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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Developing a Data Fusion Tool for Improved Traffic Crash Exposure
Analysis and Modeling</title>
      <link>https://rip.trb.org/View/2663603</link>
      <description><![CDATA[Accurate measurement of exposure is critical for understanding and preventing traffic crashes, as crash frequency is directly related to how much road users are exposed to risk. However, current exposure estimates rely on data sources with complementary but individually insufficient characteristics. Traditional traffic counts and Annual Average Daily Traffic (AADT) offer high accuracy but limited spatial and temporal coverage, while emerging Location-Based Services (LBS) data provide high-resolution mobility patterns but are often biased and less reliable. This fundamental mismatch between accuracy and coverage prevents agencies from developing the complete and reliable exposure estimates needed for effective safety analysis and planning.
This project develops a data fusion tool that integrates traffic counts and AADT, LBS data, and socio-demographically representative survey data from the National Household Travel Survey (NHTS) into a unified measure of exposure. Unlike previous efforts that focused on a single travel mode or low temporal resolution, the proposed framework generates exposure estimates for motor vehicles, pedestrians, bicyclists, and scooters at fine spatial scales (intersection and mid-block) and temporal scales (daily and monthly). The tool is evaluated in Washington, D.C., using three alternative fusion paradigms: Bayesian fusion through hierarchical or state-space modeling, Dempster–Shafer theory for explicit uncertainty representation and accommodation of LBS coverage gaps, and model-based fusion employing structured error modeling with NHTS socio-demographics to correct LBS data bias.
The fusion methods are compared through crash prediction models estimated with fused exposure measures against models using individual data sources, evaluated via pseudo-R², AIC, BIC, and out-of-sample prediction error, with a target improvement of at least 10% in predictive performance. Fused exposure patterns are further validated against Washington, D.C.’s High Injury Network and independent ground-truth count data where available. The final tool is delivered as an open-source Python package with documentation and secure coding practices. Agency outreach, including engagement with D.C. stakeholders managing the High Injury Network, informs tool refinement and supports preparation for future pilot deployment. This research supports USDOT’s Safety priority by generating more accurate and complete multimodal exposure measures that enable better identification of high-risk locations, improved crash prediction, and targeted safety interventions
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:31:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663603</guid>
    </item>
    <item>
      <title>The Reverse Side of Online Shopping: Examining Sociodemographic and Built-Environment Determinants of Delivery Returns</title>
      <link>https://rip.trb.org/View/2553166</link>
      <description><![CDATA[The rise of online shopping has led to a significant increase in the return rate for items purchased online (or "delivery returns"). The process of returning items, once a rare occurrence in the traditional retail setting, has become a commonplace aspect of the e-commerce experience. Online purchase return rates (30%) significantly exceed those of physical stores (8.89%). Overall, these high return rates, have substantial financial, logistical, and transportation-related repercussions. From a transportation perspective, the large volume of returns necessitates additional truck trips, leading to increased freight vehicle miles traveled. This trend also results in more truck traffic at residential locations or return points. Despite the acknowledged impacts, this topic remains under-researched, with existing studies focusing on product characteristics, or retailer policies while overlooking consumer-level perspectives. This study aims to bridge this gap by examining how sociodemographic and built-environment factors influence the frequency and channel choice (physical store, mail carrier, Amazon drop-off, home pickup) for returning online purchases. Utilizing the National Household Travel Survey (NHTS) 2022 dataset, the research team analyzes responses on delivery return frequency across four channels. The team employs a multivariate probit ordered-response model to jointly analyze the full product returning behavior spectrum. This approach recognizes that behaviors are multifaceted, involving both the decision to return an item and the choice of a channel, and accounts for the interconnectedness of decision-making processes. The findings provide a foundation for developing targeted strategies to reduce return rates, streamline reverse logistics, manage travel demand, enhance customer satisfaction, and contribute to a more sustainable e-commerce future. ]]></description>
      <pubDate>Thu, 15 May 2025 14:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553166</guid>
    </item>
    <item>
      <title>An Evaluation of the Long-Term Effects of the COVID-19 Pandemic on Public Transportation Use</title>
      <link>https://rip.trb.org/View/2553165</link>
      <description><![CDATA[Public transportation provides many advantages compared to other transportation modes. It offers a cost-effective commuting opportunity, plays an important role in reducing traffic congestion and carbon emissions, and promotes equity among travelers. However, recent changes in transportation behavior, largely influenced by the COVID-19 pandemic, have resulted in a decline in transit ridership, posing challenges for the future of this mode. While there is evidence of significant rebounds in ridership from pandemic lows, transit has not fully recovered. Continued fears of safety, service cuts, new travel habits, evolving work arrangements, and the growth of online activity participation have all contributed to the slow recovery of public transportation. In this context, an in-depth and rigorous study is needed to assess the ongoing and long-term effects of the pandemic on public transportation use. To explore these changing dynamics, the research team examines the changes in individual-level use of public transportation since the onset of the pandemic, as well as the possible return to pre-pandemic behaviors. Using data from the 2022 National Household Travel Survey, the team considers the self-reported impact of the pandemic on public transportation ridership and the expected permanence of this impact. The proposed investigation will allow the team to identify individuals who have altered their public transportation use since the pandemic, and distinguish which groups of individuals may be willing to return to previous ridership levels. In addition, the research will also examine the characteristics of current users of public transportation. Taken together, the results will have important implications for ongoing and future public transportation policies, providing insights into future mobility trends and informing strategies to improve public transportation ridership. ]]></description>
      <pubDate>Thu, 15 May 2025 14:50:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553165</guid>
    </item>
    <item>
      <title>Teleworking to Play or Playing to Telework? A Latent Segmentation Approach to Exploring the Relationship Between Telework and Nonwork Travel</title>
      <link>https://rip.trb.org/View/2519199</link>
      <description><![CDATA[Technology has evolved at a tremendous pace over the past decade, permeating into our everyday existence and affecting literally every aspect of our lives. Our activity-travel choices have been no exception in this regard, as we make continuous and joint decisions about which activities we can and want to undertake (either in-person or virtually). Add to this the pandemic’s upheaval of habits and behaviors, and there emerges a critical and renewed need to understand the activity-travel choices and decisions of individuals within a new landscape of transportation, technology, and pandemic-altered lifestyles. In this study, the research team explores the causal direction/jointness issue underlying the interplay of teleworking choice and nonwork travel, within the context of the telework landscape in the aftermath of the pandemic. In particular, the team models the telework frequency, maintenance stop frequency, and leisure stop frequency decision-making process as a package choice to account for unobserved factors, as well as use a latent segmentation approach to recognize the two possible and distinct causal behavioral directions that may be at play. The methodology combines an ordinal choice model for telework adoption/intensity with weekly count models for the number of maintenance and leisure stops. The data for the analysis is drawn from a 2021-2022 weekly travel diary and survey of Minnesotan workers.]]></description>
      <pubDate>Sat, 08 Mar 2025 11:28:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519199</guid>
    </item>
    <item>
      <title>Tour Generation of Interregional Travel in the United States: Insights from the 2017 National Household Travel Survey (NHTS)</title>
      <link>https://rip.trb.org/View/2459118</link>
      <description><![CDATA[This study focuses on interregional travel, a submarket of long-distance travel (LDT) with one-way distance in the range of 50–600 miles. Interregional travel warrants focal attention for two reasons. First, despite its modest share (less than 4%) in the US domestic travel market, interregional travel contributes over 20% of total vehicle miles traveled and a commensurate amount of transportation emissions. Second, interregional travel covers a distance range with the greatest potential to achieve multimodality. Better understanding interregional travel can help inform statewide and nationwide transportation planning, for instance, the ongoing Federal Rail Administration’s Regional Rail Planning. However, existing studies on interregional travel are scarce due to data limitations. This study taps into the data from National Household Travel Surveys (NHTS) and analyzes the characteristics of interregional tours, with a tour consisting of multiple connected trips. To address the issue of excessive zero observations for interregional travel in the cross-sectional NHTS dataset, the study estimates zero-inflated models and contrasts tour generation characteristics for interregional travel with those for intraregional and the long-haul component of LDT. Elasticities of interregional tour frequencies are calculated with respect to five planning or policy variables, including age, gasoline price-adjusted income, vehicle ownership, household size, and tour complexity. The study demonstrates the potential of utilizing the existing NHTS data for interregional travel analysis. The study’s findings help inform multiregional and national transportation investment decisions and policy deliberations.]]></description>
      <pubDate>Sat, 23 Nov 2024 10:58:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459118</guid>
    </item>
    <item>
      <title>Exploring the Changing Dynamics of Household Vehicle Ownership and Use in the U.S.</title>
      <link>https://rip.trb.org/View/2440045</link>
      <description><![CDATA[This project is driven by a pressing need to understand the rapidly evolving landscape of household vehicle dynamics amidst technological advancements and significant societal changes. It focuses on the growing urgency of climate change mitigation and adaptation, a push for equitable mobility for all, and the transition towards vehicle electrification. Aiming to fill the knowledge gap in how households are adapting to these transformative forces, the project will design and deploy a comprehensive nationwide survey, called Evolving Vehicle Ownership Preferences and Use Survey (EVOPUS). This survey seeks to collect data on vehicle ownership, use, and preferences in the context of societal and environmental changes as well as related changes in household energy use (e.g. the adoption of residential solar photovoltaics and battery storage). The major contributions of the project are the following: 1) a nationwide dataset including data on travel behavior, household characteristics, vehicle ownership/transactions and use, mobility patterns as well as attitudes, perceptions, preferences, and lifestyles, made available to other researchers; 2) enhanced understanding of key barriers and drivers of electric vehicle adoption in distinct population segments; 3) a basis for new policies and programs and improvements to existing policies and programs to enable an equitable transition to sustainable mobility across heterogeneous population segments throughout the country.]]></description>
      <pubDate>Thu, 10 Oct 2024 17:16:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440045</guid>
    </item>
    <item>
      <title>A Dynamic Analysis of the Built Environment-Travel Behavior Relationship Using Three Activity-Travel Surveys in the Austin, Texas Region</title>
      <link>https://rip.trb.org/View/2440264</link>
      <description><![CDATA[The relationship between the built environment (BE) and travel behavior (TB) has long interested scholars and practitioners in transportation, urban planning and design, health, and other fields. The proposed research aims to advance knowledge of the BE-TB relationship by taking a dynamic analysis approach. The study will pool three activity-travel surveys (1998, 2007, and 2017) in Austin, TX, and analyze how variations and changes in TB revealed in the surveys relate to BE variations and changes in the timeframe corresponding to the surveys. The three activity-travel surveys do not provide longitudinal observations, strictly speaking, since the sampled travelers and households differ between surveys. The 20-year timeframe, however, enables the researchers to investigate 1) TB changes over time by groups, which can be defined by socioeconomic, demographic, and geospatial (e.g., neighborhoods) characteristics, and 2) changes in TB attributable to changes in BE. The proposed research is expected to gain new insights into the BE-TB connection and inform effective, BE-based planning and policy interventions to achieve broad goals of efficient and equitable transportation and sustainable cities and regions.]]></description>
      <pubDate>Thu, 10 Oct 2024 16:34:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440264</guid>
    </item>
    <item>
      <title>Investigating Travel Survey Representativeness: Who’s Missing and What Can We Do?</title>
      <link>https://rip.trb.org/View/2440263</link>
      <description><![CDATA[The core source of data for transportation planning and forecasting comes from household travel surveys. Travel surveys are used to obtain insight into the behavioral decisions of travelers; for example: (1) trip purposes such as work or shopping; (2) means/mode of transport such as car, walk, bus, etc.; (3) travel time; and (4) time of day/week. However, these surveys tend to underrepresent the views and needs of people of color and low-income travelers, precisely the groups that depend most on historically underfunded travel modes like public transit, biking, and walking. In addition to this underrepresentation, it is increasingly difficult to obtain high quality data from those who do respond (e.g., response biases, measurement errors for underrepresented groups), as well as to obtain the detailed contextual and psychological attribute information needed for accurate behavioral forecasting. The goal of this project is to investigate household travel survey biases to identify the causes and propose potential solutions.]]></description>
      <pubDate>Thu, 10 Oct 2024 16:16:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440263</guid>
    </item>
    <item>
      <title>Updating and Expanding the Nation’s Most Comprehensive Database of Household Travel Survey Data and Related Built Environmental Data</title>
      <link>https://rip.trb.org/View/2422599</link>
      <description><![CDATA[For many years, the PIs of this study have been gathering household travel survey data from metropolitan planning organizations across the US. In addition to household sociodemographic data and trip purpose, length, mode and other variables, this unique database includes the precise XY coordinates of all households and trip ends. The current database consists of 36 regions, 107,949 households, and 1,059,678 trips, making it nearly as large as the National Household Travel Survey (NHTS) of 2022 but more geographically detailed. The unique feature of this database is the precise geocodes it offers, unlike the NHTS, which only provides geocodes at the level of the census tract. These data have been provided by MPOs to the PIs in many cases through non-disclosure agreements. Thus, it has been possible to relate travel characteristics directly to the built environment at various geographic scales, including census block groups, traffic analysis zones, mixed-use developments, and buffers of different widths around households or trip ends. Another unique feature of this database is that travel data have been linked to built environmental data representing all the so-called ‘D’ variables: development density, land use diversity, street network design, destination accessibility, and distance to transit. These data have been used to study relationships between the built environment and travel in many peer-reviewed, published studies. Spanning nearly two decades of data collection, we have frequently had to update travel data, replacing older surveys with newer ones. Portland, for example, conducted a household travel survey in 1994, and another in 2011. The latter replaced the former in our master cross sectional database, and the two together were employed in one longitudinal study. The current database includes travel data as old as 2005. Travel patterns have doubtless changed since then, with new modes (ride hailing, for example), new travel behavior (extensive telecommuting), and new structural relationships to the built environment. In this project, we will first contact the MPOs of the 36 regions in our current database to determine if they have conducted household travel surveys more recently than the ones we already have. If so, we will acquire the more recent data (provided they are once again made available) and replace older datasets for these regions. We will link the newer datasets to shapefiles for built environmental variables as before. We will conduct both cross sectional and longitudinal studies to see how travel patterns and relationships between travel and the built environment have changed, updating earlier published studies. Time permitting, we will also contact additional MPOs, to see if they are now willing to release XY geocoded data when they weren’t previously. Outputs will include 1. Updated and expanded travel survey database with linked built environment shapefiles 2. Updates to previous studies based on the dataset 3. Contributions to future cross-sectional and longitudinal studies 4. Various travel demand models 5. Final research report.]]></description>
      <pubDate>Tue, 27 Aug 2024 18:12:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422599</guid>
    </item>
    <item>
      <title>Investigating unmet travel needs in disadvantaged and rural communities: Can sustainable transportation meet these needs?
</title>
      <link>https://rip.trb.org/View/2422814</link>
      <description><![CDATA[Disadvantaged, low-income, rural, and tribal communities, often collectively referred to as
underserved or priority populations, have less access to transportation infrastructure, face more barriers in getting around, and are underrepresented in the transition to sustainable and electric transportation. Using results from a California wide questionnaire survey that overrepresents priority populations, the study team will investigate unmet and suppressed travel needs, whether sustainable transportation can meet those needs, and what policy, practice, and infrastructure interventions can support sustainable transportation in these communities. The research will give insight into communities that are typically not included in large numbers in travel surveys, which means their transportation needs, unmet and suppressed travel needs, and any barriers they face in adopting sustainable transportation are as not well understood in quantitative research. The results will inform policy, practice, and infrastructure programs that aim to create a more equitable and just transportation system by providing insight on the nature of unmet and suppressed travel, including types
of destinations (e.g., work, education, health) populations cannot access or have difficulties in accessing, their preferred mode to reach that location, and whether sustainable transportation can meet these unmet needs (in addition to meeting currently met needs).]]></description>
      <pubDate>Tue, 27 Aug 2024 17:47:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422814</guid>
    </item>
    <item>
      <title>Travel Demand Survey to Inform Infrastructure Investments (4.15)</title>
      <link>https://rip.trb.org/View/2378078</link>
      <description><![CDATA[Maine is challenged to fund and implement transportation solutions that address climate
change, connect people with employment opportunities, and provide equitable, affordable access to services for all communities. Maine’s investments in transportation infrastructure and strategies must deal with the realities of a few urban clusters with transit and active transportation options, but where large portions of the the state are quite rural with limited choices for travel other than personal vehicles. To date, most transportation research has been oriented toward higher density urban areas, dealing primarily with reducing congestion and increasing mobility through a combination of surface road and multi-modal improvements, transit improvements, and changes in road design for vulnerable road users. For states such as Maine, new strategies need to consider rural travel distances, challenging economics for public transit, changing vehicle connectivity technology and the expanded use of remote technologies for work,
education, healthcare and social activities. This project will provide a better understanding of the elasticity of travel demand and the factors that influence current and future transportation patterns, such as fuel prices, micro transit and mobility as a service, new vehicle technologies and broadband connectivity. The research team is proposing new primary data collection through the use of a travel survey that will expand upon the US Census National Household Travel Survey. The work will develop a detailed profile of current travel choices and patterns in each of Maine 16 counties. The survey will focus on understanding of current travel needs and the perceived priorities for infrastructure needed to deliver the services communities need to thrive.]]></description>
      <pubDate>Thu, 09 May 2024 15:11:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2378078</guid>
    </item>
    <item>
      <title>Synthesis Study of Costs and Trip Rates of Recent Household Travel Surveys 
</title>
      <link>https://rip.trb.org/View/2233697</link>
      <description><![CDATA[Travel surveys are a staple of transportation planning.  They are conducted regularly by regional planning agencies and states, sometimes in conjunction with larger national surveys.  Their primary uses are in describing local travel patterns, updating travel models, evaluating major project proposals and monitoring travel trends.   Since the last time the Hartgen Group conducted a survey, many improvements in travel survey methods have been implemented, most notably the use of smartphones which measure trips across multiple days.  Additionally, lower response rates have been so prevalent that some agencies are now using convenience sampling to ensure adequate representation from all market segments. Both of these have added cost and the effects on trip rates and representation are somewhat confounded due to the different survey modes.

Ohio Department of Transportation (ODOT) is currently conducting Household Travel Surveys (HTS) and long distance (LD) surveys over a 10-year period.  Over the five years the survey has been fielded, the percentage of smartphone ownership and participation has increased.  However, the response rates from the address-based random sample have decreased to the point that the issue of moving to a convenience sample has arisen. To date, ODOT has not allowed convenience samples. However, if findings from an updated synthesis demonstrate that there is little difference in survey responses, then ODOT may allow convenience samples in order to obtain a more representative population. To assist ODOT in ensuring the utilization of the most effective and appropriate HTS and LD survey methods, research is needed.

The goal of this research is to enhance the fielding of ODOT's HTS and LD surveys and their sample design. 
The objectives of this research include the following: (1) update the 2009 synthesis report, Costs and Trip Rates of Recent Household Travel Surveys, conducted by the Hartgen Group; and (2) develop a Power-BI database and a supplemental MS Excel spreadsheet of all data utilized in the analysis including data from the 2009 synthesis report, missing data obtained to address any gaps in the 2009 report, and new data collected since the 2009 report. ]]></description>
      <pubDate>Fri, 25 Aug 2023 13:51:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2233697</guid>
    </item>
    <item>
      <title>Response Willingness in Consecutive Travel Surveys</title>
      <link>https://rip.trb.org/View/1983974</link>
      <description><![CDATA[Declining survey response rates have increased the costs of travel survey recruitment. Recruiting respondents based on their expressed willingness to participate in future surveys, obtained from a preceding survey, is a potential solution but may exacerbate sample biases. In this study, the research team analyzes the self-selection biases of survey respondents recruited from the 2017 U.S. National Household Travel Survey (NHTS), who had agreed to be contacted again for follow-up surveys. The team applies a probit with sample selection (PSS) model to analyze (1) respondents’ willingness to participate in a follow-up survey (the selection model) and (2) their actual response behavior once contacted (the outcome model). Results verify the existence of self-selection biases, which are related to survey burden, sociodemographic characteristics, travel behavior, and item non-response to sensitive variables. The team finds that age, homeownership, and medical conditions have opposing effects on respondents’ willingness to participate and their actual survey participation. The PSS model is then validated using a hold-out sample and applied to the NHTS samples from various geographic regions to predict follow-up survey participation. Effect size indicators for differences between predicted and actual (population) distributions of select sociodemographic and travel-related variables suggest that the resulting samples may be most biased along age and education dimensions. Further, the team summarized six model performance measures based on the PSS model structure. Overall, this study provides insight into self-selection biases in respondents recruited from preceding travel surveys. Model results can help researchers better understand and address such biases, while the nuanced application of various model measures lays a foundation for appropriate comparison across sample selection models.]]></description>
      <pubDate>Tue, 21 Jun 2022 09:18:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1983974</guid>
    </item>
    <item>
      <title>Montana MPO Travel Survey Analysis</title>
      <link>https://rip.trb.org/View/1890062</link>
      <description><![CDATA[The purpose of this study is to provide the Small Urban, Rural and Tribal Center on Mobility with additional information and greater understanding of transportation planning and travel behavior in the areas served by the Billings-Yellowstone County Metropolitan Planning Organization and the Missoula Area Metropolitan Planning Organization.
This project will benefit transportation policy and planning professionals, decisionmakers, and community members in the two study areas. It may also serve as a helpful reference for other small urban areas.]]></description>
      <pubDate>Thu, 04 Nov 2021 10:27:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1890062</guid>
    </item>
    <item>
      <title>A Better Understanding of Shopping Travel in the U.S. (Project K5)</title>
      <link>https://rip.trb.org/View/1868828</link>
      <description><![CDATA[Online shopping has been growing in popularity for several decades, and the pandemic has accelerated its adoption. The environmental and congestion effects of a shift to delivery services are unclear, and depend on a number of parameters about the delivery services. However, understanding the relative impact of a shift to online shopping also requires understanding the environment and congestion implications of current shopping behavior. The amount of travel associated with shopping is often calculated by adding up the total mileage of trips to/from shopping destinations, or by computing the round trip distance from home to store. Both methods may significantly overestimate shopping travel since much shopping travel occurs on the way to another destination. In the limit, stopping at a store that is directly on the way to another destination may add no additional travel, but traditional travel survey methods would attribute at least a portion of the travel to the shopping trip. Using geocoded travel survey information from the Transportation Secure Data Center, this project will estimate the marginal travel associated with shopping trips. These surveys include precise geographic locations for all stops made by respondents. We will use a network analysis algorithm to compute travel distances for “counterfactual” tours where the shopping destination was skipped. From this, we will calculate the marginal travel associated with shopping—which is likely to be much lower than current estimates of shopping travel, making delivery services relatively less attractive. We will disaggregate these results by time of day and location on the network to better understand congestion effects of shopping travel.
]]></description>
      <pubDate>Tue, 27 Jul 2021 09:30:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868828</guid>
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