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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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    <item>
      <title>Emerging Travel Behavior Insights From National Surveys and Count Data</title>
      <link>https://trid.trb.org/View/2712616</link>
      <description><![CDATA[The early 21st century to the present is a pivotal era in the study of travel behavior and time use. This two-decade period has witnessed not only incremental adjustments but also transformative shifts in how individuals allocate their time across various activities and make travel-related decisions within their time and monetary constraints. These shifts are not isolated phenomena; they are deeply intertwined with broader, multidimensional changes driven by factors such as the widespread adoption of Internet and Communication Technologies (leading to innovations in mobility services and online substitutes for many in-person activities) and shifts in demographics and cultural norms. Overall, this period of change holds profound implications for transportation planning and policy development. This multi-stage project aims to navigate this complex terrain to shed light on the trends in time, travel, transit, telework, and treasure (T5) during this period. The project primarily utilizes data from the American Community Survey (ACS), the American Time Use Survey (ATUS), and the Consumer Expenditure Survey (CES), along with insights from field counts and travel estimates, to analyze these trends. Understanding these trends is crucial for effectively addressing current and future challenges and seizing opportunities in transportation management, economic resilience, and societal wellbeing.]]></description>
      <pubDate>Tue, 16 Jun 2026 07:28:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712616</guid>
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    <item>
      <title>Bridging the Gap: Infrastructure, Services, and Labor Market Integration</title>
      <link>https://trid.trb.org/View/2709188</link>
      <description><![CDATA[How does transport infrastructure shape services provision, labor markets, and regional inequality? This paper studies the 2000 opening of the Øresund Bridge between Malmö, Sweden, and Copenhagen, Denmark, which sharply reduced travel time. The setting mirrors infrastructure linking peripheral regions to urban hubs, while the border makes unobserved services measurable in trade statistics. The bridge enabled cross-border commuting for on-site services and lowered business-travel costs for partially remote services like consulting. The paper develops a spatial model with commuting, migration, and services trade to separate these channels and quantify welfare effects. Services trade is distance-sensitive, with elasticities from −1.06 to −0.45, but less so than commuting. Lower commuting costs thus generate uneven gains, while lower services trade costs produce more diffuse benefits. Ignoring services understates welfare gains of infrastructure by 18%, especially for farther regions benefiting through services trade via business travel but not commuting.]]></description>
      <pubDate>Thu, 11 Jun 2026 13:20:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709188</guid>
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    <item>
      <title>Understanding traveller behaviour under congestion pricing: A survey-based decision tracking approach</title>
      <link>https://trid.trb.org/View/2704528</link>
      <description><![CDATA[This paper examines commuter responses to three congestion pricing (CP) schemes including cordon-based, distance-based, and travel time–based in Calgary, Canada. The study uses an efficiently structured, personalized stated preference survey that integrates sociodemographic information, revealed commuting patterns, and responses to hypothetical CP scenarios, with a focus on joint mode and departure-time decisions. A key contribution of this study lies in the survey design, which records respondents’ interactions with the choice interface and identifies the alternatives actively examined during each choice task. This information is used to estimate a tracked multinomial logit model in which the choice set is restricted to alternatives genuinely considered by each respondent. Comparison with a conventional non-tracked specification shows that assuming full consideration of all displayed alternatives inflates sensitivity to travel time, cost, and toll attributes. By contrast, the tracked model yields more moderate elasticities and parameter estimates that better reflect observed decision-making behavior. Applied to the congestion pricing context, the results suggest shifts towards transit and ridesharing under certain pricing structures, highlighting how pricing design can influence peak-period travel decisions. Overall, the findings demonstrate that incorporating interaction-tracking data into stated preference surveys enhances the behavioral credibility and policy relevance of model-based evaluations of congestion pricing strategies.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704528</guid>
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    <item>
      <title>Mobility situations in Mexico City Metropolitan Zone: An exploration of time and distance in the journey to work through machine learning</title>
      <link>https://trid.trb.org/View/2672683</link>
      <description><![CDATA[This study examines the determinants of commuting time and distance using the mobility situations framework in the Mexico City Metropolitan Zone (MCMZ), a megacity marked by spatial mismatch, socioeconomic segregation, and fragmented transport infrastructure. Using data from the 2017 Origin-Destination Survey, we classify 192 travel districts into four mobility situations—Short Commutes, Long Commutes, Travelscarps, and Wormholes—based on average commuting time and distance. Our approach combines spatial econometrics with a machine learning LASSO algorithm to evaluate 88 potential predictors across transport infrastructure, urban spatial structure, and socioeconomic conditions. Results show that each situation is driven by distinct factors: Short Commutes align with centrality and privilege; Long Commutes with exclusion and mass transit; Travelscarps with inefficient short trips from poor infrastructure; and Wormholes with efficient long trips through multimodal strategies. The study demonstrates the value of the mobility situations framework in a Global South city and highlights machine learning’s utility for variable selection and theory-building in journey-to-work research.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672683</guid>
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    <item>
      <title>Micromobility and Urban Planning</title>
      <link>https://trid.trb.org/View/2580775</link>
      <description><![CDATA[The rise of micromobility has significantly influenced urban planning, offering innovative solutions to address urban challenges such as traffic congestion, air pollution, and climate change. This chapter explores the impact of micromobility, encompassing electric scooters, bicycles, e-bikes, and other personal mobility devices, on sustainable and efficient urban transport systems. Drawing on the United Nations’ 2030 Sustainable Development Agenda, the study emphasizes the need for sustainable mobility strategies to improve urban quality of life. As cities increasingly integrate micromobility into their transport networks, urban planning has evolved to accommodate these changes, leading to new concepts like the 15-min city, where essential services are accessible within a short walk or cycle ride. The relationship between urban planning and micromobility is reciprocal; urban design must adapt to support the growth of micromobility, while micromobility provides opportunities to reshape urban spaces. This paper examines how micromobility contributes to reducing car dependency, creating pedestrian-friendly streets, and enhancing the use of green spaces. It assesses how the planning and development of dedicated infrastructure, such as bike lanes, scooter parking, and multi-modal hubs, can facilitate the safe and efficient use of micromobility vehicles. Moreover, it explores how urban planners can leverage micromobility to promote social equity by improving accessibility for diverse socio-economic groups. The study provides an in-depth analysis of micromobility policies and regulations, underscoring the importance of collaboration between urban planners, policymakers, and micromobility providers to ensure equitable and sustainable growth. It also addresses the challenges of safety, infrastructure inadequacies, and the need for regulations to guide effective integration into urban transport systems. By investigating infrastructure requirements, policy implications, and societal adaptation processes, this research aims to contribute to the development of strategies that position micromobility as a key component in future urban mobility plans. Ultimately, the findings highlight how investing in micromobility infrastructure can reshape cities into more livable, resilient, and sustainable environments.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2580775</guid>
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    <item>
      <title>Identifying key factors associated with commuting burden and modelling their non-linear relationships: the case study of Shenzhen, China</title>
      <link>https://trid.trb.org/View/2694455</link>
      <description><![CDATA[Being closely related to quality of life, commuting burden has received increasing academic attention, especially for its influencing factors, such as land use and transportation infrastructures. However, scholars have seldom investigated different dimensions of commuting burden simultaneously. Furthermore, linear or generalised linear assumptions dominate previous studies regarding built environment-commuting burden associations. Therefore, employing mobile phone location data in Shenzhen, China, and utilising K-means Clustering and Gradient Boosting Decision Tree methods, this study examines the refined non-linear relationships between the built environment and commuting burden. It finds: (1) three commuting burden patterns are observed; (2) the built environment has the largest collective contribution for predicting commuting burden, and the residential environment is more important than the workplace environment; (3) nonlinearity generally exists in the relationships between the built environment and commuting burden. This study enriches the literature regarding commuting burden and its influencing factors and provides policy references for commuting burden mitigation.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694455</guid>
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    <item>
      <title>Understanding the perceived barriers to cycling as a mode of commuting in children and adolescents: A systematic review</title>
      <link>https://trid.trb.org/View/2702889</link>
      <description><![CDATA[To identify the perceived barriers to cycling as a mode of commuting for children and adolescents and to examine which barriers are associated with cycling behavior in studies addressing commuting to school and other destinations published between 2011 and 2024.The search across different databases identified a total of 11,011 records. After screening, 45 full-text articles were assessed for eligibility. Finally, 21 records were included because they met all the inclusion criteria. These records comprised both quantitative and qualitative studies. Studies were identified in four electronic databases: Web of Science, Scopus, PubMed, and SPORTDiscus in January 2024. Eligible studies were those published in English between 2011 and 2024 that included children and adolescents (5–17 years) and/or parents, with no geographical restrictions and focused on perceived barriers to cycling for commuting. Two reviewers independently extracted data using a standardized form, and methodological quality was assessed with the Hawker Appraisal Tool. Due to heterogeneity of study designs and outcomes, a narrative synthesis within an ecological framework (personal, social, environmental factors) was performed. The review was prospectively registered in INPLASY (DOI: 10.37766/inplasy2024.2.0061; protocol available via direct: https://inplasy.com/wp-content/uploads/2024/02/INPLASY-Protocol-5881.pdf).A total of 21 articles met the inclusion criteria. Barriers were categorized into personal, social, and environmental domains. Social factors were the most frequently reported barriers (15 studies), followed by built environment (12 studies) and safety-related concerns (11 studies). However, the barriers most consistently associated with cycling behavior were social support and built environment factors, whereas safety perceptions showed context-dependent associations. Although social factors were the most frequently reported barriers to cycling among children and adolescents, social support and built environment characteristics demonstrated the strongest and most consistent associations with cycling participation.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702889</guid>
    </item>
    <item>
      <title>Analyzing the interplay between driver’s license ownership, household characteristics, socioeconomic factors, and work travel mode choice by using the 2022 NHTS dataset</title>
      <link>https://trid.trb.org/View/2701747</link>
      <description><![CDATA[Travel mode choice (TMC) prediction is crucial for urban planning, transportation policy-making, and environmental sustainability. Understanding and forecasting how individuals choose their work travel modes can provide valuable insights that influence several societal and economic outcomes. Past studies analyzed individual datasets to separately examine the relationships among the built environment, socio-demographic characteristics, economic factors, household vehicle ownership, and driver’s license ownership. However, there is a lack of research examining how these variables collectively shape individual work TMC at both the individual and household levels. The current study addresses this gap by utilizing multidimensional data from the 2022 National Household Travel Survey to assess the combined influence of household characteristics, socio-economic status, driver’s license ownership, and urban/rural spatial environment on work TMC. The data are analyzed using cross-tabulation, graphical bivariate analyses, and multinomial logistic regression due to a categorical dependent variable. The statistical analysis reveals that the employment status and driver’s license ownership are the primary predictors significantly influencing the preference for motorized (MT) over non-motorized transportation (NMT). Households with more vehicles and workers are more likely to use MT, whereas larger households tend to prefer public transport (PT) or NMT. Additionally, demographic factors, including age, gender, income, and urban/rural residence, play a pivotal role, with older individuals and males showing a higher likelihood of choosing MT, while high-income individuals favor both MT and PT over NMT. Governments can promote PT through infrastructure development, last-mile connectivity, and subsidies, while reducing car dependency through congestion pricing and carpooling incentives, and encouraging NMT by expanding bike lanes and pedestrian-friendly infrastructure.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701747</guid>
    </item>
    <item>
      <title>Dedicated autonomous vehicle lane measure for development of autonomous vehicles: A system dynamics approach</title>
      <link>https://trid.trb.org/View/2669586</link>
      <description><![CDATA[Different from the stage of full use of human-driven vehicles (HDVs) or autonomous vehicles (AVs), the mixed use of AVs and HDVs represents a special transition stage. Interaction between HDVs and AVs presents a challenge for urban transport development, especially during the initial transition stage. Dedicated AV lane (DAVL) measure has been proposed for promoting development of AVs. This study focuses on the impact analysis of implementing DAVL measure under heterogeneous commuting conditions, which are characterised by varying automation levels and evolving AV market share. A system dynamics framework is developed to integrate the DAVL deployment with dynamic shifts in vehicle market adoption and commuting conditions. Specifically, macro-level commuting indicators are examined, i.e., average commuting speed and congestion degree, as AVs with varying automation levels progressively enter the market. Scenario analyses are based on the high-level autonomous driving demonstration zone in Yizhuang new city (YNC) of Beijing, they indicate that DAVL measure has a significant impact on AV market share, and positively influencing AV market development. However, with increasing AV market share, the initially superior commuting performance delivered by DAVL is gradually eroded, manifested by shrinking improvements in average commuting speed and weaker reductions in congestion degree. Moreover, the fixed DAVL approach results in uneven utilisation of road space, i.e., the congestion degree on regular lanes shifts from oversaturation toward underutilisation, whilst the congestion degree on the DAVL shifts from initial underutilisation toward saturation. Furthermore, using scenario analyses, this study examines a flexible DAVL measure that addresses the tension between rapid AVs development and the necessity of maintaining stable commuting conditions. Insights into the implementation of the DAVL measures are provided, which are expected to be applied in YNC and other urban contexts.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669586</guid>
    </item>
    <item>
      <title>Between dispersion and recentring: Observations on post-pandemic migration flow and housing relocation behaviours in Montréal, Canada</title>
      <link>https://trid.trb.org/View/2701514</link>
      <description><![CDATA[The COVID-19 pandemic has generated debate over whether changes in work and travel behaviour have resulted in a lasting reconfiguration of residential location preferences. This study examines residential relocation behaviour before, during, and after the COVID-19 pandemic using a spatial multinomial logit model applied to mobility survey data from the Montréal metropolitan region. The model achieves strong explanatory power and enables assessment of how housing relocation determents changed across pandemic phases. There is evidence of decentralization in relocation patterns; however, this evidence does not support a structural decline in the importance of spatial land use and transport factors, such as accessibility. Job accessibility by public transport remains positively valued across all periods, but the penalty associated with distance to work weakens after the pandemic. This weakening indicates increased tolerance for residential-workplace separation but not necessarily a rejection of accessibility itself. Neighbourhood quality consistently shapes relocation utility, alongside a strengthening post-pandemic preference for larger dwellings in suburban locations. Car ownership enables relocation beyond the core, while transit reliance anchors households to central areas. For low-income households, pandemic-related decentralization was temporary, with post-pandemic patterns reinforcing long-standing affordability constraints. Together, these findings suggest that pandemic-induced decentralization was a behavioural adaptation within structural determinants of residential choice. The study contributes to understanding how short-term shocks interact with housing markets, accessibility, and mobility resources, and informs housing and transport planning in post-pandemic urban regions.]]></description>
      <pubDate>Wed, 20 May 2026 10:20:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701514</guid>
    </item>
    <item>
      <title>How the built environment characteristics shape parents’ perception of active commuting norms</title>
      <link>https://trid.trb.org/View/2702194</link>
      <description><![CDATA[Active commuting to school (ACS) is a promising strategy to increase physical activity among adolescents. While characteristics of the built environment influence commuting behaviors, they may also shape parents’ perceptions of social norms related to ACS. This study analyses how subjective and objective built environment characteristics, at both micro- and macro-scale, are associated with parents’ perceptions of the social norm. When the built environment was considered as whole in a multivariable model, only the density of sports facilities (a macro-scale measure) was linked to parents’ perception of social norms, while subjective and micro-scale characteristics were not. These findings imply that environments with visible opportunities for physical activity, such as sports facilities, may shape parental perceptions of ACS as normative behavior. The presence and design of such facilities could be considered in planning, with macro-scale variables emerging as the most informative measure capturing associations with the social norm.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702194</guid>
    </item>
    <item>
      <title>Factors influencing the modal shift from motorised to active commuting among university populations</title>
      <link>https://trid.trb.org/View/2694657</link>
      <description><![CDATA[In a context of the transition to more sustainable cities and related public policy development, this study aims to analyze the willingness of current motorized commuters to switch to active modes of transport (walking, cycling) and the factors influencing this transition decision. The authors use data from the 12,000 respondents of the MobiCampus-UdL university community survey (2017–2019), conducted in 17 academic institutions in Lyon (France). The authors estimate a probit model with sample selection, explaining both the probability of using a motorized mode to usually get to campus and the propensity of these motorized commuters to switch to active modes. The results show that 31% of students and 27.5% of employees who use a motorized mode would be willing to switch to an active mode. The authors identify a number of barriers to change, such as the distance between home and campus, limited access to cycling infrastructure or a bike-share service, and the entrenched habits of young private car drivers. Most importantly, the results suggest that targeted policies, especially regarding cycling infrastructure and financial incentives, could accelerate the shift to active commuting. The presence of peer effects also seems to be a relevant lever to promote these practices, especially among students whose mobility habits influence their future behavior.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694657</guid>
    </item>
    <item>
      <title>Commuting of teaching and administrative staff as a driver of sustainable transport policy at the university</title>
      <link>https://trid.trb.org/View/2670078</link>
      <description><![CDATA[Universities are increasingly focusing on sustainability. This includes sustainable transportation. Research on commuting by university students and employees provides valuable insights into improving campus transport systems in terms of their social and environmental impacts. By identifying trends, challenges, and effective solutions, such studies help universities design policies and infrastructure that promote sustainability, accessibility, and equity in higher education.Teaching staff (working in research and/or teaching positions), administrative staff (working in administrative or technical positions), and students commuting to and from the university have environmental, social, and economic implications. To explore the effects of this influence, a survey of employees and students at the Poznań University of Economics and Business was conducted in 2023 to examine their present travel behaviour, including the number of trips, needs, modal choice, and travel frequency.This study highlights the potential of universities to lead sustainable transport initiatives, demonstrating that significant strides toward sustainability can be achieved through concerted efforts in policy, infrastructure, and community engagement.By adopting innovative transportation solutions, universities not only advance their sustainability agendas but also set an example for cities and communities worldwide. As living labs for sustainability, they can significantly contribute to global efforts in transport innovation and climate action.The general findings of the study are as follows: (a) the most important criterion for choosing a specific mode of transport according to PUEB staff is availability, which reflects an ease of traveling between two points in space and of accessing different transport options; (b) the environmental impact is not an important criterion for choosing a commuting method and is less important than “availability”, “distance”, and “flexibility”; (c) policies on sustainable mobility for universities should be based on measures that primarily relate to “availability”,”costs”, and “(time) flexibility“.]]></description>
      <pubDate>Fri, 15 May 2026 15:44:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670078</guid>
    </item>
    <item>
      <title>Post-commute self-assessed work performance</title>
      <link>https://trid.trb.org/View/2669974</link>
      <description><![CDATA[Commuting can have immediate and long-term impacts on employees’ behaviour and motivations at work. This study focuses on the self-assessed impact of commute mode on multiple aspects of work performance, namely energy and productivity at work, and punctuality in getting to work. Using large-scale national data (N = 6,671) from the Canadian Mobility Survey conducted in Fall 2024 across 12 metropolitan regions, we conduct weighted logistic regression models that investigate each self-assessed work performance aspect. We use three data subsets based on region population size (large, mid-size, and small). Our models account for main travel mode, commute duration, motivations for mode choice, and sociodemographic factors. For all three subsets, we find that active commuters (pedestrians and cyclists) are the most likely to express positive impacts of commuting on energy and productivity at work, followed by car users. Meanwhile, bus, streetcar, LRT, and BRT users are the least likely to express such results. Car users have a higher likelihood of expressing punctuality to work compared to pedestrians and transit users in all three subsets. Longer commute durations are associated with negative impacts on all three work performance measures, especially punctuality.Our study underscores that commuting is not a neutral transition between home and work but an influential factor in work performance. Investment in infrastructure that supports active transport and high-quality transit that is fast and comfortable can promote positive performance at work.]]></description>
      <pubDate>Fri, 15 May 2026 15:44:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669974</guid>
    </item>
    <item>
      <title>Spatial structure and mechanism typologies of metro commuting efficiency in Chinese megacities: A collective rationality perspective</title>
      <link>https://trid.trb.org/View/2698568</link>
      <description><![CDATA[Metro systems are primarily concentrated in large urban areas worldwide, serving as the backbone of public transportation and a crucial indicator of urban modernization and transport system performance. From the perspective of collective rationality theory, overall efficiency should be maximized under constraints of limited transport resources. Building on this theoretical premise, this study constructs an evaluation framework for metro commuting efficiency by integrating accessibility-based service capacity with actual commuting behavior. Taking Xi’an, a representative megacity in China as the empirical case, we employ multi-source data, including population raster datasets and Baidu Heat Map data, alongside spatial analytical techniques such as graph-theoretical network analysis and Geographically Weighted Regression, to explore the spatial distribution and underlying patterns of metro commuting efficiency. The results reveal that: (1) Metro commuting efficiency in Chinese megacities exhibits significant spatial disparities, with high-efficiency clusters concentrated in mature urban cores, while suburban industrial parks and peripheral counties are characterized by low-efficiency agglomerations. (2) The built environment in megacities exhibits multidimensional associations with collective commuting efficiency and pronounced spatial heterogeneity. Closeness centrality of metro, bus route density, commercial service density, and resident population density show consistently positive effects, whereas land-use mix, public service density, and office employment density display spatially heterogeneous impacts with varying directions. Notably, the effects of resident population density and commercial service density under the collective efficiency framework differ markedly from conclusions derived from cost-based efficiency measures. (3) The diversity and complexity of built environments in megacities give rise to multiple mechanism typologies influencing commuting efficiency. In Xi’an, four distinct spatial mechanism types are identified: “Life-Service-Oriented Pattern,” “Mixed and Synergistic-Driven Pattern,” “Metro Underutilization–Constrained Pattern,” “Development Positioning–Constrained Pattern,” These findings deepen the understanding of metro commuting performance and its driving mechanisms in large cities, providing scientific evidence for transit-oriented spatial governance and urban mobility planning.]]></description>
      <pubDate>Fri, 15 May 2026 10:44:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698568</guid>
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