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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Exploring the Dynamics of Ride-Hailing Fares in Madrid: A Machine Learning Approach</title>
      <link>https://trid.trb.org/View/2581614</link>
      <description><![CDATA[Ride-hailing apps are getting increasingly common in cities all around the world. However, the major factors that determine how supply and demand interact to determine the ultimate prices are still mostly understood. By using statistical and supervised machine learning techniques (Linear Regression, Decision Tree, and Random Forest), this study aims to comprehend and forecast the behavior of ride-hailing fares. Ten months’ worth of data were taken from the Uber Application Programming Interface for the city of Madrid and used to calibrate the model. The results show that the Random Forest model is the most suitable for this kind of prediction due to its superior performance metrics. The unsupervised methodology of cluster analysis (using the k-means clustering method) was also used to examine the variation of the difference between Uber fare forecasts and observed values to better understand prediction error patterns. The investigation found that a tiny percentage of observations (approximately 1.96%) had substantial prediction errors due to unexpected surges caused by supply and demand imbalances, which typically happen during major events, peak hours, weekends, holidays, or when there is a taxi strike. This study assists in the understanding of pricing, service demand, and ride-hailing market pricing structures by policymakers.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581614</guid>
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    <item>
      <title>Shipping-Freight Forecasting with Multi-Domain Features: Role of News Sentiment</title>
      <link>https://trid.trb.org/View/2711987</link>
      <description><![CDATA[The shipping market is complex and nonlinear, which makes freight-rate forecasting highly challenging. This study proposes an STE-Informer model that integrates a multisource feature set. First, we collect 120,000 shipping-news articles from several major industry websites and use the FinBERT model to extract sentiment features, capturing market-sentiment fluctuations. Second, we apply technical analysis to construct a set of technical indicators, exploring the intrinsic information embedded in freight indices. Third, we incorporate economic features to reflect the impact of global economic conditions and external shocks on the shipping market. Finally, we integrate sentiment, technical, and economic features into a multidimensional feature matrix and employ the Informer deep neural network for freight-index forecasting. The results show three key findings: (1) short-term sentiment provides the best predictive performance, improving accuracy by 10%–70% compared with medium- and long-term sentiment; (2) the multisource feature set reduces mean squared error by 76.9%, 83.1%, 88.2%, and 92.5% across four shipping markets, respectively, relative to the no-feature baseline Informer(N); (3) the proposed STE-Informer model achieves the best overall performance in shipping-index prediction, ranking first in forecasting the Baltic Dry Index and Baltic Dirty Tanker Index.]]></description>
      <pubDate>Tue, 09 Jun 2026 10:54:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711987</guid>
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    <item>
      <title>Forecasting the consumption evolution of battery electric vehicles under dynamic market conditions: The case study of Jiangsu Province, China</title>
      <link>https://trid.trb.org/View/2616231</link>
      <description><![CDATA[Predicting the future market size of battery electric vehicles (BEVs) and their market share is essential for analyzing transport externalizations and optimizing charging infrastructure deployment. Current smooth-curve models, the system dynamics, and agent-based models for BEV market forecasting are usually static functions or rely on market interactions. Still, they hardly quantify the influencing effects and changes of covariates under dynamic market conditions. Given the above-mentioned, the BEV cumulative sales are forecasted under dynamic market conditions using the artificial neural network and the bidirectional short- and long-term memory models. The samples of five covariates are derived from available data about BEV sales, price changes, fuel-to-electricity ratio, charging piles, driving range, and incentive effects from the priorities of BEV license plates in Jiangsu province. Different evolutionary analyses are set the three future scenarios of the BEV sale market based on the Time-Series Multi-Layer Perceptron model, and the marginal effect of a single covariate was further analyzed. Finally, our results show the advantages of machine-learning methods over smooth-curve models used to generate market predictions, further providing insights on covariates effects for market managers to promote the BEV sale market.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:53:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616231</guid>
    </item>
    <item>
      <title>Early-phase evaluation and screening of infrastructure investment projects using the analogy methodology</title>
      <link>https://trid.trb.org/View/2604723</link>
      <description><![CDATA[This paper presents the Analogy Method as a pragmatic approach to address the challenges of transportation planning, under the Norwegian government's extensive investment plan of 1308 billion NOK for infrastructure improvements during 2025–2036. The plan aims to foster mobility, regional development and environmental sustainability, necessitating efficient resource allocation that meets political objectives while permitting quantifiable evaluation of impacts. Traditional analytical methods often fall short due to data limitations and time constraints faced by decision-makers. In response, this study demonstrates the broader applicability of the Analogy Method within the transportation sector with an analysis of high-speed railway impacts on regional development in the Eidsvoll and Aurskog-Høland areas. Through the comparative analysis of known phenomena with emerging projects, the Analogy Method facilitates early cost/benefit predictions, offering a strategic tool for decision-makers under pressure. This paper explores the concept of generalised travel costs (GCs) in assessing socio-economic impacts and illustrates the resulting time savings, cost efficiency and clarity in evaluations. The article outlines the method's theoretical foundation and applies it to a case study of a proposed railway station project, aiming to elucidate both the economic profitability for society and the implications for broader planning practices. In addressing two key research questions – whether the Analogy Method can effectively predict socio-economic impacts in data-scarce environments and how its application increases understanding of project benefits and costs – this paper aspires to bridge theoretical insights with practical applications. The findings encourage further exploration of the Analogy Method's potential in transportation planning and its capacity to support informed, data-driven decision-making amidst the complexities of infrastructure development.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604723</guid>
    </item>
    <item>
      <title>Assessment of Transport Corridors Efficiency in the Arctic Zone</title>
      <link>https://trid.trb.org/View/2407788</link>
      <description><![CDATA[Nowadays, the problem of determining the prospective contours of the development of the transport system of the Arctic part of Russia is becoming very urgent. The insufficient study of this territory considerably increases the cost and complicates the implementation of projects in the region. The purpose of this study is to assess the effectiveness of the transport directions development in the Arctic region of Russia. The work applies the methods for assessing the economic efficiency of investment projects under the conditions of radical and probabilistic uncertainties, using the Hurwitz, Bayes, Wald, Laplace, and Savage criteria. In the framework of the study the authors have achieved the following results: the analysis of the transport system development in the Arctic zone of Russia, based on the implementation of the methodology of investment projects efficiency, the evaluation of alternative scenarios for the formation of transport corridors in conditions of uncertainty is presented. The results of the study will expand the scientific and methodological basis for forecasting studies needed to form the key areas of strategic development of the transport system in Russia in accordance with the Strategy for the Railway Transport Development of the Russian Federation until 2030.]]></description>
      <pubDate>Tue, 25 Mar 2025 09:28:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407788</guid>
    </item>
    <item>
      <title>Economic Assessment of the Innovative Potential of Transport Corporation</title>
      <link>https://trid.trb.org/View/2407810</link>
      <description><![CDATA[An integrated indicator of net value added is proposed as the most convenient and understandable measure of the financial and economic component of the innovation potential of a corporation for all stakeholders in economic and legal relationships: shareholders, potential investors and managers. The research is based on methods of induction, deduction and general knowledge. Methods of logical, statistical and correlation analysis are also used. When assessing the innovative potential of a corporation, it is recommended to reduce the information asymmetry for principals and agents by using a modified indicator of economic value added (EVA) - net value added, depending on the number of production structural divisions of a legal entity, capital structure, and the amount of consolidated profit of subsidiaries and associated corporations. The production structural divisions of the corporation contribute to the formation of the net added value of products and increase the consolidated net profit. Due to the synergy effect, the innovative potential of the corporation is formed. To predict the innovative potential of a transport corporation, economic and mathematical modeling is used, which makes it possible to give an objective cost estimate of the financial and economic component for a period of three to five years, while reducing information asymmetry for principals and agents when making strategic management decisions.]]></description>
      <pubDate>Fri, 21 Mar 2025 09:14:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407810</guid>
    </item>
    <item>
      <title>Forecasting the Development of the Type of Economic Activity “Transportation and Storage”</title>
      <link>https://trid.trb.org/View/2407718</link>
      <description><![CDATA[The paper discusses the methodological and practical aspects of forecasting the development of the type of economic activity “Transportation and storage”. The relevance of the formation of a management mechanism and a system of economic forecasting at various levels of management due to an increase in the degree of uncertainty and risk, instability of the external environment and the need to develop a foresight system is revealed. The analysis of publications and aspects of forecasting currently being developed for various hierarchical levels of management is carried out. It is noted that at present, issues of forecasting at the macroeconomic and microeconomic levels are most fully developed. In connection with the carried out analysis, the purpose of the study is formulated. Macroeconomic conditions and requirements that are important for forecasting are given: the development of globalization processes, resource constraints, an increased degree of uncertainty and risk, and others. The specificity of the type of economic activity as an object of forecasting in a market economy is revealed. The characteristics of the information and instrumental base of the study are given. Analytical tools are used, provided by the theories of correlation and regression as the most effective and accurate ones. The composition of the predicted and factor indicators used is analyzed: external and internal, controlled and uncontrollable. The method of forecasting “Transportation and storage” as a type of economic activity is proposed and tested.]]></description>
      <pubDate>Wed, 19 Mar 2025 10:12:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407718</guid>
    </item>
    <item>
      <title>Exploring the Economic Feasibility of Advanced Air Mobility in the Early Stages</title>
      <link>https://trid.trb.org/View/2410641</link>
      <description><![CDATA[Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, the authors discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:11:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2410641</guid>
    </item>
    <item>
      <title>A Study on the Prediction of LNG Ship Spot Freight Rates using Artificial Intelligence</title>
      <link>https://trid.trb.org/View/2399755</link>
      <description><![CDATA[Natural gas is considered an environmentally friendly energy source, and its demand is continuously increasing. Consequently, the volatility of freight rates for Liquefied Natural Gas (LNG) carriers is also rising. Given this high volatility, there is a need for research to predict freight rates in advance, aiding the decision-making processes of shipping companies. While numerous studies have focused on freight rate prediction for various types of ships, research specifically targeting LNG carriers remains limited. This study exclusively utilizes freight data from 160K LNG carriers and employs the Long Short-Term Memory (LSTM) model, enhanced through hyperparameter tuning, to predict spot freight rates. Additionally, the study compares the predictive performance of the LSTM model with that of the Auto-Regressive Integrated Moving Average (ARIMA) and SARIMA models, which are well-established in time series analysis. The prediction experiments reveal that the LSTM-based model, in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), outperforms the others, offering the most accurate freight rate predictions. However, the hyperparameter-tuned model shows proficiency in predicting sudden increases in freight rates. This study suggests that using time series data alone can enhance the objectivity of freight rate predictions. Future research involving comparative analyses and experiments across various predictive models in high-performance computing environments is expected to improve prediction performance further. Such advancements would be valuable for forecasting shipping market conditions more effectively.]]></description>
      <pubDate>Tue, 30 Jul 2024 14:35:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2399755</guid>
    </item>
    <item>
      <title>Forecasting Methods for the Electric Vehicle Ownership: A Literature Review</title>
      <link>https://trid.trb.org/View/2380446</link>
      <description><![CDATA[The sustainability issue in the transportation sector brings the electric vehicle (EV) as a new promising solution for reducing carbon emissions. However, the EV adoption faces issues regarding range, recharging time, and high initial investment. To enhance the adoption, supporting infrastructures should be planned. Therefore, forecasting the growth of EV adoption becomes important to help industries and government in strategic decision making. This paper provides a literature review about forecasting methods in EV ownership, which includes studies from 2011 to March 2023. This will contribute to highlight the current methods and stimulating more developments of forecasting methods related to EV ownership.]]></description>
      <pubDate>Thu, 25 Jul 2024 17:11:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2380446</guid>
    </item>
    <item>
      <title>Pricing Adjustments of Investment Costs of Infrastructure Projects in the Slovak Republic: A Risk Worthy of Attention</title>
      <link>https://trid.trb.org/View/2325501</link>
      <description><![CDATA[The present article aims to highlight the risk of potential price adjustments associated with the uncertainty of market price volatility of construction activities during the implementation phase of infrastructure transport projects. Price adjustments in the conditions of the Slovak Republic are regulated by methodological guidelines, the main determinants of which are selected items of macroeconomic inflation indicators with a significant impact on the resulting volumes of financial claims for price adjustment and the total investment costs of project implementation. The impacts of the values of the macroeconomic forecast indicators in question on the volumes of price adjustments and co-financing of the public administration budget were examined based on regularly updated and published macroeconomic forecasts.]]></description>
      <pubDate>Tue, 28 May 2024 10:45:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2325501</guid>
    </item>
    <item>
      <title>Projections of the costs of light-duty battery-electric and fuel cell vehicles (2020–2040) and related economic issues</title>
      <link>https://trid.trb.org/View/2374081</link>
      <description><![CDATA[This paper provides a comprehensive analysis of the initial costs and total cost of ownership (TCO) for light-duty battery electric vehicles (BEVs) and fuel cell vehicles (FCVs) from 2020 to 2040, covering cars, SUVs, and light trucks, alongside the infrastructure requirements. Key findings indicate that by 2040, the initial costs for BEVs will align with those of gasoline vehicles if battery costs can reach cell-level $70/kWh (or pack-level $84/kWh). For FCVs, achieving cost parity with gasoline cars before 2040 will be challenging unless the cost of fuel cells decreases to about $40/kW through high-volume production (>500000 units). Regarding 5-year TCOs, both BEVs and FCVs are expected to be close to or slightly lower than those of gasoline vehicles by 2040 across all LDV market segments. Investment analysis for large fleets suggests that by 2040, public fast charging for BEVs could cost $2000/vehicle, and hydrogen refueling for FCVs $1100/vehicle. Additionally, the study assesses the impact of low carbon fuel standard (LCFS) credits on the profitability of refueling stations, concluding that these credits are essential for transforming potentially unprofitable stations into profitable ventures with returns of 5% or higher. This highlights the critical role of LCFS in financing BEVs and FCVs infrastructure.]]></description>
      <pubDate>Mon, 06 May 2024 14:02:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2374081</guid>
    </item>
    <item>
      <title>The Statistical Analysis of China's Logistics Development in 2008 and the Forecast of Its Development for the Following Two Years</title>
      <link>https://trid.trb.org/View/2282263</link>
      <description><![CDATA[Based on the statistical analysis of the data of China's logistics from 2004 to 2008, the characteristics and the operating status of the logistics development in 2008 were obtained; by adopting appropriate forecast methods, logistics basic data for 2009–2010 were forecasted, analyzed and evaluated.]]></description>
      <pubDate>Wed, 17 Jan 2024 11:50:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282263</guid>
    </item>
    <item>
      <title>Demand Forecast in Food Logistics of Sichuan Province in China</title>
      <link>https://trid.trb.org/View/2282246</link>
      <description><![CDATA[Food logistics is very important to Sichuan, which is a major province of food production and consumption. This paper analyzes the current situation of food logistics in Sichuan. In addition, the paper predicts the two parts of food logistic capacity by using forecast methods, respectively. Finally, the forecasted results provided the corresponding data and reference for decision-making to relevant food logistics planning and building of Sichuan.]]></description>
      <pubDate>Wed, 17 Jan 2024 11:50:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282246</guid>
    </item>
    <item>
      <title>Forecast of Logistics Demand in Hercynian Region Based on BP Neural Network</title>
      <link>https://trid.trb.org/View/2282239</link>
      <description><![CDATA[This paper aims at seeking the intrinsic relationship between regional economy and regional logistics so as to provide the necessary decision-making data and basis for the Hercynian logistics planning. On the basis of the analysis and comparison of various forecasting methods, forecasting models of logistics demand in Hercynian region will be constructed and logistics demand in Hercynian region will also be forecasted. The approach to the neural network model applied by this paper offers the forecast of regional logistics demand a new way of thinking and method.]]></description>
      <pubDate>Wed, 17 Jan 2024 09:41:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2282239</guid>
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