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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Assessing agglomeration impacts of large-scale transport infrastructure</title>
      <link>https://trid.trb.org/View/2694894</link>
      <description><![CDATA[Large-scale transport infrastructure can influence national patterns of population and employment, and induce agglomeration effects and long-term economic development. However, conventional appraisal methods often do not capture these indirect impacts. This study investigates how transport investments may affect the spatial distribution of population and employment and the resulting agglomeration effects and productivity changes, using East West Rail in England as a case study. We develop an extended land use–transport interaction (LUTI) model with industry-level disaggregation. It integrates a recursive gravity framework with a multimodal transport network and iteratively updates residential and employment distributions in response to accessibility changes. Results reveal that the rail line stimulates significant growth within the region, particularly along its route, but attracts limited inflows from more distant areas. Employment relocation (particularly in retail and service sectors) responds more sensitively to accessibility gains than residential patterns. When we apply our model to the national level (England and Wales), we capture the net benefits of agglomeration and disagglomeration. We find significant productivity gains in the corridor, with an estimated uplift of about 0.7%, and a modest but positive impact at the national scale around 0.02%. The results suggest that part of the corridor-level gain reflects redistribution through displacement from elsewhere rather than wholly additional national output. The analysis can thus contribute to the understanding of granular spatial patterns related to agglomeration and disagglomeration that arise from transport investments.]]></description>
      <pubDate>Tue, 28 Apr 2026 17:06:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694894</guid>
    </item>
    <item>
      <title>Mukara: A Deep Learning Alternative to the Four-Step Travel Demand Model with a Case Study on Interurban Highway Traffic Prediction in the Uk</title>
      <link>https://trid.trb.org/View/2695817</link>
      <description><![CDATA[Accurate traffic volume prediction is essential for managing congestion, improving road safety, mitigating environmental impacts, and supporting long-term transportation planning. The traditional four-step travel demand model (FSM) is a well-established framework, but it relies on static survey data, substantial calibration effort, and simplified behavioural assumptions that may not adequately capture complex travel patterns. In contrast, data-driven models are capable of learning nonlinear relationships from large datasets, yet they are often designed for short-term forecasting and typically do not target the long-term, segment-level volume estimation tasks required for strategic planning. This study proposes Mukara, a deep learning framework that directly approximates the mapping from external socioeconomic and network features to observed traffic volumes on highway trunk road segments. The model is trained on eight years of data from England and Wales and incorporates population, employment, land use, road network characteristics, and points of interest as inputs. Mukara achieves a mean GEH of 50.74, a mean absolute error of 8,989 vehicles per day, and an R2 of 0.583 under random cross-validation, outperforming baseline models and existing studies under comparable settings. Under a more stringent region-based spatial cross-validation scheme, performance remains robust, demonstrating strong spatial transferability. Ablation experiments further demonstrate the robustness of the proposed architecture and reveal the relative importance of different input feature groups for prediction.]]></description>
      <pubDate>Mon, 27 Apr 2026 16:20:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2695817</guid>
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    <item>
      <title>Vehicle choice and use under alternative policy scenarios: What needs to be done to promote electric vehicle uptake and usage</title>
      <link>https://trid.trb.org/View/2642444</link>
      <description><![CDATA[The aim of this study is to evaluate the determinants affecting two interrelated discrete and continuous decisions that households typically make when purchasing a new vehicle, consisting of vehicle type choice and their usage. To this end, we develop a double hurdle model that explicitly accounts for mixed nature of the choices under investigation. The proposed methodological approach is applied to a discrete choice experiment primarily designed to elicit New South Wales (Australia) residents’ preferences for alternative fuelled vehicles. Evidence from the empirical analysis suggests that respondents are more inclined to acquire fuel efficient automobiles relative to passenger vehicles powered by petrol, with battery electric cars being the most preferable purchase option. Nevertheless, the model shows that respondents still prefer driving conventional vehicles longer, with petrol automobiles being the fuel type car associated with the highest kilometres driven. Finally, given the model parameter estimates, this study undertakes a simulation exercise to explore how the New South Wales automobile market will evolve under different policy settings. The modelling predictions suggest that lowering the purchase price of plug-in hybrid-electric and battery electric vehicles below that of all other vehicle fuel types will give rise to more electric vehicles on roads compared to a faster charging time of home stations.]]></description>
      <pubDate>Tue, 17 Mar 2026 09:47:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642444</guid>
    </item>
    <item>
      <title>Perceptions of autonomous vehicles among older adults and people with physical disability</title>
      <link>https://trid.trb.org/View/2630849</link>
      <description><![CDATA[Fully Autonomous Vehicles (AVs) capable of performing all driving functions represent a significant advancement in transportation technology. AVs are increasingly regarded as a promising transport solution for older adults and people with physical disabilities. However, previous literature shows that these groups present low intention to use AVs. This contradiction between potential benefits and low intention to use AVs highlights the need for deeper understanding of the factors influencing intention to use amongst these groups, particularly their attitudes. The Technology Acceptance Model suggests attitudes are shaped by perceived usefulness and perceived ease of use. Yet, in-depth research into these group’s perceptions of AVs remains limited. This study explores the underlying beliefs that shape perceptions of private autonomous vehicles (PAVs), shared autonomous vehicles (SAVs) and autonomous public transport (APT) among older adults (≥65) and people with physical disabilities in New South Wales, Australia. Based on focus groups, supplemented by interviews, this study found that perceptions are shaped not only by beliefs about AVs themselves, but also by beliefs associated with their human-driven counterparts. The introduction of self-driving technology is perceived as enhancing private transport, making PAVs more useful and easier to use than human-driven private cars. In the shared and public transport contexts, the introduction of self-driving technology is perceived as replacing human elements – assistance, information, reassurance, and supervision – making SAVs and APT less useful and more difficult to use than human-driven taxis and buses. Based on these findings, this study provides recommendations to promote AV acceptance among older adults and people with physical disabilities.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:56:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630849</guid>
    </item>
    <item>
      <title>Estimating health benefits of a peri-urban railway: A quantitative health impact assessment focused on diabetes</title>
      <link>https://trid.trb.org/View/2663680</link>
      <description><![CDATA[Transport is a significant determinant of health. This study investigates the potential health benefits of introducing a commuter railway line to a rapidly growing peri-urban area in South West Sydney. The authors focus on diabetes, a condition with high regional prevalence. The rationale is to assess how infrastructure development can influence public health outcomes, particularly in areas with low active transport rates and high car dependency. A quantitative Health Impact Assessment (HIA) was conducted using the DYNAMO-HIA modelling tool. This tool utilizes a partial micro-simulation multi-state model to estimate changes in disease burden and life expectancy. Two scenarios were simulated over an 11-year period (2019–2029): a baseline scenario with no intervention and an intervention scenario involving the introduction of a commuter rail line. The intervention was expected to increase walking and reduce diabetes burden. Data inputs included population demographics, walking prevalence, and diabetes incidence and mortality. The intervention scenario showed an annual reduction in diabetes incidence and prevalence of approximately 0.02%. Life expectancy increased by about one month, and a total of 222 Disability-Adjusted Life Years (DALYs) were gained due to reduced diabetes burden. Although these numbers may appear modest, they represent meaningful health gains for a small and rapidly growing community, underscoring the significant impact that even incremental improvements can achieve at this scale. The study demonstrates the feasibility of applying predictive modelling tools in HIA for infrastructure planning. It highlights the importance of integrating health considerations into urban development, especially in peri-urban contexts. Although limited to one disease and intervention, the findings support evidence-based planning for healthier communities.]]></description>
      <pubDate>Wed, 25 Feb 2026 08:53:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663680</guid>
    </item>
    <item>
      <title>Public support for new transport policies: Exploring the effects of misinformation and disinformation</title>
      <link>https://trid.trb.org/View/2647834</link>
      <description><![CDATA[In their analysis of controversial policies, van Wee et al. (2023) called for more ex-post analyses of new transport policies and that is what is offered here with a case study of the nature and effects of online mis(dis)information on the new national default speed limit of 20mph in Wales. High levels of opposition were observed in Wales during and post-launch. Possible underlying reasons for these protests are discussed. The protests had a dramatic effect, with the speed limit change becoming an unexpected ‘cause-celebre’ on both sides of the political divide. The findings of one exploratory case study should be interpreted with caution, however, in the context of rising international trends of the use of online persuasion techniques by campaigners and politicians, it is hoped that the in-depth exploration of what happened in Wales and why it happened is helpful. If the Wales 20mph policy launch is indicative of the power of online activism in potentially de-stabilising new transport initiatives, then policy makers and delivery professionals in future need to plan accordingly. Suggestions are made for policies that would help mitigate the effects of the type of mis/disinformation seen here.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:32:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647834</guid>
    </item>
    <item>
      <title>Exploring community public budget preferences for transport electrification: Evidence from a contingent budget allocation study in New South Wales, Australia</title>
      <link>https://trid.trb.org/View/2652610</link>
      <description><![CDATA[The allocation of funds across government functions often reflects political priorities that do not align with public expectations. This paper employs a contingent allocation method in which respondents are required to distribute a fixed budget across 12 different potential project types. The main goal of this research is to elicit community priorities for public spending with a specific focus on transport electrification. Based on a sample of 727 residents from the state of New South Wales (NSW), Australia, our findings indicate that transport electrification is not regarded as a majority priority relative to initiatives aimed at improving healthcare, utilities, roads and education. Further, we find that the prioritisation of decarbonising transport services is heavily influenced by households' financial circumstances: individuals experiencing financial distress are less inclined to allocate substantial public resources to curbing transport-related emissions. These results suggest that policy actions should be designed to ensure that climate transition measures do not disproportionately burden financially vulnerable communities.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:32:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652610</guid>
    </item>
    <item>
      <title>Fair assignment of urban–rural electric vehicle shared charging using queuing theory</title>
      <link>https://trid.trb.org/View/2633897</link>
      <description><![CDATA[The widespread adoption of electric vehicles (EVs) is crucial for decarbonising transportation and achieving global net-zero goals. However, a significant challenge in this transition is ensuring equitable access to charging infrastructure, particularly when addressing the simultaneous charging needs of urban residents and rural visitors in urban areas. This is a critical aspect often overlooked in existing literature. This study formulates the problem of implementing a charging system for urban areas that can support both urban and rural users as a multi-objective integer linear programming (ILP) model. This approach uniquely achieves fairness by reducing congestion in urban charging systems to ensure sufficient charging capacity for rural residents visiting the area. Specifically, the expected mean waiting time for all users is minimised. Concurrently, the travel distance for urban residents to their assigned charging stations is also minimised, thereby ensuring sufficient charging capacity for rural residents visiting the area. An ILP solver was employed to evaluate the proposed model across various problem instances, including a detailed case study of Cardiff city, UK. Results demonstrate the significant advantages of this assignment model: for a simulated scenario with 36 charging stations in Cardiff’s urban centre, the model reduced the mean waiting time by approximately 7 min per user (from 16.6 to 9.6 min) and decreased the average travel distance for urban users by 2.25km (from 3.6 to 1.35km) compared to a baseline approach. Further experiments across different charging station densities consistently showed that the optimisation model reduced mean waiting times by up to 12.8 min and average travel distances by up to 3.3km. This research provides a robust, data-driven framework that enables more equitable and efficient EV charging infrastructure planning, facilitating a truly inclusive transition to electric vehicles for both urban and rural communities.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633897</guid>
    </item>
    <item>
      <title>Classification of road transport earth retaining structures based on improved ConvNeXt V2</title>
      <link>https://trid.trb.org/View/2627401</link>
      <description><![CDATA[An earth-retaining structure (ERS) is a type of wall widely used to support and protect soil, rock, or other earth materials in many parts of transport infrastructure. ERSs are constructed to create grade separation, particularly at inclined locations or where earth cuttings are required. Over 42 types of ERSs exist in the areas under the jurisdiction of Transport for New South Wales (TfNSW). For effective structural health monitoring, it is crucial to classify an ERS according to its type and establish its GPS coordinates and longitudinal length. The classification is necessary because the defects that occur in the ERS depend on its type. This study proposes a deep learning-based framework that leverages ConvNeXt V2 to classify ERS types and capture the relevant data from vehicle-mounted video capturing systems. The framework integrates spatial–temporal image sequences with GPS coordinates and longitudinal length to enhance context-aware recognition. An improved ConvNeXt V2 model is fine-tuned to learn rich visual features of ERS embedded with GPS metadata. The model is trained and evaluated on a dataset comprising diverse ERS classes captured across TfNSW road transport areas. The experimental results demonstrate significant improvements in classification accuracy and robustness. This emphasizes the potential of the proposed system for scalable, automated ERS inventory and condition assessment.]]></description>
      <pubDate>Tue, 20 Jan 2026 09:09:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627401</guid>
    </item>
    <item>
      <title>Investigating the Impact of Hazard Management Training on Young Novice Drivers’ Speed Management Skills and Vice Versa: A Driving Simulator Study</title>
      <link>https://trid.trb.org/View/2632060</link>
      <description><![CDATA[A lack of Hazard Management (HM) and Speed Management (SM) skills have been identified as a leading factor in young novice drivers’ road crash involvement. Despite the success of individual training programmes targeting these two skills, their generalizability to one another remains unclear. Hence, the aim of the present research was to address this limitation. Ninety young novice drivers were randomly divided into five different training groups, and following training, their HM and SM skills were assessed on two different occasions in a fixed-based driving simulator. The result revealed that HM and SM training improved their targeted skill; however, no generalization of training was evident. These findings emphasise the need for a new training technique that could improve these two critical road safety skills among young novice drivers.]]></description>
      <pubDate>Wed, 17 Dec 2025 09:40:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632060</guid>
    </item>
    <item>
      <title>Public perspectives on the 20 mph speed reduction in Wales: A Latent Dirichlet Allocation Approach</title>
      <link>https://trid.trb.org/View/2622049</link>
      <description><![CDATA[This study investigates public attitudes toward the Welsh Government’s implementation of a 20mph default speed limit on all non-unrestricted roads, for example those in urban environments, residential neighborhoods, and areas with high pedestrian activity, that previously were 30 mph, aimed at reducing traffic crashes, emissions, and improving quality of life.  Using a Latent Dirichlet Allocation (LDA) approach and VADER sentiment analysis to assess emotional tone, posts on X (formerly Twitter) with hashtags #20 mph and #20 mya (Welsh language for mph) were analyzed during two periods: one-month post-implementation (September–October 2023) and six months later (February–March 2024).  The results indicate that public opposition to the 20mph default speed limit decreased slightly six months after its implementation but remained high. However, since opposition declined while positive sentiment increased, results suggest a positive trend, aligning with psychological theories on social influence and attitude change.  The study underscores the value of a qualitative-quantitative approach in capturing nuanced public perspectives, offering insights beyond traditional survey methods. These findings provide actionable guidance for policymakers and practitioners seeking to implement and communicate public safety policies effectively. This research uniquely contributes to the literature by examining the 20mph speed limit in Wales, combining computational and psychological methods to explore public opinion dynamics over time.]]></description>
      <pubDate>Tue, 25 Nov 2025 09:25:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622049</guid>
    </item>
    <item>
      <title>Reassessment of post-construction residual settlement of a bridge approach embankment using Bayesian back analysis</title>
      <link>https://trid.trb.org/View/2592547</link>
      <description><![CDATA[As part of a highway upgrade project in northern New South Wales, Australia, a bridge was constructed over deep soft soils improved by preloading and prefabricated vertical drains (PVDs). Shortly after the bridge opened to traffic, the bridge approach slab settled beyond the serviceability limit. Although slab jacking was implemented, subsequent monitoring revealed settlement again exceeded the predicted upper bound, prompting a reassessment of long-term residual settlement and mitigation strategies. However, this reassessment is challenged by discontinuous monitoring data, instrumentation changes and uncertainty in settlement offsets. Early settlement measurements (May 2017 to February 2018) were taken away from the final embankment location due to a design-stage realignment that shifted the southern abutment. Monitoring was halted during abutment construction and resumed from July 2019. To overcome these challenges, a Bayesian back analysis framework was adopted to calibrate both dataset offsets and soil parameters. The analysis showed that using only the post-construction monitoring data provides the closest fit to the measurements and a reliable prediction of the ongoing settlement growth. The predicted residual settlement over the service life ranges from 306 to 444 mm, with an average value of 385 mm. Sensitivity analyses indicate that slight variations in fill unit weight, due to heavy compaction, and in soft soil thickness, influenced by bridge realignment, have limited impact on settlement predictions due to compensating effects within the Bayesian model. This study also demonstrates the value of probabilistic approaches for assessing long-term settlement under data discontinuities and soil uncertainty, providing insights for similar infrastructure projects.]]></description>
      <pubDate>Thu, 16 Oct 2025 17:02:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592547</guid>
    </item>
    <item>
      <title>Pre-disaster evacuation transport network design under uncertain demand and connectivity reliability: A novel bi-level programming model</title>
      <link>https://trid.trb.org/View/2587278</link>
      <description><![CDATA[Evacuation transport network design plays a critical role in the efficiency of emergency response. This research proposes a novel bi-level nonlinear programming model for the pre-flood evacuation transport network design. The model considers both uncertainties of demand and network connectivity reliability. An upper-level model is developed with the minimum total evacuation time and maximum network connectivity reliability, while the lower-level model is a traffic assignment model that describes people’s evacuation route choice behavior. For the uncertain network connectivity reliability, an approach to quantify it based on percolation theory is proposed. For the uncertain demand, an approach to transform it into a solvable form based on Robust Optimization (RO) is proposed. Furthermore, the lower-level model introduces the regret-risk utility function as its objective function and proves its applicability. An equilibrium condition is proposed to improve the Logit model based on the regret-risk utility function. For the solution of this model, an Improved Genetic Algorithm combined with Non-dominated Sorting Genetic Algorithm II(IGA-NSGA-II) is designed. Then, the Nguyen-Dupuis network is used to demonstrate that the approach developed in this paper can be used to solve the bi-level nonlinear programming model and to obtain a satisfactory design solution. Further, a parameter sensitivity analysis is shown to study the impact of the risk aversion parameter and regret aversion parameter in the regret-risk utility function. Finally, the Central Coast region of New South Wales, Australia is used as a case study, and the research output will help government authorities to plan and design a pre-flood evacuation transport network, especially to answer the questions of “Where to build potential roads?”, “How much budget is needed?”, “How long does it take to evacuate?”, and “How reliable is the network connectivity?”.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587278</guid>
    </item>
    <item>
      <title>A review of the effectiveness of interventions in improving uptake of active travel in women in Wales</title>
      <link>https://trid.trb.org/View/2584304</link>
      <description><![CDATA[Active travel is positively linked to overall health, but is only used for around 2% of UK journeys and is lower in women than men. An increase in active travel by women will improve equality of health benefits with men, reduce emissions, and may also improve uptake of active travel in children. Identifying interventions which increase uptake of active travel in women is therefore of interest to policy makers. A literature review using systematic methods was carried out to identify effective interventions for increasing active travel by women. Seven studies were identified, four reviewed individual or community interventions, and three reviewed environmental interventions. Overall, evidence was of weak quality with variable outcomes. Bike-to-work days appeared to result in long-term modal change in female first-timers compared to male, but men were still more likely to cycle overall. Infrastructure changes, particularly segregated bicycle lanes, improved active travel uptake, but usually to a lesser extent in women than men. Overall, this review found that infrastructure improvements and a cultural shift towards active travel is needed to improve uptake in women, however data are limited by short follow-up periods. It identified a need for more consistency in data collection and reporting, as well as an improved breakdown of outcomes by sociodemographic characteristics, to identify which interventions work in which population subgroups.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2584304</guid>
    </item>
    <item>
      <title>Reevaluating Network Connectivity: The Critical Role of the Relative Size of Largest Connected Component (RSLCC)</title>
      <link>https://trid.trb.org/View/2571355</link>
      <description><![CDATA[In network structure analysis, metrics such as Isolated Node Ratio (INR), Network Efficiency (NE), Network Clustering Coefficient (NCC), Betweenness Centrality (BC), and Closeness Centrality (CC) are used as quantitative tools to measure network connectivity. However, there is another metric that is often easily overlooked and underestimated: the Relative Size of the Largest Connected Component (RSLCC). The authors' research not only proves that this metric is underestimated but also designs seven methods to predict its value, achieving a Deep Neural Network (DNN) prediction accuracy of more than 99%. DNN has demonstrated excellent predictive performance in large-scale networks, while Random Forest Regression (RFR) has proven to be highly effective and the fastest method for small-scale networks. The results of this research can be applied to any network. In a disaster scenario, whether it involves a physical network or a virtual abstract network. The authors illustrate the practical application of these insights using a case study of transportation network disaster rescue. Detailed steps are provided to explain how to implement these research findings in real-world scenarios, demonstrating the feasibility of applying this research to actual emergency situations.]]></description>
      <pubDate>Mon, 25 Aug 2025 13:44:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571355</guid>
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