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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Toward sustainable active school travel: A systematic umbrella review highlighting correlates and interventions</title>
      <link>https://trid.trb.org/View/2704839</link>
      <description><![CDATA[Active school travel (AST) supports children's health and contributes to urban sustainability, yet the rapidly expanding literature remains fragmented and inconsistent. This study presents the first umbrella review on AST, integrating a systematic mapping of the review landscape with a targeted synthesis of correlates and interventions. A systematic search of six databases and four review repositories was conducted in accordance with PRISMA and JBI guidelines, with the protocol prospectively registered in PROSPERO (CRD42024607002). Thirty-four reviews met the eligibility criteria and were classified into four thematic categories and nine review types. Although the Social Ecological Model was frequently employed to examine AST correlates, the evidence base remains heterogeneous, characterized by inconsistent measurement approaches, conflation of subjective and objective indicators, and imprecise construct definitions. The synthesis identified relatively robust positive associations for objective population density, perceived facility accessibility and traffic safety, whereas other environmental correlates exhibited weaker, mixed, or context-dependent relationships. Interventions to promote AST were largely framed within the Safe Routes to School paradigm, with the Walking School Bus program demonstrating particular promise. However, many intervention studies lacked solid theoretical foundations, sufficient methodological rigor, and adequate attention to systemic implementation and governance, which constrained their overall effectiveness. By identifying critical methodological and empirical gaps, this review calls for robust analytical strategies and the consolidation of existing findings to foster cumulative scientific progress. Future efforts should prioritize underexplored qualitative, longitudinal, policy-related, and technology-informed perspectives. These findings provide targeted guidance for advancing empirical research and evidence synthesis in AST and related domains.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704839</guid>
    </item>
    <item>
      <title>Data-driven insights on urban walkability: Modeling the relationships between urban form and pedestrian activity</title>
      <link>https://trid.trb.org/View/2704835</link>
      <description><![CDATA[Enhancing neighborhood design for pedestrians presents a powerful opportunity to foster sustainability in urban settings. Improving walkability is one strategy that can help cities reduce energy consumption, lower emissions, and improve public health. Architectural theories have long emphasized the importance of pedestrian-accessible design in fostering vibrant, resilient communities. However, empirical evaluations of pedestrian activity across large spatiotemporal scales remain limited. In this study, we present a data-driven analysis of how urban form features are associated with pedestrian count data from motion sensors in Melbourne's Central Business District. We defined 5- and 15-minute walking isochrones from each sensor, retaining 50 features after preprocessing. Tree-based regression and feature importance models were used to assess the relative importance and marginal effects of urban form features on pedestrian activity. Our findings show that high bench density and job counts within a 15-minute isochrone are linked to increased pedestrian activity. Dwelling counts and elderly population are also key predictors, suggesting the impacts of demographics and land use mix. Additionally, green spaces, visual factors, and tram stops also show greater importance, with their marginal effects varying by time and day type, emphasizing the need to account for temporal variations. Our findings also show alignment with survey data, offering deeper insights. Overall, this study highlights the potential of analyzing urban design features alongside walking data to inform pedestrian-oriented city planning. Our approach demonstrates how accessible spatial and longitudinal data can support data-based policymaking and urban planning by uncovering associations relevant to walkability.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704835</guid>
    </item>
    <item>
      <title>Unveiling the dual impacts of built environments on walking using GPS and machine learning: A case study of older adults in Jeonju, South Korea</title>
      <link>https://trid.trb.org/View/2704823</link>
      <description><![CDATA[As populations age, promoting healthy aging becomes crucial for individual and societal benefits. Healthy aging involves residing in environments that support healthy behaviors, and walking is one of them. According to previous studies, older individuals preferred certain environments for walking, but some findings showed inconsistency. The inconsistency might be due to a lack of a comprehensive comparison of walking patterns, such as walking frequency and speed, across different settings, which may lead to divergent results. Therefore, this research comprehensively compares walking frequency and speed within the same built environment, using 200 m × 200 m grid cells. GPS data from 32 seniors in Jeonju, South Korea, collected over 104 days and spanning 341 trips, were analyzed, using QGIS for spatial analysis and machine learning techniques, SHAP, and PDP for interpreting non-linear results. Shorter distances from home (<827.06 m), higher vegetation area ratios (8.27–86.76%), and larger water area ratios (≥31.74%) positively influenced walking trip frequency. While higher vegetation ratios (≥20.59%) and narrower (<2.33 m) or wider (≥11.30 m) roads were linked to slower speeds, despite the same natural characteristics, larger water area ratios (≥0.09%) were associated with faster walking speeds. The findings revealed distinct features influencing each pattern, with varying trends and impacts. This study underscores the nuanced impact of built environment features on seniors' walking behaviors and highlights the need for tailored urban designs to foster healthy aging.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704823</guid>
    </item>
    <item>
      <title>Quantifying Pedestrian Thermal Comfort under Extreme Heat Using 360° Street View Factors</title>
      <link>https://trid.trb.org/View/2704791</link>
      <description><![CDATA[Extreme heat increasingly threatens pedestrian thermal comfort in compact urban streets, yet commonly used indicators (e.g., hemispherical SVF) inadequately represent the full three-dimensional enclosure experienced at street level. This study proposes an integrated field–image framework based on 360° view factors (360°VFs) to quantify omnidirectional streetscape exposure and its links to the pedestrian thermal environment under extreme heat. Mobile measurements of air temperature (Ta), surface temperature (Ts), mean radiant temperature (Tmrt), and physiological equivalent temperature (PET) were conducted along a representative walking route during both daytime and nighttime. Fisheye images were semantically segmented to derive 360°VFs for buildings, trees, grass–shrubs, roads, sky, and water, and their effects were examined across 10–70 m buffer scales. Results reveal strong diurnal and spatial heterogeneity in all thermal metrics, driven by the coupled influences of radiative exchange, surface heat storage (impervious materials), vegetation-related shading/evapotranspiration, and ventilation constraints imposed by built form. Correlation and regression analyses suggest 360°BVF as the dominant warming contributor, whereas tree and grass–shrub view factors provide consistent cooling in both periods. Clear scale dependence is observed: daytime Tmrt/PET respond most strongly within 20–30 m, while nighttime conditions are more influenced by larger-scale morphology around ∼50 m. Compared with SVF, 360°VFs improve the prediction of Ta, Tmrt, and PET, particularly at night. The proposed 360°VFs framework provides a practical basis for scale-specific, climate-adaptive street design targeting vegetation configuration, enclosure control, ventilation pathways, and material strategies to reduce extreme-heat exposure for pedestrians.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704791</guid>
    </item>
    <item>
      <title>Understanding metro origin-destination passenger flow dynamics: a novel entropy and machine learning perspective</title>
      <link>https://trid.trb.org/View/2732505</link>
      <description><![CDATA[This study explores how the built environment around origin and destination (OD) metro stations impacts passenger flow, using an entropy-based method to classify OD pairs into stable, unstable, and chaotic types based on daily flow rankings. Advanced machine learning is employed to explore the relationship between station environments and OD flows. Findings reveal the built environment’s varying influence. In stable OD flows, livelihood and economic services at the destination station are the primary factors driving passenger movement. In unstable OD flows, a mix of economic and recreation & health services near the origin station creates more fluctuating travel patterns. For chaotic OD flows, the presence of recreation & health services at both the origin and destination stations shapes spontaneous and unpredictable travel behaviors. Based on these findings, this study provides insights essential for guiding transit-oriented development (TOD) and underscore the importance of considering passenger flow dynamics in urban planning.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732505</guid>
    </item>
    <item>
      <title>A sample selection model with multinomial endogenous switching: Addressing sample selection bias on binary and continuous outcomes of household vehicle miles traveled</title>
      <link>https://trid.trb.org/View/2701475</link>
      <description><![CDATA[The sample selection modeling approach has been used to quantify the influence of residential self-selection (RSS) on travel behavior outcomes within the framework of sample selection bias (SSB) correction. A limitation of this approach is the reliance on a simple binary endogenous switching mechanism, which oversimplifies residential choices as a binary decision. To address this, this study proposes a sample selection modeling framework that incorporates multinomial endogenous switching. It also accommodates correlated alternatives in multinomial residential choice and handles binary or continuous travel behavior outcomes. We apply this model to data from the 2017 US National Household Travel Survey, which includes 129,587 households. It identifies the SSB influences and the resulting average effects of living in each of the four neighborhood types on household vehicle miles traveled (VMT) for binary and continuous outcomes: (i) whether a household took a car trip on the survey day and (ii) VMT for those households that took a car trip. For the binary outcome, the average predicted probability of taking a car trip is highest in the Second City neighborhoods, followed by the Suburban and the Urban neighborhoods, and lowest in the Small Town/Rural neighborhoods. For the continuous outcome, expected household VMT is highest in the Second City neighborhoods, followed by the Suburban and the Small Town/Rural neighborhoods, and lowest in the Urban neighborhoods. While the identified SSB influences may also capture certain unobserved built environment characteristics, we nevertheless derive valuable insights for the formulation of more effective land-use and transport policies.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701475</guid>
    </item>
    <item>
      <title>Socio-ecological determinants of activity choice and independence in children’s out-of-home leisure time. A study from Germany</title>
      <link>https://trid.trb.org/View/2731035</link>
      <description><![CDATA[This study investigates associations between socio-ecological factors and children’s activity choice and independence in children’s out-of-home leisure time. Adapting the “Socio-ecological model of children's independent mobility and transportation outcomes” (Mitra, Manaugh 2020), we draw on a survey conducted in Dortmund, Germany, in autumn 2020 (n = 649) and analyze children’s activities on the weekend prior to the survey (children aged 5–10). Our results show that household travel and parents’ perceptions are significantly associated with children’s leisure activities. In particular, the routine of escorting children to walkable non-school places is negatively associated with visiting friends or going to playgrounds, parks or forests but with a higher likelihood of adult company for all activity categories studied. The analyses suggest a negative relationship between parental concerns and activity participation as well as independence in children’s leisure time. Conversely, parental perception of familial integration into the neighborhood was positively associated with activity participation and negatively associated with adult company on activities. Furthermore, socio-economic status of the household and social disadvantage appear to affect children’s leisure activities. Regarding built environments, we find that children living in commercial-industrial and multifamily residential environments had higher odds of going to playgrounds, parks or forests, but children living in commercial-industrial residential environments had lower odds of visiting friends. The availability of playgrounds and playing fields in the neighborhood was not associated with activity choice and independence on any of the activity categories studied here. We recommend ensuring access to adequate outdoor play spaces for children, particularly in residential environments, where private gardens are not available as outdoor alternatives. To promote children’s active and independent leisure time, policies could benefit from more holistic approaches. For example, car-free days help strengthen local social bonds to build mutual trust and reduce parental concerns.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731035</guid>
    </item>
    <item>
      <title>When closer is not better: travel well-being among peri-urban older adults</title>
      <link>https://trid.trb.org/View/2743041</link>
      <description><![CDATA[Transportation-related subjective well-being captures emotions and satisfaction during travel and is increasingly used to evaluate the social benefits of transport and spatial policies. Using survey data from older adults in peri-urban Chengdu, China, we examine the associations of objective accessibility, perceived built-environment quality, perceived accessibility, and mobility capability with travel well-being and overall well-being. This study combines structural equation modeling, explainable machine learning, and robust interaction regressions to examine structural pathways, nonlinear patterns, and conditional associations. Perceived built-environment quality and mobility capability are positively associated with travel well-being, which is, in turn, positively associated with overall well-being. By contrast, higher objective accessibility is associated with lower travel well-being on average. This negative association tends to weaken at higher levels of perceived built-environment quality and mobility capability. The findings suggest that improvements in proximity alone may be insufficient and should be accompanied by safe, comfortable, continuous, predictable, and barrier-free walking conditions.]]></description>
      <pubDate>Tue, 11 Aug 2026 16:35:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2743041</guid>
    </item>
    <item>
      <title>Advancing active mobility in African towns − An AHP-based approach to active mobility planning in Namibia</title>
      <link>https://trid.trb.org/View/2692400</link>
      <description><![CDATA[Active mobility (walking and cycling) remains the dominant mode of travel in small- and medium-sized towns across Sub-Saharan Africa, yet the supporting infrastructure is often inadequate. This study applies the Analytic Hierarchy Process (AHP) to systematically prioritise active mobility infrastructure improvements in four Namibian towns: Rundu, Tsumeb, Walvis Bay, and Keetmanshoop. Through pairwise comparisons, local urban transport and planning experts evaluated fourteen key indicators related to crosswalks, sidewalks, and bikeways, focusing on safety, accessibility, and continuity. The results reveal clear and context-specific priorities. Across all towns (particularly in Rundu and Walvis Bay), the highest weights were assigned to improving the safety and comfort of sidewalks, followed by ensuring accessible crosswalks for pedestrians and persons with disabilities and providing basic crossing facilities. Walvis Bay prioritised visibility and maintenance of existing crossings, reflecting its more developed infrastructure. Bikeway indicators received comparatively lower weights but gained prominence in Tsumeb and Keetmanshoop, highlighting emerging attention to cycling. Standard deviations were generally low, indicating strong expert consensus. The findings demonstrate that AHP is a useful decision-support tool for identifying locally relevant and high-impact priorities in data-sparse contexts. They further highlight the need for tailored and phased interventions, focussing on basic infrastructure provision in some towns and maintenance and enforcement in others, to create safer, more inclusive active mobility networks. These insights provide a structured evidence base to guide policy and investment decisions in Namibian and African towns with similar urban mobility challenges.]]></description>
      <pubDate>Mon, 10 Aug 2026 16:51:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692400</guid>
    </item>
    <item>
      <title>Nonlinear effects of built environment on integrated usage of electric bike-sharing and metro</title>
      <link>https://trid.trb.org/View/2692385</link>
      <description><![CDATA[The rapid expansion of electric bike-sharing (EBS) services provides a convenient and flexible feeder mode for metro systems, offering considerable potential to address the first- and last-mile mobility challenge. Although previous studies have examined the integration of conventional bike-sharing with metro networks, research specifically focusing on EBS as a feeder mode remains scarce. Using large-scale EBS trip data from Hefei, China, this study uncovers the spatial and temporal patterns of EBS–metro integrated usage and applies a generalized additive mixed model to analyze their nonlinear relationship with the built environment around metro stations. The results show pronounced peak-hour patterns on weekdays, while weekend usage is more evenly distributed. Spatially, integrated usage is concentrated in the western and southern parts of the city, where business parks and universities cluster. The modeling results further reveal significant nonlinear relationships between built environment variables and integrated usage. Dense major road networks are negatively associated with metro-integrated EBS usage, whereas dense branch road networks are positively associated with it. Moreover, a moderate level of land-use diversity appears to be associated with higher integration, while higher bus station density is negatively associated with integrated usage, which may reflect a degree of competition between the two feeder modes. This study demonstrates that the built-environment effects on EBS–metro integration both align with and differ from those documented for conventional bike-sharing systems, thereby providing mode-specific practical insights for policymakers and service operators seeking to optimize EBS management and implement targeted built-environment interventions to support sustainable multimodal travel.]]></description>
      <pubDate>Mon, 10 Aug 2026 16:51:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692385</guid>
    </item>
    <item>
      <title>Disentangling travel characteristics of micro-mobility: Insights provided by an interpretable spatial machine learning modeling framework</title>
      <link>https://trid.trb.org/View/2702338</link>
      <description><![CDATA[Micro-mobility offers a practical pathway to drive sustainable transformation of urban transportation systems. Few studies have comprehensively examined the similarities and disparities between travel characteristics of different micro-mobility modes from multiple perspectives, and understanding travel characteristics of micro-mobility still remains challenging. Using micro-mobility travel data from Zhoukou, China, this paper utilizes bicycles and e-bikes as examples and takes personal attributes, household characteristics, and built environment into consideration to comparatively analyze the spatial heterogeneity and nonlinear characteristics of micro-mobility travel with the aid of an interpretable spatial machine learning modeling framework combining Geographically Weighted Random Forest (GWRF) model and SHapley Additive exPlanations (SHAP) model. Study results reveal that: (1) The GWRF model outperforms traditional models in data-fitting capability and predictive accuracy for micro-mobility travel data. (2) The built environment, especially road density and building density, exerts more important impacts on micro-mobility travel distance than personal attributes and household characteristics. (3) The association between micro-mobility travel distance and the number of household travel modes exhibits significant spatial heterogeneity, varying between bicycles and e-bikes. (4) Personal attributes, household characteristics, and the built environment have nonlinear associations with micro-mobility travel distance, characterized by significant threshold effects and both shared and distinct patterns. These findings provide policymakers and urban planners with useful and reliable insights to contribute to the formulation and implementation of differentiated micro-mobility development policies.]]></description>
      <pubDate>Fri, 07 Aug 2026 09:21:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702338</guid>
    </item>
    <item>
      <title>Typhoon and bike-sharing: Non-linear built environment associations with activity recovery in Shenzhen</title>
      <link>https://trid.trb.org/View/2737794</link>
      <description><![CDATA[With extreme weather becoming more frequent, transportation resilience is critical. However, evidence remains limited regarding how built-environment and urban-context conditions are associated with grid-level bike-sharing activity recovery under disruption. We analyze dockless bike-sharing recovery during Typhoon Cempaka in Shenzhen using Light Gradient Boosting Machine (LightGBM), SHapley Additive exPlanations (SHAP), Generalized Additive Model (GAM), and model-based 3D response surfaces. The outcome captures service-use recovery, not individual cycling safety, welfare, or causal resilience. Results show that: (1) nighttime light intensity, population density, and distance to the nearest planned urban functional center are the leading diagnostic predictors; (2) several predictors show non-linear fitted responses, so recovery associations vary by range; and (3) the strongest interaction patterns connect activity intensity with population concentration, polycentric location, and transit accessibility. Robustness checks indicate sensitivity to baseline demand and meteorological exposure, and spatial validation shows residual spatial structure.]]></description>
      <pubDate>Mon, 03 Aug 2026 09:23:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737794</guid>
    </item>
    <item>
      <title>Evaluating the mixture Weibull distribution for modeling the pedestrian-level wind environment around an isolated building</title>
      <link>https://trid.trb.org/View/2689881</link>
      <description><![CDATA[Wind speed probability distribution functions (PDFs) are essential for assessing urban wind environments. While unimodal distribution functions have commonly been used in previous studies, urban wind exhibits bimodal PDF patterns at specific locations. This study examines the robustness of the mixture Weibull distribution (2W2W) in capturing unimodal and bimodal PDFs of wind speed around an isolated building, ensuring reliability even in the presence of rare bimodal features. The 2W2W model's performance is compared against two unimodal models of the two-parameter Weibull distribution (2W) and the three-parameter Weibull distribution (3W) using two parameter estimation methods: the method of moments (MM) and the maximum likelihood method (ML). MM utilizes higher-order moments, enabling convenient implementation with the statistics from simulations or experiments, while ML serves as a robust validation approach for MM. The findings indicate that 2W2W provides superior accuracy in modeling complex PDFs, particularly in representing bimodal patterns. However, numerical deviations were observed in 2W2W under MM, which are attributed to the parameter solution falling into local optima. To mitigate these issues and improve reliability, an adaptive strategy was introduced, enhancing the local accuracy of the model. This study successfully advanced PDF modeling, offering significant improvements in evaluating urban wind environment.]]></description>
      <pubDate>Fri, 31 Jul 2026 16:18:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689881</guid>
    </item>
    <item>
      <title>Interpretable machine learning of urban noise levels from street-scale morphology and functionality</title>
      <link>https://trid.trb.org/View/2690027</link>
      <description><![CDATA[Urban noise exposure is shaped not only by traffic intensity but also by the spatial configuration and functional hierarchy of streets. Traditional prediction approaches, however, often prioritize traffic flow while underrepresenting the role of urban form. This study examines how street-scale morphological and functional characteristics are associated with environmental noise levels across four Chilean cities with contrasting urban structures. A total of 530 daytime in situ measurements of the equivalent continuous sound level LAeq were combined with 71 urban predictors derived from municipal records and open-source data within 150 m buffers around each sampling location. Three ensemble learning models (Random Forest, XGBoost, and LightGBM) were optimized using Bayesian hyperparameter search and evaluated under intra-city validation, achieving strong explanatory power (R² = 0.65–0.95) and high accuracy (MAE≈ 1.5–2.2 dBA). Cross-city evaluation using a leave-one-city-out scheme revealed moderate performance degradation when predicting unseen urban contexts, highlighting both the potential and limits of territorial transferability. Model interpretability analyses based on LightGBM gain, SHAP values, and LIME explanations consistently identified street functional hierarchy as the dominant indicator associated with urban noise exposure, followed by roadway capacity and land-use intensity. Spatial patterns revealed elevated noise levels (>65 dBA) along higher-order arterial corridors, while quieter conditions were typically associated with lower-category residential streets. Overall, the results show that interpretable machine learning provides a transparent, data-driven, and non-causal framework for linking urban form and function to street-level noise patterns, supporting noise screening and urban planning in data-constrained contexts.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2690027</guid>
    </item>
    <item>
      <title>Asymmetric Bidirectional Interaction of EVCS Infrastructure and Urban Spatial Structure within Diurnal Rhythms</title>
      <link>https://trid.trb.org/View/2687120</link>
      <description><![CDATA[The alignment between electric vehicle charging station (EVCS) infrastructure and urban spatial structure is fundamental to sustainable city planning. However, this potential is restricted by conventional design, which often treats EVCS layout as a passive adaptation to urban structure rather than a bidirectional process. To quantify this interaction, a cross-lagged panel model is applied to analyze a comparative dataset of Chengdu and Chongqing from 2020 to 2025. A dynamic urban composite index is devised in this paper by fusing hourly population heat data with the static urban spatial structure. The findings reveal an asymmetric relationship: the city’s stable underlying structure (in particular, road networks) predicts the long-term EVCS layout (β≈ 0.21–0.28). In contrast, the charging infrastructure reveals a targeted association with specific urban functional structures (UFS), representing a distinct alignment with the nighttime residential function (β≈ 0.11). These findings provide quantitative evidence to revisit the passive response paradigm, implying an integrated framework that coordinates the macro-level structural basis with micro-level functional rhythms.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687120</guid>
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