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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Analysis of Clifton Suspension Bridge</title>
      <link>https://trid.trb.org/View/2159552</link>
      <description><![CDATA[The paper describes the fundamentals of the analysis of Clifton Suspension Bridge to understand its structural behaviour under traffic loads and to evolve its repair strategy.]]></description>
      <pubDate>Sat, 07 Mar 2026 16:05:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2159552</guid>
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      <title>Safety Assessment of New England Roadways During the COVID-19 Pandemic</title>
      <link>https://trid.trb.org/View/2669639</link>
      <description><![CDATA[Safety assessment of roadway facilities is a critical task to maintain the system operational efficiency of transportation infrastructure. The comprehensive stay-at-home orders implemented in response to the COVID-19 pandemic have resulted in massive reductions in traffic volumes, especially on major highways. Motorists have responded to these greatly reduced volumes by increasing their travel speeds; the result of this behavioral response has been an increase in the rate and incidence of fatal crashes. There is a clear need to analyze speeding during and after the duration of stay-at-home orders. In addition, the rate of severe injury and fatal crashes continued to remain high in 2021 and 2022, even when the traffic volume returned to the pre-pandemic condition. This project employed an innovative approach to use traditional data archived from permanent count stations, as well as new data sources (i.e., probe data) to develop models to better understand the impact of pandemic on New England roadways. Particularly, this research developed models to analyze speeding during and after COVID-19 stay-at-home orders in Maine and Connecticut. This research also developed models to better understand the impact of pandemic on crashes in 2021, and 2022, and explore if any factors other than speed, also impacted the increase in rate of severe and fatal crashes in years after the stay-at-home order in Maine.]]></description>
      <pubDate>Mon, 02 Mar 2026 13:24:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669639</guid>
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      <title>Temporary recurring closures and changing mobility patterns: A quasi-experimental study of the impacts of London’s Covid-19 school streets on travel to school</title>
      <link>https://trid.trb.org/View/2625915</link>
      <description><![CDATA[With over 500 schemes installed since March 2020, School Streets have been one of the most significant street experiments conducted in London. As temporary and recurring closures to streets these schemes provide a novel typology of street experiment which may be applicable to other contexts. In research on urban street experiments, questions have been raised about the extent to which such schemes can contribute to wider mobility transitions. Through a quasi-experimental analysis of school travel data, this study seeks to assess this question in relation to London’s School Streets schemes. It asks to what extent have these schemes reduced the use of private motor vehicles and increased the uptake of active modes of travel to school. The analysis finds positive but modest results on this count, with some evidence that School Streets have helped to prevent a ‘car-based recovery’ from Covid-19 in London. It goes on to reflect on the implications of this for the wider study of the impacts of street experiments on urban mobility.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625915</guid>
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    <item>
      <title>How to Integrate Electric Vehicle Charging Policy with Wider Sustainable Travel Initiatives – a Case Study of Cambridge</title>
      <link>https://trid.trb.org/View/2647828</link>
      <description><![CDATA[This paper investigates the varying cross-mode effects of electric vehicles (EVs) charging policy through a choice experiment among car commuters in Greater Cambridge. The design of the choice experiment aims to reveal the interdependences between EV charging policy and the wider sustainable travel goals of promoting public transport and reducing single-occupancy car commuting. A total of three travel choice settings involving 12 policy interventions are developed. Response to policy interventions and the associated socio-economic attributes are collected through an online survey (N = 170). Key socio-demographic predictors of policy acceptance, such as employment status, workplace location, and residential proximity to city, are identified through Latent Class Analysis (LCA). Novel policy packages that combine EV charging policy with other sustainable travel policies are proposed, which are tailored to specific traveller groups and stages of local EV uptake. The proposed choice experiment method coupled with LCA for capturing the different sensitivities to policies among heterogeneous population represent a new, practical approach for supporting place/population-targeted policy making for promoting sustainable travel.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647828</guid>
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    <item>
      <title>Resistance to cycling infrastructure : Lessons from The Plain roundabout in Oxford</title>
      <link>https://trid.trb.org/View/2633904</link>
      <description><![CDATA[Improving cycling infrastructure for safety, comfort, and experience enables cycling as a sustainable alternative to short car trips and last-mile public transport. However, policy interventions in spatially constrained areas face active and passive resistance to reprioritising active mobility. This study examines barriers to improving The Plain Roundabout in Oxford, UK, a ‘safety hot spot’ and cyclist deterrent, highlighting how material, institutional, political, and discursive resistance prevent substantial change towards a cycling-friendly redesign. Based on thematic and discourse analysis of stakeholder interviews, cyclist testimonials, and planning and policy materials, this study reveals that infrastructural resistance is embedded in existing governance practices. Four main themes with 11 sub-themes of resistance to experimentation were identified. Materiality concerns included physical layout, vehicle sizes, design details, and geographical constraints. Institutionalisation reflected on tools and data availability, built-in skillsets, and experimentation culture. Agency and interests shaped stakeholder involvement, resources and impact. Discourses reinforced norms of unfettered motorised movement, freedom of choice, and the perceived need for a compromise. Findings indicate that while experimentation is seen as a catalyst for sustainable mobility transitions, institutional inertia, a legacy of motorised transport planning, and the uneven agency of stakeholders limit transformation. Overall, safety, though touted as important, is insufficient to justify radical change without challenging modernist values of speed and motorised transport flows. While The Plain Roundabout remains controversial to this day, the proposed framework provides insights into how barriers to cycling infrastructure can be systematically identified and addressed, offering an analytical tool for policymakers and practitioners.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633904</guid>
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    <item>
      <title>3.19 Detection and Monitoring of Material Aging and Structural Deterioration Using Electromagnetic and Mechanical Sensors with Virtual Reality and Machine Learning Modeling</title>
      <link>https://trid.trb.org/View/2666685</link>
      <description><![CDATA[The research problem we are trying to solve is the detection and monitoring of aging civil infrastructure components and systems in New England by using visual information and subsurface images in a virtual reality (VR) environment for data visualization and machine learning (ML) for data interpretation. The overall research objective is to study the detection and monitoring problem of aging civil infrastructure components and systems in New England by using visual information and subsurface images in a virtual reality (VR) environment for data visualization and machine learning (ML) for data interpretation. New ground-penetrating radar (GPR) B-scan image datasets have been created for the nondestructive inspection and structural health monitoring of a highway bridge in Massachusetts. New x-ray diffraction (XRD) data have been developed for material aging study. We monitored a reinforced concrete (RC) highway bridge (I-495, Chelmsford, MA) by collecting high-frequency GPR B-scan images for about two year on 186 days. We analyzed the material samples collected from the RC highway bridge for material aging characterization. We developed a VR chamber for training transportation professionals. We proposed and applied a new Deep Learning model (Power2Net) to predict steel rebar corrosion in GPR B-scab images without using any environmental data.]]></description>
      <pubDate>Thu, 19 Feb 2026 17:04:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666685</guid>
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    <item>
      <title>An Unequipped Vehicle State Estimation Algorithm to Augment the Person-Based Control in Low Connected Vehicle Penetration Rates via Deep Reinforcement Learning</title>
      <link>https://trid.trb.org/View/2658725</link>
      <description><![CDATA[This paper develops an Estimation of Unequipped Vehicle with Occupancy (EUVO) algorithm to predict departure times and occupancy levels for vehicles across different approaching lanes in mixed traffic environments containing both Connected Vehicles (CVs) and Unequipped Vehicles (UVs). The algorithm integrates multi-source data from CVs, loop detectors and roadside cameras. After processing by EUVO algorithm, a Person-based Deep Deterministic Policy Gradient (PB-DDPG) algorithm is proposed to improve the performance of person-based traffic control under low CV penetration rates and reduce computational complexity using Deep Reinforcement Learning (DRL). The integration of EUVO and PB-DDPG algorithms reconstructs the states of both CVs and UVs, combining vehicle occupancy levels and excess waiting time as input data. Through a trial-and-error training process, it derives optimal signal timing solutions with flexible actions and person-based rewards. The method remains effective even at low CV penetration rates, ranging from 0% to 20%. The algorithm is evaluated across two study sites in Hull and Birmingham, U.K., under various traffic scenarios. Results show that compared with the vehicle-based DQTSC-M model, PB-DDPG reduces average person delay and the number of person stops by approximately 18.3% and 19.6%, respectively. It also exhibits faster convergence and more stable performance than the Double Deep Q Network (DDQN) model. In addition, the EUVO algorithm significantly improves the performance of PB-DDPG in reducing average person delay and stops under the following conditions: CV penetration rates below 90%, UV position estimation errors within 6 meters, loop detection errors below 50%, loop detection latency within 2 seconds, and camera occupancy detection errors below 30%.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658725</guid>
    </item>
    <item>
      <title>Implementation of UHPC Technology into the New England Construction Industry (TIDC Project 2.14)</title>
      <link>https://trid.trb.org/View/2666583</link>
      <description><![CDATA[This research focuses on the long-term stability and durability of newly developed, resource-efficient non-proprietary ultra-high performance concrete (UHPC), advancing material design and development to support field implementation in structural transportation projects across New England. The study drives innovation in durability testing and results analysis, addressing critical gaps in performance assessment to facilitate the use of non-proprietary UHPC in bridge components and connections. This work builds upon the foundational efforts of previous US DOT UTC-TIDC Project 2.5: “Development and Testing of High/Ultra-High Early Strength Concrete for Durable Bridge Components and Connections."]]></description>
      <pubDate>Wed, 18 Feb 2026 13:22:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666583</guid>
    </item>
    <item>
      <title>Incorporation of Pollinator Plantings to Enhance Ecosystem Functions and Durability of Transportation Right-of-Way Infrastructure</title>
      <link>https://trid.trb.org/View/2666584</link>
      <description><![CDATA[Monarch butterflies, ground-nesting bees and many other crucial pollinators depend on early succession grassland habitats for survival. In New England these habitats have been disappearing as agricultural lands are developed or allowed to mature into forest. Many of the native pollinator species are threatened or endangered. Landscape management intensity has decreased but mowed turfgrass remains the default ground cover. Departments of transportation and the landscape installation companies they contract with are experienced at establishing cool-season turfgrasses using hydroseeding, and at establishing perennial forbs from container-grown transplants. However, they have little experience establishing native grasses and forbs from seed, and the lengthy pre- and post-seeding maintenance protocols recommended to minimize weed intrusion do not fit with existing project timelines. Most of the available resources on roadside use of native plants do not include information on the pollinator benefits of the recommended species. This project addressed these knowledge gaps through three inter-connected tasks. The project determined that broadcast seeding into plantable soil was the best method for establishing native forbs on roadsides, identified twelve species that are good candidates for inclusion in pollinator-friendly seed mixes for roadsides in Rhode Island and 14 species that should not be used, and created a guide to pollinator-friendly native woody plants for use by landscape architects in New England]]></description>
      <pubDate>Tue, 10 Feb 2026 09:47:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666584</guid>
    </item>
    <item>
      <title>Exploring transportation mode choices and air quality concerns: Insights from a diverse urban sample</title>
      <link>https://trid.trb.org/View/2616229</link>
      <description><![CDATA[We surveyed 1,936 participants in Bradford, England, to examine patterns of travel modes for commuting, school travel, and general transportation, and how these patterns differ based on attitudes toward air quality. Participants rated air quality, their level of concern, and the importance of improving it. Logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs) to assess associations between air quality concerns and transportation mode choices. Our findings revealed a significant reliance on unsustainable travel modes—54% of participants reported exclusively using petrol/diesel/van vehicles for commuting, and 75% for traveling around town. In contrast, 50% of participants used sustainable travel modes (public transit, active transportation, or electric vehicles) for school trips. Active travel was more common among White British participants, while South Asian participants were more likely to rely on unsustainable vehicles. Participants concerned about air quality had significantly lower odds of using petrol/diesel/van vehicles for commuting (OR = 0.72, 95% CI: 0.53–1.01), school trips (OR = 0.75, 95% CI: 0.54–1.01), and traveling around town (OR = 0.70, 95% CI: 0.52–0.94) compared to those unconcerned. Additionally, concerned individuals were more likely to use sustainable transportation, with increased odds of choosing active travel modes for commuting (OR = 1.46, 95% CI: 1.04–2.07) and traveling around town (OR = 1.95, 95% CI: 1.46–2.60). These findings suggest that air quality concerns independently influence travel behavior, encouraging the adoption of sustainable transport options. Future research should explore how changing attitudes shape long-term transportation choices and policy interventions aimed at promoting environmentally friendly mobility.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:53:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616229</guid>
    </item>
    <item>
      <title>Using Machine Learning to Investigate User Behaviour of Tyne and Wear Metro</title>
      <link>https://trid.trb.org/View/2639385</link>
      <description><![CDATA[This study investigates the factors influencing passenger satisfaction on the Tyne and Wear Metro system, a key public transportation network in North East England. The research explores how various service attributes, such as accessibility, reliability, and comfort, contribute to overall user satisfaction. Data from 850 passengers were collected through a structured questionnaire and analysed using advanced machine learning (ML) and deep learning (DL) techniques, including Random Forest, Gradient Boosting, and Neural Networks. This study addresses a gap in the literature by applying a multi-model ML/DL approach to a hybrid light rail system, incorporating sustainability-oriented variables such as environmental concern into the analysis of passenger satisfaction. The Random Forest model demonstrated high predictive accuracy, achieving Area Under the Curve scores of 0.93 and 0.91 for specific user classes. The findings highlight critical areas for improvement, particularly in service reliability and comfort, while also underscoring the potential of the Metro to increase ridership and reduce emissions through enhanced service quality. The findings can guide operators such as Nexus in prioritising service improvements, particularly in reliability and comfort, to boost ridership and support the region’s Net Zero targets. This research provides valuable insights for transit authorities aiming to enhance user satisfaction and promote sustainable urban mobility.]]></description>
      <pubDate>Thu, 05 Feb 2026 09:16:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639385</guid>
    </item>
    <item>
      <title>Urban heat island effect: Examining spatial patterns of socio-demographic inequalities in Greater London</title>
      <link>https://trid.trb.org/View/2632912</link>
      <description><![CDATA[This study examines how Urban Heat Island effects interact with socio-demographic disparities in Greater London, UK, proposing a spatial method to help local governments prioritize heat mitigation. Measuring high-resolution decennial satellite-derived land surface temperature at the street level linked to detailed Census data, the authors find that young children, ethnic minorities (especially Asian and Black populations), and lower-income groups experience significantly higher surface temperatures – up to 4°C hotter – than wealthier, predominantly White populations. A space syntax-based street network analysis reveals reduced pedestrian movement potential in temperature hotspots, particularly in boroughs with larger ethnic minority populations, suggesting that heat-exposed streets may further limit mobility for vulnerable communities. Accessibility metrics show a potential 6–9% decline in affected areas, which can exacerbate socio-spatial inequalities. The authors propose integrating demographic data with fine-scale land surface temperature measurements to identify high-risk areas for targeted interventions, such as tree planting, water features, and shading. This method enables local authorities to address heat disparities without complex modelling, supporting targeted, practical interventions with the potential to reduce climate vulnerability while enhancing walkability and public health.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632912</guid>
    </item>
    <item>
      <title>Control Function Approach for Addressing Endogeneity in Transport Models: A Case Study on the London–Amsterdam Route</title>
      <link>https://trid.trb.org/View/2633868</link>
      <description><![CDATA[Endogeneity is a key empirical challenge in transportation modeling, which may lead to inconsistent estimates and biased policy decisions. This paper investigates the sources of endogeneity and focuses on tackling this issue for a discrete choice model analyzing the multimodal London–Amsterdam route, where air transport and high-speed rail (HSR) compete. Contrary to previous literature, we found no evidence of endogeneity in service frequency for the London–Amsterdam market. This could be attributed to market-specific features, such as feeding considerations, slot retention dynamics, and the congestion of the HSR network, which constrains capacity expansion opportunities. Conversely, we observed that fare introduced endogeneity into the model. To address this issue, we applied the control function approach and proposed two novel instruments: the fare for similar markets and the price of power sources. These instruments proved to be effective in correcting for endogeneity by increasing model performance. We also discuss the adverse impact of neglecting endogeneity and estimate price and frequency elasticities, ultimately demonstrating the significance of dealing with endogeneity in ensuring the reliability of results in transportation studies and appropriately informing policy decisions.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633868</guid>
    </item>
    <item>
      <title>Slot allocation at capacity-constrained airports: A reform proposal</title>
      <link>https://trid.trb.org/View/2634085</link>
      <description><![CDATA[Slot allocation at congested airports is based upon administrative procedures in accordance with the guidelines of the International Air Transport Association’s (IATA) Worldwide Airports Slot Group. The key principle is the first come, first served rule. As long as airlines do use the slots allotted to them on a regularly basis (the so-called use-it-or-lose-it rule), these will be automatically reallocated to them in the next scheduling period (grandfathering). However, this allocation regime creates a very effective entry barrier for newcomers at slot-constrained airports. Only very few countries, e.g. the UK, permit airlines to trade excess slots on a secondary market. Evidence from these few legal secondary markets – especially at London’s Heathrow Airport – clearly demonstrates that the current slot allocation system has produced substantial scarcity rents to the benefit of the incumbent airlines. The existing literature has focused on the potential anticompetitive effects and antitrust implications of grandfathering and on secondary trading of slots only. Our conceptually oriented paper contributes to this literature by adding a property rights perspective to the analysis and by including airports as a new player in the slot allocation process. Our starting point is that airlines are not legal (though de facto) owners of airport slots which are just a temporarily conferred right-to-use which. Our conceptually oriented analysis will briefly address the antitrust implications of the existing slot allocation regime(s) (Scenario I). Then, we will assess, in a comparative institutional analysis, the potential effect on airline competition of two alternative slot allocation regimes. Scenario II is based on a combination of slot auctions with secondary trading while – as is the case in Scenario I – slot coordinators and airlines remain the only players. Scenario III modifies Scenario II by removing both slot ownership and the role as slot suppliers from slot coordinators which are reassigned to airport operators.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2634085</guid>
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      <title>Automated structural integrity assessment of bridges: a hybrid machine learning and feature-based framework</title>
      <link>https://trid.trb.org/View/2635291</link>
      <description><![CDATA[This study investigates the effectiveness of two advanced analytical methodologies, Sequential Feature Isolation (SFI) and Filtration-Based Structuring (FBS) for large-scale structural assessments, specifically in analyzing critical features of the Silver Jubilee Bridge. Ensuring precise detection and classification of structural components in 3D point cloud data is crucial for effective damage assessment. The SFI method employs successive stages of CANUPO analysis followed by dip angle filtration, whereas the FBS method begins with dip angle filtration before proceeding with CANUPO analysis. A critical aspect of this research is optimizing the Local Neighbor Radius (LNR) for dip angle filtration. By testing LNR values ranging from 0.01m to 0.025m, the study identified 0.01m, paired with an 80-degree dip angle, as the optimal setting, significantly enhancing filtration precision. The SFI and FBS methods effectively reduced the number of brick points by an average of 99% and joint points by 90%, while retaining 28% of crack points crucial for shaping crack configurations. The comparative analysis revealed that the SFI method is suited for projects requiring high precision and detailed feature isolation, whereas the FBS method is better suited for tasks needing a broader retention of structural details. The study underscores the importance of selecting the appropriate method based on specific research objectives and provides clear guidelines for method selection and structural feature analysis. This comprehensive approach enhances the precision and reliability of structural assessments, offering significant contributions to the field of geological and structural analysis.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635291</guid>
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