabilities. In order to present the results of the study more fully and clearly, the authors considered aspects of the impact of informal assets and informal liabilities on the example of a transport company (carriage of goods and passengers). Results. As a result of the analysis, the article shows that there are assets which are not recorded in the accounting records, but which ultimately affect the value of the company. Such assets cannot be recorded and reported in the accounting (financial) statements because they do not qualify (hidden assets). At the same time, assets may be recorded that are not justified there due to an error or because the assets have lost their qualifying characteristics. These assets are defined by the authors as being imaginary. There are liabilities that have not been recognised in the accounting records, but which affect the value of the company. Such liabilities cannot be entered in the accounting records and recognised in the financial statements because they do not meet the qualification requirements (hidden liabilities). Liabilities may be recognised in accounting that are not reasonably recognised there due to an error or due to the loss of qualifying characteristics of the liability (imaginary liabilities). It is also appropriate, from a financial point of view, to include liabilities equated to equity as imaginary liabilities. In the course of the study, the authors considered the impact of informal assets and informal liabilities on the determined value of a transport company. The authors propose scientific and methodological principles for identifying, classifying and valuing informal assets and liabilities.]]></description>
      <pubDate>Wed, 17 Aug 2022 09:33:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1993410</guid>
    </item>
    <item>
      <title>2020 TAM Data Summary: A Snapshot of Asset-Related Data Reported to the National Transit Database</title>
      <link>https://trid.trb.org/View/2004420</link>
      <description><![CDATA[This report summarizes data that transit agencies reported to the National Transit Database (NTD), providing an inventory and assessment of the condition of assets used to provide transit service nationally. This report provides a snapshot of the data submitted for Report Year 2020, with some references and comparisons to the 2018 and 2019 report year data; 2018 was the first year in which transit agencies reported this information on transit assets, in accordance with the requirements of the Transit Asset Management (TAM) rule (49 CFR 625). This report highlights data that transit agencies reported, providing a comprehensive look at the wide range of capital assets supporting transit service, including revenue vehicles, equipment (service vehicles), facilities, and infrastructure (guideway and track). The data include information on count and age of assets, as well as current condition and expectations of agencies’ ability to maintain assets in a state of good repair, as indicated by the reported performance targets. The data are self-reported to the NTD by transit agencies based on the best quality information available to them.]]></description>
      <pubDate>Mon, 15 Aug 2022 08:44:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2004420</guid>
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