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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
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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>Failure mechanisms at asphalt/aggregate interfaces: coupled effects of interfacial heat conduction and molecular diffusion</title>
      <link>https://trid.trb.org/View/2643697</link>
      <description><![CDATA[The interfacial adhesion between asphalt and aggregate is critical to pavement performance, yet the dynamic visualisation of molecular detachment processes remains insufficiently explored. This study combines pull-off tests and molecular dynamics simulations to develop a model visualising asphalt molecule desorption from aggregate surfaces. A multiple regression model quantitatively analyses relationships among the interfacial failure force, critical energy, oxide composition and temperature. The key findings include: (1) Highly polar oxides (CaO, MgO) restrict molecular diffusion with coefficients as low as 1.2 × 10⁻⁵ cm²/s and activation energies of 34.8 kJ/mol, while low-polarity oxides (Fe₂O₃, SiO₂) exhibit lower diffusion barriers (15.6 kJ/mol), resulting in faster diffusion. (2) High-thermal-conductivity oxides (MgO at 2.4 W/m K and Al₂O₃ at 2.1 W/m K) facilitate rapid heat transfer, enhancing diffusion-thermal conduction coupling (r = 0.87) compared to low conductivity oxides (r = 0.42). (3) Elevated temperatures weaken asphalt–oxide interactions, with interfacial energy decreasing by 200 kcal/mol for Al₂O₃ between 0 °C and 40 °C. Asphalt residue is higher (~10%) on highly polar oxide surfaces. (4) Critical interfacial energy values range from -1300  kcal/mol (Al₂O₃) to −300 kcal/mol (CaCO₃), with highly polar oxides showing greater temperature sensitivity. (5) Temperature significantly affects the interfacial pull-off strength, decreasing peak values from 1.4 MPa at 0 °C to 0.4 MPa at 40 °C. For every 1 °C increase, the limestone interfacial failure force decreases by approximately 112.37 N, while granite decreases by 119.33 N. This study provides a framework for visualising asphalt molecular pulling and uncovering mechanisms underlying interfacial failure.]]></description>
      <pubDate>Sun, 22 Mar 2026 17:19:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643697</guid>
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    <item>
      <title>Efficiency and Sustainability of Bangkok Port Barge Transport: A Comparative Analysis with Feeder Vessels Under Thailand’s Logistics Strategy</title>
      <link>https://trid.trb.org/View/2598749</link>
      <description><![CDATA[This study assessed the effectiveness and sustainability of using barge versus feeder vessels to transport containerized cargo to Bangkok Port, Thailand. A survey of 387 stakeholders in marine logistics was conducted from October to December 2024. Multiple regression analysis (MRA) showed that cost-effectiveness, environmental impact, and operational flexibility primarily influenced transport mode choice, explaining 56.2% of the variance. Cost-effectiveness emerged as the key factor, while environmental impact was the strongest predictor of perceived sustainability. While operators favored feeders due to cost and time efficiency, barges scored higher due to environmental friendliness and operational flexibility. Notably, 68% of respondents preferred barges for short routes under 100 km due to their role in reducing road congestion and pollution. Furthermore, 73% expected greater barge use over the next five years, driven by technology and environmental policies. Improved waterway infrastructure would lead 82% to use barges more frequently, and 76% believed better intermodal integration would enhance logistics efficiency. This study is limited to the context of Thailand’s domestic maritime logistics and stakeholder perceptions, which may not be fully generalizable to other ASEAN or global port systems. Future research should explore multi-country comparative studies and assess longitudinal trends as green port policies evolve across Southeast Asia.]]></description>
      <pubDate>Wed, 29 Oct 2025 09:11:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598749</guid>
    </item>
    <item>
      <title>An improved fractional-order Tikhonov regularization method for impact load identification</title>
      <link>https://trid.trb.org/View/2604985</link>
      <description><![CDATA[In order to ensure the safety of carrier-based aircraft and ship structure, it is essential to effectively identify the impact load generated during the landing of carrier-based aircraft. However, during the process of inverting impact load based on ship structural parameters and structural responses, there exists an ill-posed problem in the solution. In this paper, an improved fractional-order Tikhonov regularization method is proposed. By modifying the singular values of the structural matrix with different magnitudes, this method addresses the "over-smoothing" phenomenon inherent in standard regularization methods. Numerical examples demonstrate that the improved fractional-order Tikhonov regularization method has advantages over the standard Tikhonov regularization and fractional-order Tikhonov regularization methods in terms of accuracy and stability. A local deck structure model of the ship was established, and multiple working conditions were designed for inversion verification. The calculation results show that the identification error of the improved fractional-order Tikhonov regularization method is reduced by more than 50 % compared to the standard Tikhonov regularization method and the fractional-order Tikhonov regularization method.]]></description>
      <pubDate>Mon, 13 Oct 2025 13:53:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604985</guid>
    </item>
    <item>
      <title>Thermodynamic and adhesive behaviors of high-viscosity asphalt binder (HVAB) subject to water erosion</title>
      <link>https://trid.trb.org/View/2528812</link>
      <description><![CDATA[Moisture acts as a typical erosive medium for asphalt pavement during its service life, and the perception of the influence of water on the thermodynamic and adhesive properties of asphalt binder is nowadays not yet thorough enough. The purpose of this study is to investigate the effect of water on the thermodynamic parameters and adhesive properties of high-viscosity asphalt binder (HVAB). To this end, the HVAB and HVAB-aggregate composite were first prepared and subjected to water immersion test under different conditions, and then contact angle test coupled with surface free energy (SFE) theory, as well as mechanical pull-out test, was employed to explore the variations of SFE parameters and adhesion features of HVAB at aqueous circumstance. Further, Pearson correlation analysis and multiple regression analysis were performed to analyze the correlation between SFE parameters of HVAB and its adhesive properties under water erosion, and molecular simulation was executed to reveal the mechanism of aqueous influence on the adhesive properties of HVAB. Results showed that the total- and dispersive-SFE of HVAB and the interfacial mechanical strength of HVAB-aggregate system exhibited a gradually reduced variation rule with the growing immersing time and temperature, and the effect of prolonging water-soaking durations on them was chiefly concentrated in the early soaking stage of 0–6 days. There was a favorable linear relationship between the total- and dispersive-SFE of HVAB and the mechanical tensile strength between HVAB and aggregate, with the Pearson correlation coefficients exceeding 0.9000. The introduction of water impelled the HVAB component distribution moving away from aggregate surface and enhanced the aggregation magnitude of asphaltene (As) self-aggregation and styrene-butadiene-styrene (SBS) modifier aggregation around As, thus altering the interphase bonding state and weakening the interfacial adhesion performance.]]></description>
      <pubDate>Thu, 01 May 2025 09:37:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2528812</guid>
    </item>
    <item>
      <title>Assessing the Safety of Cyclist–Pedestrian Interactions in Seasonal Pedestrian Streets Using Computer Vision Techniques</title>
      <link>https://trid.trb.org/View/2529913</link>
      <description><![CDATA[Pedestrian streets, also known as streets closed to motorized traffic, serve to promote active modes of transportation. This concept offers the potential to enhance safety for the most vulnerable road users while concurrently reducing air pollution. The present study aims to evaluate the safety of interactions between pedestrians and cyclists, focusing on three pedestrian streets within the city of Montreal. Video data was collected during the day in the summer of 2021. Following camera calibration, a total of 80?h of data was analyzed. Each road user detected and tracked was categorized as either a “pedestrian” or “cyclist”. The analysis involves the computation of indicators for individual cyclists (speed and acceleration) and for their interactions with pedestrians (distance and time to collision [TTC]). Two multivariate regression models were estimated to analyze the relationship between TTC or the cyclist speed as the dependent variables and several other factors. The findings from the safety analysis reveal a discernible variation in safety indicator values between distinct sites, even those situated on the same thoroughfare, independent of regulatory measures. The statistical analysis indicates that elevated TTC values correspond to high acceleration and increased distances between pedestrians and cyclists. Moreover, high TTC values are associated negatively with the density of pedestrians within the camera’s field of view. In contrast, concerning speed, high values are linked to low TTC and distances, together with elevated acceleration values.]]></description>
      <pubDate>Fri, 28 Mar 2025 09:10:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2529913</guid>
    </item>
    <item>
      <title>Shopping Centre Traffic Impact: A Traffic Forecasting Model for Medium Sized Towns in Italy</title>
      <link>https://trid.trb.org/View/2264032</link>
      <description><![CDATA[This paper reports the results of a research project conducted to predict retail demand and evaluate its impact on the transportation system. Similar studies found in the "Guidelines for Traffic Impact Assessment" (edited by IHT), the Planning Policy Guidelines (PPG 6 and PPG 13) published in Great Britain, and the TRIP GENERATION manual edited by ITE in the United States of America evaluate the number of consumers attracted based on retail size. The approach differs in that the authors evaluate consumer behaviour at shop destination by means of questionnaires then apply statistical methods such as multiple correspondence and cluster analysis to define trip generation and retail choice behavioural characteristics and rank consumers accordingly; the authors then apply variables to the different rankings and build algorithms based only on shop characteristics (size, location, accessibility, etc.) which are then calibrated by multiple regression to define the relationship between the estimated number of consumers and real consumer flow. Questionnaires were collected at six large shopping malls in the metropolitan areas of Cagliari and Sassari. Further studies are also being conducted along the same line in another 14 urban areas in Italy, where the authors are investigating different size shops (hypermarkets, supermarkets, corner shops).]]></description>
      <pubDate>Mon, 24 Feb 2025 14:46:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264032</guid>
    </item>
    <item>
      <title>Comparative analysis of pedestrian volume models: Agent-based models, machine learning methods and multiple regression analysis</title>
      <link>https://trid.trb.org/View/2487829</link>
      <description><![CDATA[Pedestrian flow distributions can inform planning for walkability and improve understanding of factors that influence pedestrian activity. However, detailed data is rarely available so pedestrian volume models, commonly relying on the Space Syntax framework, are often utilized to predict pedestrian volumes. This study compares the performance and dominant variables of three modelling families – multiple regression analyses, machine learning models, and agent-based models – in Tel Aviv-Yafo, Israel. Using 247 flow observations, optimal models from each family were fitted and validated for 3 separate areas that differ in their urban growth and morphological characteristics, as well for the whole city. Results showed that ensemble-based machine learning models were best for city-wide predictions while agent-based models had an advantage at the local scale of neighborhoods – especially in neighborhoods that did not develop in a self-organized process. Regression analyses fell short for all areas, even when using principal component analysis to reduce multicollinearity and overfitting. These differences are attributed to the relative influence of cognitive-behavioral and structural factors on pedestrian flows: agent-based models outperform statistical models in individual areas, where behavior is captured more accurately using a small set of cognitive-behavioral parameters. Statistical models are dominant in the city-wide context, where structural variables can predict aggregate patterns. This is crucially important when evaluating the distribution of pedestrians in a planned urban environment. Overall, the results indicate that stepwise regression are not sufficient for pedestrian volume modelling, that agent-based models better capture complex interactions between independent variables, and that machine learning models have a strong potential for city-wide pedestrian volume modelling.]]></description>
      <pubDate>Tue, 18 Feb 2025 10:56:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487829</guid>
    </item>
    <item>
      <title>Development and verification of rutting prediction model based on variable frequency load test</title>
      <link>https://trid.trb.org/View/2487690</link>
      <description><![CDATA[An innovative rutting test method was developed based on the existing rutting prediction model to improve the rutting prediction model used in the asphalt pavements. The loading mould was improved based on test requirements and equipment characteristics, and small-scale and full-scale variable frequency load rutting tests were conducted. Nonlinear multiple regression analysis was conducted on the test results using numerical analysis software, converting driving speed into loading frequency and incorporating it into the rutting prediction model. This resulted in a permanent deformation prediction model for asphalt mixtures, incorporating factors such as temperature, pressure, loading cycles, loading frequency, and pavement thickness. Fourteen highways were selected as validation sections. The validation results showed that the average error of the prediction model established in this study was 15.48%, compared to 24.42% and 27.94% for the other two models. For different grades of highways, the load frequency parameter can distinguish the rutting conditions under different driving speeds, making the rutting prediction model established in this study more accurate compared to measured values.]]></description>
      <pubDate>Mon, 17 Feb 2025 17:09:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487690</guid>
    </item>
    <item>
      <title>Bridge damage location and quantification under the moving vehicle loads based on deep learning multi-objective regression</title>
      <link>https://trid.trb.org/View/2426701</link>
      <description><![CDATA[The extraction of damage-sensitive features based on deep learning for the vibration response caused by vehicle-bridge interaction (VBI) has become a mainstream method in damage identification. However, most studies remain at the stage of qualitative damage assessment, not achieving precise numerical quantification of damage. They also typically involve multiple frequent runs using only a single dedicated vehicle. In this study, utilizing a deep neural network designed for multi-objective regression tasks, a novel method for bridge damage localization and quantification is presented with a multi-objective regressor designed to create a direct mapping between the curve of bridge deflection in time domain and the damage information matrix. The final quantitative damage values are obtained through statistical analysis. The method is evaluated on a simulated data set generated from VBI simulation using various 3D heavy vehicle models. The results demonstrate that this method can accurately locate and quantify damage even with unseen vehicle load conditions, damage scenarios, and measurement noise. The structure and depth affect the effectiveness of extracting damage-sensitive features from time-domain deflection during training. The study shows potential for real-time damage identification under normal operating conditions of real bridges in the rapid development of intelligent transportation systems.]]></description>
      <pubDate>Thu, 17 Oct 2024 11:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2426701</guid>
    </item>
    <item>
      <title>A state-of-the-art review of intelligent compaction measurement values (ICMVs) for subgrade and pavement: Advances and challenges</title>
      <link>https://trid.trb.org/View/2399529</link>
      <description><![CDATA[Compaction construction is one of the most critical factors affecting road performance and durability. To improve the construction quality and service life of road, intelligent compaction (IC) technology has received continuous attention. In contrast to traditional compaction construction, IC improves the compaction quality and uniformity of each road layer through real-time monitoring and adjustment of the compaction process. Intelligent compaction measurement values (ICMVs) were proposed to provide quantitative indexes for compaction quality monitoring and basis for dynamic regulation of compaction conditions. However, the current ICMVs have problems such as low accuracy and stability, and poor applicability under different working conditions, which limit the application of IC technology. To address these shortcomings, this paper conducts a state-of-the-art review of ICMVs, outlining the past research achievements and discussing future development directions. In this paper, the vibrating dynamical models of compaction was firstly summarized and the future research directions are proposed. Next, the existing calculation models of ICMVs are systematically investigated and the significant factors that affected ICVMs calculations are summarized. Solutions to eliminating influences from those factors are discussed in further, e.g., by a combined effort in improving calculations models for ICMVs and advancing the multiple regression analysis method.]]></description>
      <pubDate>Tue, 23 Jul 2024 17:41:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2399529</guid>
    </item>
    <item>
      <title>Damage evolution in asphalt mixtures based on in-situ CT scanning</title>
      <link>https://trid.trb.org/View/2399452</link>
      <description><![CDATA[A novel approach utilizing in-situ Computed Tomography (CT) scanning and digital image processing techniques was developed to overcome the limitations of traditional CT image analysis in studying microcrack evolution in asphalt mixtures. Two types of graded asphalt mixtures (continuous gradation/AC and intermittent gradation/SMA) were scanned in-situ to capture internal structural changes. Digital image processing distinguished primary voids from cracks and extracted crack evolution. Multivariate regression analysis linked crack parameters to compressive strain. The study found that crack evolution during uniaxial compression in asphalt mixtures can be divided into densification, linear elastic deformation, and nonlinear failure stages. As load increases, the number, volume, and void percentage occupied by cracks rise, peak, and then decrease due to merging and engulfing after the ultimate load. The skeletal structure of cracks was obtained, and a damage evolution equation was established, linking crack parameters to damage evolution. Crack tortuosity was used to characterize the resistance effect of different gradations on crack propagation. This methodology provides new insights and technical support for studying damage evolution and understanding the failure mechanisms of asphalt mixtures.]]></description>
      <pubDate>Fri, 19 Jul 2024 11:49:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2399452</guid>
    </item>
    <item>
      <title>Variability in airborne noise emissions of container ships approaching ports</title>
      <link>https://trid.trb.org/View/2396063</link>
      <description><![CDATA[Container ships emit airborne noise that can vary significantly due to diverse vessel characteristics and operating conditions. This study aims to investigate the factors influencing noise emission from container ships moving in ports to enhance the authors' understanding and improve noise prediction models. Using a dataset comprising long-term sound pressure level measurements, video recordings, and weather station data, a multiple regression analysis was conducted to assess the impact of static (ship-specific) and dynamic (pass-by specific) variables on noise emission. Static variables included ship dimensions and age, while dynamic variables encompassed distance from the microphone, speed, and draught. A k-means unsupervised clustering analysis was performed using 1/3rd octave band spectra to identify subcategories of container carriers based on sound emission characteristics. Significant correlations between emissions and both static and dynamic variables were found. The k-means clustering analysis yielded distinct subcategories of container carriers based on their sound emission profiles. This study highlights the importance of considering various factors, including ship characteristics and operating conditions, when assessing noise emission from container ships. By better understanding the factors contributing to noise emission, the authors can effectively mitigate noise pollution and minimize the impact on surrounding communities.]]></description>
      <pubDate>Thu, 18 Jul 2024 10:49:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2396063</guid>
    </item>
    <item>
      <title>Effect of Logistics Performance Index on Human Development Index: An Application to Logistics Sector</title>
      <link>https://trid.trb.org/View/2365031</link>
      <description><![CDATA[Human Development Index (HDI) has been subject to a lot of criticism over time, with the idea that it does not fully reflect human development, and has been complemented with different indicators in various studies. Despite its importance, especially considering the requirements of the era, logistics has not been considered with HDI and, although there are many synthetic indices that combine different methods (i.e. DEA, MCDM etc.) with HDI, these indices are far from reaching logical results. Logistics is a fundamental activity in meeting all human needs, and therefore should be considered as a measure of countries' human development. Therefore, this study offers a revision of the HDI with special consideration to the logistics performances of countries. It is observed that infrastructure and timeliness indicators from these sub-indicators of Logistics Performance Index (LPI) have a significant effect on the HDI. Using the United Nations' HDI calculation method with the statistically significant indicators, Logistics-HDI (L-HDI) is developed and proposed as a new index. This study argues that L-HDI reflects the human development of countries more appropriately. The L-HDI will also offer better benchmarking than other synthetic indices in its scientifical field.]]></description>
      <pubDate>Fri, 10 May 2024 16:50:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2365031</guid>
    </item>
    <item>
      <title>A hybrid robust SBM-DEA, multiple regression, and MCDM-GIS model for airport site selection: Case study of Sistan and Baluchestan Province, Iran</title>
      <link>https://trid.trb.org/View/2355461</link>
      <description><![CDATA[This study focuses on the strategic decision-making process for selecting airport locations in developing countries, where investment constraint is a significant concern. Recognizing that airport location decisions are influenced by a multitude of factors, often in the absence of comprehensive data and amidst uncertain forecasts, there is a need for a methodology capable of adeptly handling various factors under uncertainty. The goal is to provide solutions that remain feasible and near-optimal, even with some variations in input parameters, ensuring robustness. To this end, this research introduces a decision tool incorporating Slack-Based Measurement in Robust Data Envelopment Analysis (SBM-RDEA). The Slack-based method is beneficial in cases with multiple inputs and outputs where their relationships are not strictly proportional, as in the airport location selection problem. DEA, in conjunction with Multiple Regression analysis, leverages available data on the attributes of operational regional airports to compute the significance of these criteria in shaping the success factors of these facilities. Furthermore, these weightings can be employed to evaluate the suitability of airport locations currently under development and assess their potential for contributing to regional development. Additionally, to address the uncertainty of input data, a Robust optimization approach is employed to ensure a reliable response for this strategic decision-making process. This weighting framework is utilized twice within the research methodology: first, to assess the potential of different counties in the state for hosting a new regional airport, and second, to identify the optimal location for the airport in an MCDM-GIS analysis. This methodology has been applied to a case study in Sistan and Baluchistan, Iran. The result of the proposed method has been compared with one of the most common existing methodologies. This method identifies Zahedan County as having the highest potential and selects the location (29.48° N, 60.90° E), in the east of Zahedan city, as the optimal site for airport construction. Validation results confirm the efficacy of this solution.]]></description>
      <pubDate>Tue, 30 Apr 2024 09:08:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2355461</guid>
    </item>
    <item>
      <title>Workability and strength optimisation of concrete utilising artificial sand blended with natural sand for road pavement</title>
      <link>https://trid.trb.org/View/2310387</link>
      <description><![CDATA[Workability and strength optimisation of concrete utilising artificial sand blended with natural sand was carried out in this paper for road pavement. There were 18 concrete mixtures with their different compositions were designed and tested. Firstly, three parameters, which were slump, compressive strength, and flexural strength of the concretes, were measured from experimental test and then evaluated. The combination between artificial sand and natural sand in marking concrete was observed to possibly improve all three parameters. Secondly, three analytical tools, including Taguchi technique (TT), multiple regression technique (MRT) and artificial neural network (ANN), were employed to predict the optimal slump, compressive strength, and flexural strength of the tested concretes based on the experimental results. The MRT performed the highest accuracy while the ANN model performed the lowest. Besides, the artificial sand to natural sand ratio was the most influencing factor on the slump and compressive strength of the concretes, whereas the water to cement ratio was the most influencing factor on the flexural strength. The artificial sand could replace the natural sand with ratio between them in range of 2.0–2.34 for achieving the best concrete performances.]]></description>
      <pubDate>Mon, 01 Apr 2024 16:57:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2310387</guid>
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